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'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[int] , snake_case__ : int , snake_case__ : Tuple=13 , snake_case__ : Tuple=30 , snake_case__ : Optional[Any]=2 , snake_case__ : Optional[Any]=3 , snake_case__ : Optional[int]=True , snake_case__ : Union[str, Any]=True , snake_case__ : Union[str, Any]=32 , snake_case__ : List[Any]=2 , snake_case__ : Optional[Any]=4 , snake_case__ : Dict=37 , snake_case__ : List[str]="gelu" , snake_case__ : Dict=0.1 , snake_case__ : Tuple=0.1 , snake_case__ : Optional[Any]=10 , snake_case__ : List[Any]=0.02 , snake_case__ : Tuple=3 , snake_case__ : Optional[Any]=None , ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : Dict = image_size
UpperCAmelCase__ : Dict = patch_size
UpperCAmelCase__ : Dict = num_channels
UpperCAmelCase__ : Dict = is_training
UpperCAmelCase__ : List[Any] = use_labels
UpperCAmelCase__ : Optional[Any] = hidden_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : Any = num_attention_heads
UpperCAmelCase__ : Union[str, Any] = intermediate_size
UpperCAmelCase__ : Optional[int] = hidden_act
UpperCAmelCase__ : Optional[Any] = hidden_dropout_prob
UpperCAmelCase__ : Optional[Any] = attention_probs_dropout_prob
UpperCAmelCase__ : Tuple = type_sequence_label_size
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : Optional[Any] = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase__ : Optional[int] = (image_size // patch_size) ** 2
UpperCAmelCase__ : List[str] = num_patches + 1
def UpperCamelCase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : str = None
if self.use_labels:
UpperCAmelCase__ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : str = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self : List[str] ):
'''simple docstring'''
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self : int , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : str ):
'''simple docstring'''
UpperCAmelCase__ : str = TFViTModel(config=snake_case__ )
UpperCAmelCase__ : str = model(snake_case__ , training=snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
UpperCAmelCase__ : Union[str, Any] = self.image_size // 2
UpperCAmelCase__ : List[Any] = pixel_values[:, :, :image_size, :image_size]
UpperCAmelCase__ : Optional[Any] = model(snake_case__ , interpolate_pos_encoding=snake_case__ , training=snake_case__ )
UpperCAmelCase__ : List[Any] = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self : Tuple , snake_case__ : Union[str, Any] , snake_case__ : Optional[int] , snake_case__ : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.type_sequence_label_size
UpperCAmelCase__ : List[Any] = TFViTForImageClassification(snake_case__ )
UpperCAmelCase__ : List[str] = model(snake_case__ , labels=snake_case__ , training=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
UpperCAmelCase__ : Dict = self.image_size // 2
UpperCAmelCase__ : List[Any] = pixel_values[:, :, :image_size, :image_size]
UpperCAmelCase__ : Tuple = model(snake_case__ , interpolate_pos_encoding=snake_case__ , training=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase__ : Tuple = 1
UpperCAmelCase__ : Optional[int] = TFViTForImageClassification(snake_case__ )
UpperCAmelCase__ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : List[Any] = model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = config_and_inputs
UpperCAmelCase__ : Tuple = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ ( A , A , unittest.TestCase ):
'''simple docstring'''
lowercase_ : Optional[Any] = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
lowercase_ : Tuple = (
{"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
if is_tf_available()
else {}
)
lowercase_ : List[Any] = False
lowercase_ : Optional[int] = False
lowercase_ : Any = False
def UpperCamelCase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = TFViTModelTester(self )
UpperCAmelCase__ : Any = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def UpperCamelCase ( self : Optional[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def UpperCamelCase ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="ViT does not use inputs_embeds" )
def UpperCamelCase ( self : List[Any] ):
'''simple docstring'''
pass
def UpperCamelCase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
UpperCAmelCase__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Any = model_class(snake_case__ )
UpperCAmelCase__ : List[Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : List[Any] = [*signature.parameters.keys()]
UpperCAmelCase__ : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case__ )
def UpperCamelCase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCamelCase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
@slow
def UpperCamelCase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : int = TFViTModel.from_pretrained("google/vit-base-patch16-224" )
self.assertIsNotNone(snake_case__ )
def snake_case_ ( ):
UpperCAmelCase__ : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase ( self : Optional[Any] ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224" ) if is_vision_available() else None
@slow
def UpperCamelCase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Dict = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224" )
UpperCAmelCase__ : Optional[int] = self.default_image_processor
UpperCAmelCase__ : Tuple = prepare_img()
UpperCAmelCase__ : Union[str, Any] = image_processor(images=snake_case__ , return_tensors="tf" )
# forward pass
UpperCAmelCase__ : List[Any] = model(**snake_case__ )
# verify the logits
UpperCAmelCase__ : Any = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , snake_case__ )
UpperCAmelCase__ : List[Any] = tf.constant([-0.2744, 0.8215, -0.0836] )
tf.debugging.assert_near(outputs.logits[0, :3] , snake_case__ , atol=1e-4 )
| 199 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = ["""GPTSw3Tokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 199 | 1 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a ( __a ) -> str:
'''simple docstring'''
UpperCamelCase__ :List[Any] = filter(lambda __a : p.requires_grad , model.parameters() )
UpperCamelCase__ :Tuple = sum([np.prod(p.size() ) for p in model_parameters] )
return params
__snake_case = logging.getLogger(__name__)
def a ( __a , __a ) -> str:
'''simple docstring'''
if metric == "rouge2":
UpperCamelCase__ :List[str] = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
UpperCamelCase__ :Tuple = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
UpperCamelCase__ :Tuple = '''{val_avg_em:.4f}-{step_count}'''
else:
raise NotImplementedError(
f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
''' function.''' )
UpperCamelCase__ :Any = ModelCheckpoint(
dirpath=__a , filename=__a , monitor=f'''val_{metric}''' , mode='''max''' , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def a ( __a , __a ) -> int:
'''simple docstring'''
return EarlyStopping(
monitor=f'''val_{metric}''' , mode='''min''' if '''loss''' in metric else '''max''' , patience=__a , verbose=__a , )
class lowercase ( pl.Callback ):
"""simple docstring"""
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :str = {F'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(UpperCamelCase_ )
@rank_zero_only
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=True ):
'''simple docstring'''
logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
UpperCamelCase__ :List[Any] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
UpperCamelCase__ :Optional[Any] = Path(pl_module.hparams.output_dir )
if type_path == "test":
UpperCamelCase__ :Optional[int] = od / '''test_results.txt'''
UpperCamelCase__ :int = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
UpperCamelCase__ :Tuple = od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
UpperCamelCase__ :List[str] = od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=UpperCamelCase_ )
generations_file.parent.mkdir(exist_ok=UpperCamelCase_ )
with open(UpperCamelCase_ , '''a+''' ) as writer:
for key in sorted(UpperCamelCase_ ):
if key in ["log", "progress_bar", "preds"]:
continue
UpperCamelCase__ :int = metrics[key]
if isinstance(UpperCamelCase_ , torch.Tensor ):
UpperCamelCase__ :int = val.item()
UpperCamelCase__ :Any = F'''{key}: {val:.6f}\n'''
writer.write(UpperCamelCase_ )
if not save_generations:
return
if "preds" in metrics:
UpperCamelCase__ :List[Any] = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(UpperCamelCase_ )
@rank_zero_only
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
try:
UpperCamelCase__ :Any = pl_module.model.model.num_parameters()
except AttributeError:
UpperCamelCase__ :Optional[Any] = pl_module.model.num_parameters()
UpperCamelCase__ :Optional[int] = count_trainable_parameters(UpperCamelCase_ )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6} )
@rank_zero_only
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(UpperCamelCase_ , UpperCamelCase_ , '''test''' )
@rank_zero_only
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid") | 280 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def a ( __a = "" ) -> dict[str, float]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
UpperCamelCase__ :Optional[int] = BeautifulSoup(requests.get(__a ).text , '''html.parser''' )
UpperCamelCase__ :Tuple = soup.find_all('''td''' , attrs='''titleColumn''' )
UpperCamelCase__ :Union[str, Any] = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(__a , __a )
}
def a ( __a = "IMDb_Top_250_Movies.csv" ) -> None:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = get_imdb_top_aaa_movies()
with open(__a , '''w''' , newline='''''' ) as out_file:
UpperCamelCase__ :int = csv.writer(__a )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies() | 280 | 1 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=True , _UpperCamelCase="pt" ):
"""simple docstring"""
lowercase_ : Union[str, Any] = {"add_prefix_space": True} if isinstance(_UpperCamelCase , _UpperCamelCase ) and not line.startswith(" " ) else {}
lowercase_ : Optional[int] = padding_side
return tokenizer(
[line] , max_length=_UpperCamelCase , padding="max_length" if pad_to_max_length else None , truncation=_UpperCamelCase , return_tensors=_UpperCamelCase , add_special_tokens=_UpperCamelCase , **_UpperCamelCase , )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None , ):
"""simple docstring"""
lowercase_ : Optional[Any] = input_ids.ne(_UpperCamelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class _UpperCAmelCase ( snake_case ):
def __init__( self : Any , a : Optional[Any] , a : Any , a : Optional[int] , a : Optional[Any] , a : Dict="train" , a : List[Any]=None , a : Tuple=None , a : Optional[int]=None , a : Optional[int]="" , ):
'''simple docstring'''
super().__init__()
lowercase_ : str = Path(a ).joinpath(type_path + ".source" )
lowercase_ : Union[str, Any] = Path(a ).joinpath(type_path + ".target" )
lowercase_ : Optional[int] = self.get_char_lens(self.src_file )
lowercase_ : Union[str, Any] = max_source_length
lowercase_ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f"""found empty line in {self.src_file}"""
lowercase_ : List[Any] = tokenizer
lowercase_ : List[str] = prefix
if n_obs is not None:
lowercase_ : List[str] = self.src_lens[:n_obs]
lowercase_ : List[Any] = src_lang
lowercase_ : Tuple = tgt_lang
def __len__( self : Dict ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : str , a : Union[str, Any] ):
'''simple docstring'''
lowercase_ : Optional[Any] = index + 1 # linecache starts at 1
lowercase_ : Tuple = self.prefix + linecache.getline(str(self.src_file ) , a ).rstrip("\n" )
lowercase_ : Optional[int] = linecache.getline(str(self.tgt_file ) , a ).rstrip("\n" )
assert source_line, f"""empty source line for index {index}"""
assert tgt_line, f"""empty tgt line for index {index}"""
# Need to add eos token manually for T5
if isinstance(self.tokenizer , a ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
lowercase_ : str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , a ) else self.tokenizer
)
lowercase_ : str = self.tokenizer.generator if isinstance(self.tokenizer , a ) else self.tokenizer
lowercase_ : Union[str, Any] = encode_line(a , a , self.max_source_length , "right" )
lowercase_ : Tuple = encode_line(a , a , self.max_target_length , "right" )
lowercase_ : int = source_inputs["input_ids"].squeeze()
lowercase_ : Optional[int] = target_inputs["input_ids"].squeeze()
lowercase_ : Optional[int] = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def lowerCAmelCase__ ( a : Dict ):
'''simple docstring'''
return [len(a ) for x in Path(a ).open().readlines()]
def lowerCAmelCase__ ( self : str , a : Any ):
'''simple docstring'''
lowercase_ : Dict = torch.stack([x["input_ids"] for x in batch] )
lowercase_ : Any = torch.stack([x["attention_mask"] for x in batch] )
lowercase_ : Optional[int] = torch.stack([x["decoder_input_ids"] for x in batch] )
lowercase_ : Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , a )
else self.tokenizer.pad_token_id
)
lowercase_ : Any = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , a )
else self.tokenizer.pad_token_id
)
lowercase_ : Optional[int] = trim_batch(a , a )
lowercase_ , lowercase_ : Any = trim_batch(a , a , attention_mask=a )
lowercase_ : str = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
UpperCamelCase__ = getLogger(__name__)
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
return list(itertools.chain.from_iterable(_UpperCamelCase ) )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
lowercase_ : List[str] = get_git_info()
save_json(_UpperCamelCase , os.path.join(_UpperCamelCase , "git_log.json" ) )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=4 , **_UpperCamelCase ):
"""simple docstring"""
with open(_UpperCamelCase , "w" ) as f:
json.dump(_UpperCamelCase , _UpperCamelCase , indent=_UpperCamelCase , **_UpperCamelCase )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
with open(_UpperCamelCase ) as f:
return json.load(_UpperCamelCase )
def __SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
lowercase_ : Tuple = git.Repo(search_parent_directories=_UpperCamelCase )
lowercase_ : Optional[Any] = {
"repo_id": str(_UpperCamelCase ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
return list(map(_UpperCamelCase , _UpperCamelCase ) )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
with open(_UpperCamelCase , "wb" ) as f:
return pickle.dump(_UpperCamelCase , _UpperCamelCase )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
def remove_articles(_UpperCamelCase ):
return re.sub(R"\b(a|an|the)\b" , " " , _UpperCamelCase )
def white_space_fix(_UpperCamelCase ):
return " ".join(text.split() )
def remove_punc(_UpperCamelCase ):
lowercase_ : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_UpperCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase ) ) ) )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Any = normalize_answer(_UpperCamelCase ).split()
lowercase_ : Tuple = normalize_answer(_UpperCamelCase ).split()
lowercase_ : List[str] = Counter(_UpperCamelCase ) & Counter(_UpperCamelCase )
lowercase_ : Optional[int] = sum(common.values() )
if num_same == 0:
return 0
lowercase_ : int = 1.0 * num_same / len(_UpperCamelCase )
lowercase_ : Union[str, Any] = 1.0 * num_same / len(_UpperCamelCase )
lowercase_ : Optional[Any] = (2 * precision * recall) / (precision + recall)
return fa
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
return normalize_answer(_UpperCamelCase ) == normalize_answer(_UpperCamelCase )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
lowercase_ : Dict = 0
for hypo, pred in zip(_UpperCamelCase , _UpperCamelCase ):
em += exact_match_score(_UpperCamelCase , _UpperCamelCase )
if len(_UpperCamelCase ) > 0:
em /= len(_UpperCamelCase )
return {"em": em}
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
return model_prefix.startswith("rag" )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : str = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
lowercase_ : List[Any] = "dropout_rate"
for p in extra_params:
if getattr(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if not hasattr(_UpperCamelCase , _UpperCamelCase ) and not hasattr(_UpperCamelCase , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(_UpperCamelCase ) )
delattr(_UpperCamelCase , _UpperCamelCase )
continue
lowercase_ : Any = p if hasattr(_UpperCamelCase , _UpperCamelCase ) else equivalent_param[p]
setattr(_UpperCamelCase , _UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
delattr(_UpperCamelCase , _UpperCamelCase )
return hparams, config
| 620 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Optional[Any] = WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase , config=_UpperCamelCase )
lowercase_ : Optional[int] = downstream_dict["projector.weight"]
lowercase_ : str = downstream_dict["projector.bias"]
lowercase_ : int = downstream_dict["model.post_net.linear.weight"]
lowercase_ : Optional[Any] = downstream_dict["model.post_net.linear.bias"]
return model
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Tuple = WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase , config=_UpperCamelCase )
lowercase_ : Any = downstream_dict["model.linear.weight"]
lowercase_ : List[str] = downstream_dict["model.linear.bias"]
return model
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Any = WavaVecaForXVector.from_pretrained(_UpperCamelCase , config=_UpperCamelCase )
lowercase_ : str = downstream_dict["connector.weight"]
lowercase_ : List[str] = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowercase_ : Union[str, Any] = downstream_dict[
F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
]
lowercase_ : Dict = downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
lowercase_ : Dict = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
lowercase_ : List[Any] = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
lowercase_ : Dict = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
lowercase_ : Dict = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
lowercase_ : Optional[Any] = downstream_dict["objective.W"]
return model
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Tuple = torch.load(_UpperCamelCase , map_location="cpu" )
lowercase_ : Dict = checkpoint["Downstream"]
lowercase_ : Optional[Any] = WavaVecaConfig.from_pretrained(_UpperCamelCase )
lowercase_ : Dict = WavaVecaFeatureExtractor.from_pretrained(
_UpperCamelCase , return_attention_mask=_UpperCamelCase , do_normalize=_UpperCamelCase )
lowercase_ : Dict = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
lowercase_ : Any = convert_classification(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
elif arch.endswith("ForAudioFrameClassification" ):
lowercase_ : Optional[int] = convert_diarization(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
elif arch.endswith("ForXVector" ):
lowercase_ : List[Any] = convert_xvector(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
else:
raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}""" )
if hf_config.use_weighted_layer_sum:
lowercase_ : List[str] = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(_UpperCamelCase )
hf_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
'--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
)
parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
UpperCamelCase__ = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 620 | 1 |
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=_UpperCAmelCase ):
"""simple docstring"""
__a : Optional[Any] = ['''onnx''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''onnx'''] )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['''onnx'''] )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''onnx'''] ) | 522 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a : Any = logging.get_logger(__name__)
__a : Dict = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
__a : Optional[Any] = '''yolos'''
def __init__( self , lowerCAmelCase__=7_68 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=30_72 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-12 , lowerCAmelCase__=[5_12, 8_64] , lowerCAmelCase__=16 , lowerCAmelCase__=3 , lowerCAmelCase__=True , lowerCAmelCase__=1_00 , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__=1 , lowerCAmelCase__=5 , lowerCAmelCase__=2 , lowerCAmelCase__=5 , lowerCAmelCase__=2 , lowerCAmelCase__=0.1 , **lowerCAmelCase__ , ) -> str:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
__lowercase = num_detection_tokens
__lowercase = use_mid_position_embeddings
__lowercase = auxiliary_loss
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
__a : int = version.parse('''1.11''' )
@property
def _SCREAMING_SNAKE_CASE ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _SCREAMING_SNAKE_CASE ( self ) -> float:
'''simple docstring'''
return 1E-4
@property
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return 12 | 522 | 1 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 658 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=24 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , ) -> Optional[Any]:
UpperCamelCase :int = parent
UpperCamelCase :List[Any] = batch_size
UpperCamelCase :List[Any] = patch_size
UpperCamelCase :Optional[int] = max_length
UpperCamelCase :Union[str, Any] = num_mel_bins
UpperCamelCase :Optional[int] = is_training
UpperCamelCase :Dict = use_labels
UpperCamelCase :Dict = hidden_size
UpperCamelCase :Optional[int] = num_hidden_layers
UpperCamelCase :str = num_attention_heads
UpperCamelCase :Optional[int] = intermediate_size
UpperCamelCase :List[str] = hidden_act
UpperCamelCase :List[str] = hidden_dropout_prob
UpperCamelCase :List[Any] = attention_probs_dropout_prob
UpperCamelCase :str = type_sequence_label_size
UpperCamelCase :List[Any] = initializer_range
UpperCamelCase :Union[str, Any] = scope
UpperCamelCase :List[Any] = frequency_stride
UpperCamelCase :Tuple = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
UpperCamelCase :List[Any] = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
UpperCamelCase :List[str] = (self.max_length - self.patch_size) // self.time_stride + 1
UpperCamelCase :Tuple = frequency_out_dimension * time_out_dimension
UpperCamelCase :Optional[int] = num_patches + 2
def UpperCAmelCase ( self ) -> Any:
UpperCamelCase :Tuple = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
UpperCamelCase :Tuple = None
if self.use_labels:
UpperCamelCase :List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase :str = self.get_config()
return config, input_values, labels
def UpperCAmelCase ( self ) -> List[Any]:
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase :Optional[Any] = ASTModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase :Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :List[Any] = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) :Union[str, Any] = config_and_inputs
UpperCamelCase :List[Any] = {'''input_values''': input_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( lowercase, lowercase, unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] =(
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
UpperCamelCase_ : Any =(
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
UpperCamelCase_ : Optional[int] =False
UpperCamelCase_ : List[Any] =False
UpperCamelCase_ : Optional[Any] =False
UpperCamelCase_ : Dict =False
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :List[Any] = ASTModelTester(self )
UpperCamelCase :Dict = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCAmelCase ( self ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''AST does not use inputs_embeds''' )
def UpperCAmelCase ( self ) -> str:
pass
def UpperCAmelCase ( self ) -> int:
UpperCamelCase , UpperCamelCase :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase :Dict = model_class(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase :Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_ , nn.Linear ) )
def UpperCAmelCase ( self ) -> Tuple:
UpperCamelCase , UpperCamelCase :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase :Dict = model_class(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase :Any = [*signature.parameters.keys()]
UpperCamelCase :Optional[int] = ['''input_values''']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> List[Any]:
UpperCamelCase :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCAmelCase ( self ) -> Optional[int]:
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase :Union[str, Any] = ASTModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def _A ( ):
UpperCamelCase :Any = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' )
UpperCamelCase , UpperCamelCase :Any = torchaudio.load(SCREAMING_SNAKE_CASE__ )
return audio, sampling_rate
@require_torch
@require_torchaudio
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase ( self ) -> Tuple:
return (
ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' )
if is_torchaudio_available()
else None
)
@slow
def UpperCAmelCase ( self ) -> str:
UpperCamelCase :Union[str, Any] = self.default_feature_extractor
UpperCamelCase :Union[str, Any] = ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = self.default_feature_extractor
UpperCamelCase , UpperCamelCase :Dict = prepare_audio()
UpperCamelCase :Dict = audio.squeeze().numpy()
UpperCamelCase :int = feature_extractor(SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE_ )
# forward pass
with torch.no_grad():
UpperCamelCase :Union[str, Any] = model(**SCREAMING_SNAKE_CASE_ )
# verify the logits
UpperCamelCase :List[Any] = torch.Size((1, 527) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase :int = torch.tensor([-0.8760, -7.0042, -8.6602] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 658 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class a__ ( _snake_case ):
"""simple docstring"""
A__ : Dict = (UniPCMultistepScheduler,)
A__ : List[Any] = (('''num_inference_steps''', 25),)
def __UpperCAmelCase ( self :str , **lowercase__ :str ):
lowercase = {
'num_train_timesteps': 1000,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'solver_type': 'bh2',
}
config.update(**lowercase__ )
return config
def __UpperCAmelCase ( self :int , lowercase__ :Union[str, Any]=0 , **lowercase__ :Dict ):
lowercase = dict(self.forward_default_kwargs )
lowercase = kwargs.pop('num_inference_steps' , lowercase__ )
lowercase = self.dummy_sample
lowercase = 0.1 * sample
lowercase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowercase = self.get_scheduler_config(**lowercase__ )
lowercase = scheduler_class(**lowercase__ )
scheduler.set_timesteps(lowercase__ )
# copy over dummy past residuals
lowercase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase__ )
lowercase = scheduler_class.from_pretrained(lowercase__ )
new_scheduler.set_timesteps(lowercase__ )
# copy over dummy past residuals
lowercase = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase , lowercase = sample, sample
for t in range(lowercase__ , time_step + scheduler.config.solver_order + 1 ):
lowercase = scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
lowercase = new_scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def __UpperCAmelCase ( self :List[Any] , lowercase__ :Any=0 , **lowercase__ :Optional[Any] ):
lowercase = dict(self.forward_default_kwargs )
lowercase = kwargs.pop('num_inference_steps' , lowercase__ )
lowercase = self.dummy_sample
lowercase = 0.1 * sample
lowercase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**lowercase__ )
scheduler.set_timesteps(lowercase__ )
# copy over dummy past residuals (must be after setting timesteps)
lowercase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase__ )
lowercase = scheduler_class.from_pretrained(lowercase__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase__ )
# copy over dummy past residual (must be after setting timesteps)
lowercase = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase = scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
lowercase = new_scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def __UpperCAmelCase ( self :Tuple , lowercase__ :Any=None , **lowercase__ :Dict ):
if scheduler is None:
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config(**lowercase__ )
lowercase = scheduler_class(**lowercase__ )
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config(**lowercase__ )
lowercase = scheduler_class(**lowercase__ )
lowercase = 10
lowercase = self.dummy_model()
lowercase = self.dummy_sample_deter
scheduler.set_timesteps(lowercase__ )
for i, t in enumerate(scheduler.timesteps ):
lowercase = model(lowercase__ , lowercase__ )
lowercase = scheduler.step(lowercase__ , lowercase__ , lowercase__ ).prev_sample
return sample
def __UpperCAmelCase ( self :List[str] ):
lowercase = dict(self.forward_default_kwargs )
lowercase = kwargs.pop('num_inference_steps' , lowercase__ )
for scheduler_class in self.scheduler_classes:
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**lowercase__ )
lowercase = self.dummy_sample
lowercase = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase__ , 'set_timesteps' ):
scheduler.set_timesteps(lowercase__ )
elif num_inference_steps is not None and not hasattr(lowercase__ , 'set_timesteps' ):
lowercase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowercase = [residual + 0.2, residual + 0.15, residual + 0.10]
lowercase = dummy_past_residuals[: scheduler.config.solver_order]
lowercase = scheduler.timesteps[5]
lowercase = scheduler.timesteps[6]
lowercase = scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
lowercase = scheduler.step(lowercase__ , lowercase__ , lowercase__ , **lowercase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def __UpperCAmelCase ( self :int ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
lowercase = UniPCMultistepScheduler(**self.get_scheduler_config() )
lowercase = self.full_loop(scheduler=lowercase__ )
lowercase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_mean.item() - 0.2464 ) < 1E-3
lowercase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowercase = DEISMultistepScheduler.from_config(scheduler.config )
lowercase = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowercase = UniPCMultistepScheduler.from_config(scheduler.config )
lowercase = self.full_loop(scheduler=lowercase__ )
lowercase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_mean.item() - 0.2464 ) < 1E-3
def __UpperCAmelCase ( self :Union[str, Any] ):
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=lowercase__ )
def __UpperCAmelCase ( self :Optional[int] ):
self.check_over_configs(thresholding=lowercase__ )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowercase__ , prediction_type=lowercase__ , sample_max_value=lowercase__ , solver_order=lowercase__ , solver_type=lowercase__ , )
def __UpperCAmelCase ( self :int ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase__ )
def __UpperCAmelCase ( self :Optional[Any] ):
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowercase__ , solver_type=lowercase__ , prediction_type=lowercase__ , )
lowercase = self.full_loop(
solver_order=lowercase__ , solver_type=lowercase__ , prediction_type=lowercase__ , )
assert not torch.isnan(lowercase__ ).any(), "Samples have nan numbers"
def __UpperCAmelCase ( self :Optional[int] ):
self.check_over_configs(lower_order_final=lowercase__ )
self.check_over_configs(lower_order_final=lowercase__ )
def __UpperCAmelCase ( self :Any ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=lowercase__ , time_step=0 )
def __UpperCAmelCase ( self :List[str] ):
lowercase = self.full_loop()
lowercase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_mean.item() - 0.2464 ) < 1E-3
def __UpperCAmelCase ( self :Union[str, Any] ):
lowercase = self.full_loop(prediction_type='v_prediction' )
lowercase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_mean.item() - 0.1014 ) < 1E-3
def __UpperCAmelCase ( self :int ):
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config(thresholding=lowercase__ , dynamic_thresholding_ratio=0 )
lowercase = scheduler_class(**lowercase__ )
lowercase = 10
lowercase = self.dummy_model()
lowercase = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowercase__ )
for i, t in enumerate(scheduler.timesteps ):
lowercase = model(lowercase__ , lowercase__ )
lowercase = scheduler.step(lowercase__ , lowercase__ , lowercase__ ).prev_sample
assert sample.dtype == torch.floataa
def __UpperCAmelCase ( self :List[Any] , **lowercase__ :str ):
for scheduler_class in self.scheduler_classes:
lowercase = self.get_scheduler_config(**lowercase__ )
lowercase = scheduler_class(**lowercase__ )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 314 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a__ ( _snake_case ):
"""simple docstring"""
def __init__( self :Union[str, Any] , lowercase__ :NestedDataStructureLike[PathLike] , lowercase__ :Optional[NamedSplit] = None , lowercase__ :Optional[Features] = None , lowercase__ :str = None , lowercase__ :bool = False , lowercase__ :bool = False , lowercase__ :Optional[int] = None , **lowercase__ :Any , ):
super().__init__(
lowercase__ , split=lowercase__ , features=lowercase__ , cache_dir=lowercase__ , keep_in_memory=lowercase__ , streaming=lowercase__ , num_proc=lowercase__ , **lowercase__ , )
lowercase = path_or_paths if isinstance(lowercase__ , lowercase__ ) else {self.split: path_or_paths}
lowercase = Text(
cache_dir=lowercase__ , data_files=lowercase__ , features=lowercase__ , **lowercase__ , )
def __UpperCAmelCase ( self :Any ):
# Build iterable dataset
if self.streaming:
lowercase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
lowercase = None
lowercase = None
lowercase = None
lowercase = None
self.builder.download_and_prepare(
download_config=lowercase__ , download_mode=lowercase__ , verification_mode=lowercase__ , base_path=lowercase__ , num_proc=self.num_proc , )
lowercase = self.builder.as_dataset(
split=self.split , verification_mode=lowercase__ , in_memory=self.keep_in_memory )
return dataset
| 314 | 1 |
from __future__ import annotations
def lowercase__ ( A_: int ) -> bool:
"""simple docstring"""
__UpperCAmelCase =str(A_ )
return len(A_ ) == 9 and set(A_ ) == set("""123456789""" )
def lowercase__ ( ) -> int | None:
"""simple docstring"""
for base_num in range(9999 , 4999 , -1 ):
__UpperCAmelCase =100002 * base_num
if is_9_pandigital(A_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__UpperCAmelCase =1002003 * base_num
if is_9_pandigital(A_ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 68 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A = logging.get_logger(__name__)
__A = {"vocab_file": "spiece.model"}
__A = {
"vocab_file": {
"AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
}
}
__A = {
"AI-Sweden/gpt-sw3-126m": 20_48,
"AI-Sweden/gpt-sw3-350m": 20_48,
"AI-Sweden/gpt-sw3-1.6b": 20_48,
"AI-Sweden/gpt-sw3-6.7b": 20_48,
"AI-Sweden/gpt-sw3-20b": 20_48,
}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : int = VOCAB_FILES_NAMES
lowerCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Optional[Any] = ['input_ids', 'attention_mask']
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=False , __SCREAMING_SNAKE_CASE : List[str]=False , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : Optional[Any] , ) -> None:
__UpperCAmelCase ={} if sp_model_kwargs is None else sp_model_kwargs
__UpperCAmelCase =kwargs.get("""name_or_path""" )
if name_or_path is None:
logger.warning(
"""name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
""" you are testing the model, this can safely be ignored""" )
__UpperCAmelCase ="""None"""
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__UpperCAmelCase ="""<|endoftext|>""" if eos_token is None else eos_token
__UpperCAmelCase ="""<unk>""" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__UpperCAmelCase =unk_token if pad_token is None else pad_token
__UpperCAmelCase =eos_token if bos_token is None else bos_token
else:
__UpperCAmelCase ="""<pad>""" if pad_token is None else pad_token
__UpperCAmelCase ="""<s>""" if bos_token is None else bos_token
super().__init__(
do_lower_case=__SCREAMING_SNAKE_CASE , remove_space=__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE , bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
__UpperCAmelCase =do_lower_case
__UpperCAmelCase =remove_space
__UpperCAmelCase =keep_accents
__UpperCAmelCase =vocab_file
__UpperCAmelCase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__SCREAMING_SNAKE_CASE )
# Used for whitespace normalization in input texts
# fmt : off
__UpperCAmelCase ={""" """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """ """, """""", """"""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__UpperCAmelCase =re.compile(
f'''[{"".join(map(__SCREAMING_SNAKE_CASE , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8203] ) )}]''' )
def __getstate__( self : Any ) -> str:
__UpperCAmelCase =self.__dict__.copy()
__UpperCAmelCase =None
return state
def __setstate__( self : str , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
__UpperCAmelCase =d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
__UpperCAmelCase ={}
__UpperCAmelCase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _a ( self : Union[str, Any] ) -> int:
return len(self.sp_model )
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : str ) -> str:
__UpperCAmelCase =self.non_printing_characters_re.sub("""""" , __SCREAMING_SNAKE_CASE )
# Normalize whitespaces
__UpperCAmelCase ="""""".join([char if char not in self.whitespaces else """ """ for char in text] )
# NFC Unicode normalization
__UpperCAmelCase =unicodedata.normalize("""NFC""" , __SCREAMING_SNAKE_CASE )
return text
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[str]:
__UpperCAmelCase =self.preprocess_text(__SCREAMING_SNAKE_CASE )
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : str ) -> int:
return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : int ) -> str:
return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE )
@staticmethod
def _a ( __SCREAMING_SNAKE_CASE : str ) -> str:
return out_string
def _a ( self : Any , __SCREAMING_SNAKE_CASE : List[str] ) -> str:
__UpperCAmelCase =[]
__UpperCAmelCase =""""""
__UpperCAmelCase =False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE ) + token
__UpperCAmelCase =True
__UpperCAmelCase =[]
else:
current_sub_tokens.append(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =False
out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE )
return out_string
def _a ( self : Any ) -> Dict[str, int]:
__UpperCAmelCase ={self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__UpperCAmelCase =os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , """wb""" ) as fi:
__UpperCAmelCase =self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : Union[str, bool] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__UpperCAmelCase =self.preprocess_text(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =self.sp_model.encode(__SCREAMING_SNAKE_CASE )
else:
__UpperCAmelCase =[self.preprocess_text(__SCREAMING_SNAKE_CASE ) for t in text]
__UpperCAmelCase =self.sp_model.encode(__SCREAMING_SNAKE_CASE )
if return_tensors is True or return_tensors == "pt":
__UpperCAmelCase =torch.tensor(__SCREAMING_SNAKE_CASE )
return token_ids
def _a ( self : str , __SCREAMING_SNAKE_CASE : Union[int, List[int]] ) -> str:
return self.sp_model.decode(__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : "Conversation" ) -> List[int]:
__UpperCAmelCase =[f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
__UpperCAmelCase =(
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(__SCREAMING_SNAKE_CASE ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=__SCREAMING_SNAKE_CASE )
| 68 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase__ ( UpperCamelCase__ , unittest.TestCase ):
a : List[str] = LongformerTokenizer
a : Union[str, Any] = True
a : str = LongformerTokenizerFast
a : List[Any] = True
def UpperCAmelCase_ ( self ) -> Dict:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowerCAmelCase = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
__lowerCAmelCase = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
__lowerCAmelCase = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
__lowerCAmelCase = {"unk_token": "<unk>"}
__lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
__lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(UpperCamelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCamelCase ) )
def UpperCAmelCase_ ( self , **UpperCamelCase ) -> str:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase )
def UpperCAmelCase_ ( self , **UpperCamelCase ) -> str:
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase )
def UpperCAmelCase_ ( self , UpperCamelCase ) -> int:
__lowerCAmelCase = "lower newer"
__lowerCAmelCase = "lower newer"
return input_text, output_text
def UpperCAmelCase_ ( self ) -> int:
__lowerCAmelCase = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
__lowerCAmelCase = "lower newer"
__lowerCAmelCase = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
__lowerCAmelCase = tokenizer.tokenize(UpperCamelCase ) # , add_prefix_space=True)
self.assertListEqual(UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = tokens + [tokenizer.unk_token]
__lowerCAmelCase = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , UpperCamelCase )
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
__lowerCAmelCase = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=UpperCamelCase ) , [0, 3_1414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=UpperCamelCase ) , [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2] , )
@slow
def UpperCAmelCase_ ( self ) -> Optional[Any]:
__lowerCAmelCase = self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )
__lowerCAmelCase = tokenizer.encode("sequence builders" , add_special_tokens=UpperCamelCase )
__lowerCAmelCase = tokenizer.encode("multi-sequence build" , add_special_tokens=UpperCamelCase )
__lowerCAmelCase = tokenizer.encode(
"sequence builders" , add_special_tokens=UpperCamelCase , add_prefix_space=UpperCamelCase )
__lowerCAmelCase = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=UpperCamelCase , add_prefix_space=UpperCamelCase )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(UpperCamelCase )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(UpperCamelCase , UpperCamelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def UpperCAmelCase_ ( self ) -> Optional[Any]:
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = "Encode this sequence."
__lowerCAmelCase = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
__lowerCAmelCase = tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase , add_prefix_space=UpperCamelCase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase , add_prefix_space=UpperCamelCase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(UpperCamelCase , UpperCamelCase )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
__lowerCAmelCase = tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(UpperCamelCase , UpperCamelCase )
# Testing spaces after special tokens
__lowerCAmelCase = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase )} ) # mask token has a left space
__lowerCAmelCase = tokenizer.convert_tokens_to_ids(UpperCamelCase )
__lowerCAmelCase = "Encode <mask> sequence"
__lowerCAmelCase = "Encode <mask>sequence"
__lowerCAmelCase = tokenizer.encode(UpperCamelCase )
__lowerCAmelCase = encoded.index(UpperCamelCase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = tokenizer.encode(UpperCamelCase )
__lowerCAmelCase = encoded.index(UpperCamelCase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(UpperCamelCase , UpperCamelCase )
def UpperCAmelCase_ ( self ) -> List[Any]:
pass
def UpperCAmelCase_ ( self ) -> List[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(UpperCamelCase , **UpperCamelCase )
__lowerCAmelCase = self.tokenizer_class.from_pretrained(UpperCamelCase , **UpperCamelCase )
__lowerCAmelCase = "A, <mask> AllenNLP sentence."
__lowerCAmelCase = tokenizer_r.encode_plus(UpperCamelCase , add_special_tokens=UpperCamelCase , return_token_type_ids=UpperCamelCase )
__lowerCAmelCase = tokenizer_p.encode_plus(UpperCamelCase , add_special_tokens=UpperCamelCase , return_token_type_ids=UpperCamelCase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
__lowerCAmelCase = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
__lowerCAmelCase = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(
UpperCamelCase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
UpperCamelCase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def UpperCAmelCase_ ( self ) -> List[str]:
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase )
__lowerCAmelCase = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
__lowerCAmelCase = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , UpperCamelCase )
self.assertEqual(post_processor_state["add_prefix_space"] , UpperCamelCase )
self.assertEqual(post_processor_state["trim_offsets"] , UpperCamelCase )
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__lowerCAmelCase = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
__lowerCAmelCase = F'''{text_of_1_token} {text_of_1_token}'''
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase , use_fast=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase )
__lowerCAmelCase = tokenizer_r(UpperCamelCase , return_offsets_mapping=UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase ) + 1, len(UpperCamelCase ) + 1 + len(UpperCamelCase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase , use_fast=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase )
__lowerCAmelCase = tokenizer_r(UpperCamelCase , return_offsets_mapping=UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase ) + 1, len(UpperCamelCase ) + 1 + len(UpperCamelCase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase , use_fast=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase )
__lowerCAmelCase = tokenizer_r(UpperCamelCase , return_offsets_mapping=UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase ), len(UpperCamelCase ) + 1 + len(UpperCamelCase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase , use_fast=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase )
__lowerCAmelCase = tokenizer_r(UpperCamelCase , return_offsets_mapping=UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase ), len(UpperCamelCase ) + 1 + len(UpperCamelCase )) , )
__lowerCAmelCase = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase , use_fast=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase )
__lowerCAmelCase = tokenizer_r(UpperCamelCase , return_offsets_mapping=UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(UpperCamelCase ) + 1, 1 + len(UpperCamelCase ) + 1 + len(UpperCamelCase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase , use_fast=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase )
__lowerCAmelCase = tokenizer_r(UpperCamelCase , return_offsets_mapping=UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(UpperCamelCase ), 1 + len(UpperCamelCase ) + 1 + len(UpperCamelCase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase , use_fast=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase )
__lowerCAmelCase = tokenizer_r(UpperCamelCase , return_offsets_mapping=UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(UpperCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(UpperCamelCase ), 1 + len(UpperCamelCase ) + 1 + len(UpperCamelCase )) , ) | 39 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
lowerCAmelCase : List[Any] = get_logger(__name__)
class UpperCAmelCase__ :
def __init__( self , UpperCamelCase = None ) -> Union[str, Any]:
__lowerCAmelCase = (
os.path.join(UpperCamelCase , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
__lowerCAmelCase = Extractor
def UpperCAmelCase_ ( self , UpperCamelCase ) -> str:
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
__lowerCAmelCase = os.path.abspath(UpperCamelCase )
return os.path.join(self.extract_dir , hash_url_to_filename(UpperCamelCase ) )
def UpperCAmelCase_ ( self , UpperCamelCase , UpperCamelCase ) -> bool:
return force_extract or (
not os.path.isfile(UpperCamelCase ) and not (os.path.isdir(UpperCamelCase ) and os.listdir(UpperCamelCase ))
)
def UpperCAmelCase_ ( self , UpperCamelCase , UpperCamelCase = False ) -> str:
__lowerCAmelCase = self.extractor.infer_extractor_format(UpperCamelCase )
if not extractor_format:
return input_path
__lowerCAmelCase = self._get_output_path(UpperCamelCase )
if self._do_extract(UpperCamelCase , UpperCamelCase ):
self.extractor.extract(UpperCamelCase , UpperCamelCase , UpperCamelCase )
return output_path
class UpperCAmelCase__ ( UpperCamelCase__ ):
@classmethod
@abstractmethod
def UpperCAmelCase_ ( cls , UpperCamelCase , **UpperCamelCase ) -> bool:
...
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
...
class UpperCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
a : List[bytes] = []
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> List[Any]:
with open(UpperCamelCase , "rb" ) as f:
return f.read(UpperCamelCase )
@classmethod
def UpperCAmelCase_ ( cls , UpperCamelCase , UpperCamelCase = b"" ) -> bool:
if not magic_number:
__lowerCAmelCase = max(len(UpperCamelCase ) for cls_magic_number in cls.magic_numbers )
try:
__lowerCAmelCase = cls.read_magic_number(UpperCamelCase , UpperCamelCase )
except OSError:
return False
return any(magic_number.startswith(UpperCamelCase ) for cls_magic_number in cls.magic_numbers )
class UpperCAmelCase__ ( UpperCamelCase__ ):
@classmethod
def UpperCAmelCase_ ( cls , UpperCamelCase , **UpperCamelCase ) -> bool:
return tarfile.is_tarfile(UpperCamelCase )
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> Dict:
def resolved(UpperCamelCase ) -> str:
return os.path.realpath(os.path.abspath(UpperCamelCase ) )
def badpath(UpperCamelCase , UpperCamelCase ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(UpperCamelCase , UpperCamelCase ) ).startswith(UpperCamelCase )
def badlink(UpperCamelCase , UpperCamelCase ) -> bool:
# Links are interpreted relative to the directory containing the link
__lowerCAmelCase = resolved(os.path.join(UpperCamelCase , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=UpperCamelCase )
__lowerCAmelCase = resolved(UpperCamelCase )
for finfo in members:
if badpath(finfo.name , UpperCamelCase ):
logger.error(F'''Extraction of {finfo.name} is blocked (illegal path)''' )
elif finfo.issym() and badlink(UpperCamelCase , UpperCamelCase ):
logger.error(F'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}''' )
elif finfo.islnk() and badlink(UpperCamelCase , UpperCamelCase ):
logger.error(F'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}''' )
else:
yield finfo
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
os.makedirs(UpperCamelCase , exist_ok=UpperCamelCase )
__lowerCAmelCase = tarfile.open(UpperCamelCase )
tar_file.extractall(UpperCamelCase , members=TarExtractor.safemembers(UpperCamelCase , UpperCamelCase ) )
tar_file.close()
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : Any = [B"""\x1F\x8B"""]
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
with gzip.open(UpperCamelCase , "rb" ) as gzip_file:
with open(UpperCamelCase , "wb" ) as extracted_file:
shutil.copyfileobj(UpperCamelCase , UpperCamelCase )
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : List[Any] = [
B"""PK\x03\x04""",
B"""PK\x05\x06""", # empty archive
B"""PK\x07\x08""", # spanned archive
]
@classmethod
def UpperCAmelCase_ ( cls , UpperCamelCase , UpperCamelCase = b"" ) -> bool:
if super().is_extractable(UpperCamelCase , magic_number=UpperCamelCase ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(UpperCamelCase , "rb" ) as fp:
__lowerCAmelCase = _EndRecData(UpperCamelCase )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
__lowerCAmelCase = fp.read(UpperCamelCase ) # CD is where we expect it to be
if len(UpperCamelCase ) == sizeCentralDir:
__lowerCAmelCase = struct.unpack(UpperCamelCase , UpperCamelCase ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
os.makedirs(UpperCamelCase , exist_ok=UpperCamelCase )
with zipfile.ZipFile(UpperCamelCase , "r" ) as zip_file:
zip_file.extractall(UpperCamelCase )
zip_file.close()
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : Tuple = [B"""\xFD\x37\x7A\x58\x5A\x00"""]
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
with lzma.open(UpperCamelCase ) as compressed_file:
with open(UpperCamelCase , "wb" ) as extracted_file:
shutil.copyfileobj(UpperCamelCase , UpperCamelCase )
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : str = [B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""] # RAR_ID # RAR5_ID
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
if not config.RARFILE_AVAILABLE:
raise ImportError("Please pip install rarfile" )
import rarfile
os.makedirs(UpperCamelCase , exist_ok=UpperCamelCase )
__lowerCAmelCase = rarfile.RarFile(UpperCamelCase )
rf.extractall(UpperCamelCase )
rf.close()
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : int = [B"""\x28\xb5\x2F\xFD"""]
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("Please pip install zstandard" )
import zstandard as zstd
__lowerCAmelCase = zstd.ZstdDecompressor()
with open(UpperCamelCase , "rb" ) as ifh, open(UpperCamelCase , "wb" ) as ofh:
dctx.copy_stream(UpperCamelCase , UpperCamelCase )
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : Any = [B"""\x42\x5A\x68"""]
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
with bza.open(UpperCamelCase , "rb" ) as compressed_file:
with open(UpperCamelCase , "wb" ) as extracted_file:
shutil.copyfileobj(UpperCamelCase , UpperCamelCase )
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : Any = [B"""\x37\x7A\xBC\xAF\x27\x1C"""]
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
if not config.PY7ZR_AVAILABLE:
raise ImportError("Please pip install py7zr" )
import pyazr
os.makedirs(UpperCamelCase , exist_ok=UpperCamelCase )
with pyazr.SevenZipFile(UpperCamelCase , "r" ) as archive:
archive.extractall(UpperCamelCase )
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : Any = [B"""\x04\x22\x4D\x18"""]
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> None:
if not config.LZ4_AVAILABLE:
raise ImportError("Please pip install lz4" )
import lza.frame
with lza.frame.open(UpperCamelCase , "rb" ) as compressed_file:
with open(UpperCamelCase , "wb" ) as extracted_file:
shutil.copyfileobj(UpperCamelCase , UpperCamelCase )
class UpperCAmelCase__ :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
a : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def UpperCAmelCase_ ( cls ) -> Optional[Any]:
return max(
len(UpperCamelCase )
for extractor in cls.extractors.values()
if issubclass(UpperCamelCase , UpperCamelCase )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ) -> Dict:
try:
return MagicNumberBaseExtractor.read_magic_number(UpperCamelCase , magic_number_length=UpperCamelCase )
except OSError:
return b""
@classmethod
def UpperCAmelCase_ ( cls , UpperCamelCase , UpperCamelCase = False ) -> bool:
warnings.warn(
"Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'infer_extractor_format' instead." , category=UpperCamelCase , )
__lowerCAmelCase = cls.infer_extractor_format(UpperCamelCase )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def UpperCAmelCase_ ( cls , UpperCamelCase ) -> str: # <Added version="2.4.0"/>
__lowerCAmelCase = cls._get_magic_number_max_length()
__lowerCAmelCase = cls._read_magic_number(UpperCamelCase , UpperCamelCase )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(UpperCamelCase , magic_number=UpperCamelCase ):
return extractor_format
@classmethod
def UpperCAmelCase_ ( cls , UpperCamelCase , UpperCamelCase , UpperCamelCase = None , UpperCamelCase = "deprecated" , ) -> None:
os.makedirs(os.path.dirname(UpperCamelCase ) , exist_ok=UpperCamelCase )
# Prevent parallel extractions
__lowerCAmelCase = str(Path(UpperCamelCase ).with_suffix(".lock" ) )
with FileLock(UpperCamelCase ):
shutil.rmtree(UpperCamelCase , ignore_errors=UpperCamelCase )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(UpperCamelCase , UpperCamelCase ): # passed as positional arg
warnings.warn(
"Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'extractor_format' instead." , category=UpperCamelCase , )
__lowerCAmelCase = extractor if extractor != "deprecated" else extractor_format
else:
__lowerCAmelCase = cls.extractors[extractor_format]
return extractor.extract(UpperCamelCase , UpperCamelCase )
else:
warnings.warn(
"Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
"exception in 3.0.0." , category=UpperCamelCase , )
for extractor in cls.extractors.values():
if extractor.is_extractable(UpperCamelCase ):
return extractor.extract(UpperCamelCase , UpperCamelCase ) | 39 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
__a = dict(zip(__A , range(len(__A ) ) ) )
__a = {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
}
__a = {
"feature_size": 1,
"padding_value": 0.0,
"sampling_rate": 16_000,
"return_attention_mask": False,
"do_normalize": True,
}
__a = tempfile.mkdtemp()
__a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__a = os.path.join(self.tmpdirname , __A )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.feature_extraction_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
# load decoder from hub
__a = "hf-internal-testing/ngram-beam-search-decoder"
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> Any:
'''simple docstring'''
__a = self.add_kwargs_tokens_map.copy()
kwargs.update(__A )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **__A )
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> str:
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **__A )
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> int:
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **__A )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.get_tokenizer()
__a = self.get_feature_extractor()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
processor.save_pretrained(self.tmpdirname )
__a = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , __A )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
__a = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(__A , '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=__A , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
__a = floats_list((3, 1_000) )
__a = feature_extractor(__A , return_tensors='''np''' )
__a = processor(__A , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
__a = "This is a test string"
__a = processor(text=__A )
__a = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=(2, 10, 16) , _snake_case=77 ) -> int:
'''simple docstring'''
np.random.seed(__A )
return np.random.rand(*__A )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
__a = self._get_dummy_logits(shape=(10, 16) , seed=13 )
__a = processor.decode(__A )
__a = decoder.decode_beams(__A )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('''</s> <s> </s>''' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
__a = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
__a = processor.batch_decode(__A )
else:
with get_context(__A ).Pool() as pool:
__a = processor.batch_decode(__A , __A )
__a = list(__A )
with get_context('''fork''' ).Pool() as p:
__a = decoder.decode_beams_batch(__A , __A )
__a = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(__A , decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] , decoded_processor.text )
self.assertListEqual(__A , decoded_processor.logit_score )
self.assertListEqual(__A , decoded_processor.lm_score )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
__a = self._get_dummy_logits()
__a = 15
__a = -20.0
__a = -4.0
__a = processor.batch_decode(
__A , beam_width=__A , beam_prune_logp=__A , token_min_logp=__A , )
__a = decoded_processor_out.text
__a = list(__A )
with get_context('''fork''' ).Pool() as pool:
__a = decoder.decode_beams_batch(
__A , __A , beam_width=__A , beam_prune_logp=__A , token_min_logp=__A , )
__a = [d[0][0] for d in decoded_decoder_out]
__a = [d[0][2] for d in decoded_decoder_out]
__a = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(__A , __A )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] , __A )
self.assertTrue(np.array_equal(__A , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , __A , atol=1E-3 ) )
self.assertTrue(np.array_equal(__A , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9_474] , __A , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
__a = self._get_dummy_logits()
__a = 2.0
__a = 5.0
__a = -20.0
__a = True
__a = processor.batch_decode(
__A , alpha=__A , beta=__A , unk_score_offset=__A , lm_score_boundary=__A , )
__a = decoded_processor_out.text
__a = list(__A )
decoder.reset_params(
alpha=__A , beta=__A , unk_score_offset=__A , lm_score_boundary=__A , )
with get_context('''fork''' ).Pool() as pool:
__a = decoder.decode_beams_batch(
__A , __A , )
__a = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(__A , __A )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] , __A )
__a = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , __A )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__a = processor.decoder.model_container[processor.decoder._model_key]
__a = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
__a = os.listdir(__A )
__a = ["alphabet.json", "language_model"]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(__A , __A )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = snapshot_download('''hf-internal-testing/processor_with_lm''' )
__a = WavaVecaProcessorWithLM.from_pretrained(__A )
__a = processor.decoder.model_container[processor.decoder._model_key]
__a = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
__a = os.listdir(__A )
__a = os.listdir(__A )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(__A , __A )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__a = AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__a = floats_list((3, 1_000) )
__a = processor_wavaveca(__A , return_tensors='''np''' )
__a = processor_auto(__A , return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
__a = self._get_dummy_logits()
__a = processor_wavaveca.batch_decode(__A )
__a = processor_auto.batch_decode(__A )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = [d[key] for d in offsets]
return retrieved_list
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__a = self._get_dummy_logits()[0]
__a = processor.decode(__A , output_word_offsets=__A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(__A , __A ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''end_offset''' ) , [1, 3, 5] )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__a = self._get_dummy_logits()
__a = processor.batch_decode(__A , output_word_offsets=__A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(__A , __A ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(__A , '''word''' ) ) for o in outputs['''word_offsets''']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''end_offset''' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
import torch
__a = load_dataset('''common_voice''' , '''en''' , split='''train''' , streaming=__A )
__a = ds.cast_column('''audio''' , datasets.Audio(sampling_rate=16_000 ) )
__a = iter(__A )
__a = next(__A )
__a = AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
__a = WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
__a = processor(sample['''audio''']['''array'''] , return_tensors='''pt''' ).input_values
with torch.no_grad():
__a = model(__A ).logits.cpu().numpy()
__a = processor.decode(logits[0] , output_word_offsets=__A )
__a = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
__a = [
{
"start_time": d["start_offset"] * time_offset,
"end_time": d["end_offset"] * time_offset,
"word": d["word"],
}
for d in output["word_offsets"]
]
__a = "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(__A , '''word''' ) ) , __A )
self.assertEqual(''' '''.join(self.get_from_offsets(__A , '''word''' ) ) , output.text )
# output times
__a = torch.tensor(self.get_from_offsets(__A , '''start_time''' ) )
__a = torch.tensor(self.get_from_offsets(__A , '''end_time''' ) )
# fmt: off
__a = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
__a = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(__A , __A , atol=0.01 ) )
self.assertTrue(torch.allclose(__A , __A , atol=0.01 ) ) | 219 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , __A : int , __A : int , __A : int , __A : Union[str, Any]=0.0 , __A : Optional[int] = None , __A : str = "geglu" , __A : Optional[int] = None , __A : bool = False , __A : bool = False , __A : bool = False , __A : bool = False , __A : bool = True , __A : str = "layer_norm" , __A : bool = False , ):
super().__init__()
snake_case__ : str = only_cross_attention
snake_case__ : str = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
snake_case__ : Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.''' )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
snake_case__ : Tuple = AdaLayerNorm(__A , __A )
elif self.use_ada_layer_norm_zero:
snake_case__ : Optional[Any] = AdaLayerNormZero(__A , __A )
else:
snake_case__ : List[str] = nn.LayerNorm(__A , elementwise_affine=__A )
snake_case__ : Optional[Any] = Attention(
query_dim=__A , heads=__A , dim_head=__A , dropout=__A , bias=__A , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=__A , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
snake_case__ : Union[str, Any] = (
AdaLayerNorm(__A , __A )
if self.use_ada_layer_norm
else nn.LayerNorm(__A , elementwise_affine=__A )
)
snake_case__ : List[Any] = Attention(
query_dim=__A , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=__A , dim_head=__A , dropout=__A , bias=__A , upcast_attention=__A , ) # is self-attn if encoder_hidden_states is none
else:
snake_case__ : Tuple = None
snake_case__ : List[str] = None
# 3. Feed-forward
snake_case__ : Optional[int] = nn.LayerNorm(__A , elementwise_affine=__A )
snake_case__ : str = FeedForward(__A , dropout=__A , activation_fn=__A , final_dropout=__A )
# let chunk size default to None
snake_case__ : List[Any] = None
snake_case__ : str = 0
def _lowercase ( self : int , __A : Optional[int] , __A : int ):
# Sets chunk feed-forward
snake_case__ : Dict = chunk_size
snake_case__ : str = dim
def _lowercase ( self : List[str] , __A : torch.FloatTensor , __A : Optional[torch.FloatTensor] = None , __A : Optional[torch.FloatTensor] = None , __A : Optional[torch.FloatTensor] = None , __A : Optional[torch.LongTensor] = None , __A : Dict[str, Any] = None , __A : Optional[torch.LongTensor] = None , ):
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
snake_case__ : Tuple = self.norma(__A , __A )
elif self.use_ada_layer_norm_zero:
snake_case__, snake_case__, snake_case__, snake_case__, snake_case__ : Dict = self.norma(
__A , __A , __A , hidden_dtype=hidden_states.dtype )
else:
snake_case__ : str = self.norma(__A )
snake_case__ : Union[str, Any] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
snake_case__ : Union[str, Any] = self.attna(
__A , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=__A , **__A , )
if self.use_ada_layer_norm_zero:
snake_case__ : str = gate_msa.unsqueeze(1 ) * attn_output
snake_case__ : Dict = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
snake_case__ : List[str] = (
self.norma(__A , __A ) if self.use_ada_layer_norm else self.norma(__A )
)
snake_case__ : int = self.attna(
__A , encoder_hidden_states=__A , attention_mask=__A , **__A , )
snake_case__ : Dict = attn_output + hidden_states
# 3. Feed-forward
snake_case__ : Any = self.norma(__A )
if self.use_ada_layer_norm_zero:
snake_case__ : int = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.''' )
snake_case__ : Dict = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
snake_case__ : str = torch.cat(
[self.ff(__A ) for hid_slice in norm_hidden_states.chunk(__A , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
snake_case__ : Dict = self.ff(__A )
if self.use_ada_layer_norm_zero:
snake_case__ : List[Any] = gate_mlp.unsqueeze(1 ) * ff_output
snake_case__ : str = ff_output + hidden_states
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[Any] , __A : int , __A : Optional[int] = None , __A : int = 4 , __A : float = 0.0 , __A : str = "geglu" , __A : bool = False , ):
super().__init__()
snake_case__ : str = int(dim * mult )
snake_case__ : Any = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
snake_case__ : Tuple = GELU(__A , __A )
if activation_fn == "gelu-approximate":
snake_case__ : Union[str, Any] = GELU(__A , __A , approximate="tanh" )
elif activation_fn == "geglu":
snake_case__ : Dict = GEGLU(__A , __A )
elif activation_fn == "geglu-approximate":
snake_case__ : Dict = ApproximateGELU(__A , __A )
snake_case__ : Tuple = nn.ModuleList([] )
# project in
self.net.append(__A )
# project dropout
self.net.append(nn.Dropout(__A ) )
# project out
self.net.append(nn.Linear(__A , __A ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(__A ) )
def _lowercase ( self : int , __A : Dict ):
for module in self.net:
snake_case__ : Optional[Any] = module(__A )
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __A : int , __A : int , __A : str = "none" ):
super().__init__()
snake_case__ : Optional[int] = nn.Linear(__A , __A )
snake_case__ : List[str] = approximate
def _lowercase ( self : List[str] , __A : Optional[Any] ):
if gate.device.type != "mps":
return F.gelu(__A , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _lowercase ( self : Any , __A : str ):
snake_case__ : List[str] = self.proj(__A )
snake_case__ : List[Any] = self.gelu(__A )
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : int , __A : int , __A : int ):
super().__init__()
snake_case__ : Any = nn.Linear(__A , dim_out * 2 )
def _lowercase ( self : Dict , __A : Optional[int] ):
if gate.device.type != "mps":
return F.gelu(__A )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _lowercase ( self : str , __A : List[Any] ):
snake_case__, snake_case__ : Optional[int] = self.proj(__A ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(__A )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __A : int , __A : int ):
super().__init__()
snake_case__ : Optional[int] = nn.Linear(__A , __A )
def _lowercase ( self : Union[str, Any] , __A : Tuple ):
snake_case__ : List[str] = self.proj(__A )
return x * torch.sigmoid(1.7_0_2 * x )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , __A : List[Any] , __A : str ):
super().__init__()
snake_case__ : Union[str, Any] = nn.Embedding(__A , __A )
snake_case__ : Optional[int] = nn.SiLU()
snake_case__ : Optional[int] = nn.Linear(__A , embedding_dim * 2 )
snake_case__ : Tuple = nn.LayerNorm(__A , elementwise_affine=__A )
def _lowercase ( self : Any , __A : Dict , __A : str ):
snake_case__ : Any = self.linear(self.silu(self.emb(__A ) ) )
snake_case__, snake_case__ : List[Any] = torch.chunk(__A , 2 )
snake_case__ : Tuple = self.norm(__A ) * (1 + scale) + shift
return x
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : str , __A : int , __A : List[str] ):
super().__init__()
snake_case__ : List[str] = CombinedTimestepLabelEmbeddings(__A , __A )
snake_case__ : str = nn.SiLU()
snake_case__ : Optional[Any] = nn.Linear(__A , 6 * embedding_dim , bias=__A )
snake_case__ : Tuple = nn.LayerNorm(__A , elementwise_affine=__A , eps=1e-6 )
def _lowercase ( self : Dict , __A : List[str] , __A : Optional[int] , __A : Any , __A : Optional[Any]=None ):
snake_case__ : List[str] = self.linear(self.silu(self.emb(__A , __A , hidden_dtype=__A ) ) )
snake_case__, snake_case__, snake_case__, snake_case__, snake_case__, snake_case__ : Optional[int] = emb.chunk(6 , dim=1 )
snake_case__ : List[Any] = self.norm(__A ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[Any] , __A : int , __A : int , __A : int , __A : Optional[str] = None , __A : float = 1e-5 ):
super().__init__()
snake_case__ : int = num_groups
snake_case__ : Any = eps
if act_fn is None:
snake_case__ : Dict = None
else:
snake_case__ : Any = get_activation(__A )
snake_case__ : List[str] = nn.Linear(__A , out_dim * 2 )
def _lowercase ( self : str , __A : Optional[Any] , __A : Tuple ):
if self.act:
snake_case__ : List[Any] = self.act(__A )
snake_case__ : Union[str, Any] = self.linear(__A )
snake_case__ : Optional[int] = emb[:, :, None, None]
snake_case__, snake_case__ : List[str] = emb.chunk(2 , dim=1 )
snake_case__ : Dict = F.group_norm(__A , self.num_groups , eps=self.eps )
snake_case__ : List[str] = x * (1 + scale) + shift
return x
| 297 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a_ : int = logging.get_logger(__name__)
a_ : Tuple = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class __lowercase( lowercase__ ):
'''simple docstring'''
__a : Optional[Any] = 'dpt'
def __init__( self , __a=768 , __a=12 , __a=12 , __a=3072 , __a="gelu" , __a=0.0 , __a=0.0 , __a=0.02 , __a=1E-12 , __a=384 , __a=16 , __a=3 , __a=False , __a=True , __a=[2, 5, 8, 11] , __a="project" , __a=[4, 2, 1, 0.5] , __a=[96, 192, 384, 768] , __a=256 , __a=-1 , __a=False , __a=True , __a=0.4 , __a=255 , __a=0.1 , __a=[1, 1024, 24, 24] , __a=[0, 1] , __a=None , **__a , ):
super().__init__(**__a )
__lowerCamelCase : str = hidden_size
__lowerCamelCase : int = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('Initializing the config with a `BiT` backbone.' )
__lowerCamelCase : Tuple = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
}
__lowerCamelCase : str = BitConfig(**__a )
elif isinstance(__a , __a ):
logger.info('Initializing the config with a `BiT` backbone.' )
__lowerCamelCase : Tuple = BitConfig(**__a )
elif isinstance(__a , __a ):
__lowerCamelCase : List[Any] = backbone_config
else:
raise ValueError(
f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )
__lowerCamelCase : Union[str, Any] = backbone_featmap_shape
__lowerCamelCase : Tuple = neck_ignore_stages
if readout_type != "project":
raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.' )
else:
__lowerCamelCase : Dict = None
__lowerCamelCase : Any = None
__lowerCamelCase : List[Any] = []
__lowerCamelCase : Dict = num_hidden_layers
__lowerCamelCase : Tuple = num_attention_heads
__lowerCamelCase : Tuple = intermediate_size
__lowerCamelCase : Union[str, Any] = hidden_act
__lowerCamelCase : List[str] = hidden_dropout_prob
__lowerCamelCase : Dict = attention_probs_dropout_prob
__lowerCamelCase : Union[str, Any] = initializer_range
__lowerCamelCase : List[Any] = layer_norm_eps
__lowerCamelCase : Any = image_size
__lowerCamelCase : Tuple = patch_size
__lowerCamelCase : Dict = num_channels
__lowerCamelCase : Dict = qkv_bias
__lowerCamelCase : List[Any] = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']' )
__lowerCamelCase : int = readout_type
__lowerCamelCase : Optional[Any] = reassemble_factors
__lowerCamelCase : str = neck_hidden_sizes
__lowerCamelCase : str = fusion_hidden_size
__lowerCamelCase : Any = head_in_index
__lowerCamelCase : List[Any] = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
__lowerCamelCase : Any = use_auxiliary_head
__lowerCamelCase : Tuple = auxiliary_loss_weight
__lowerCamelCase : int = semantic_loss_ignore_index
__lowerCamelCase : int = semantic_classifier_dropout
def snake_case_ ( self ):
__lowerCamelCase : str = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
__lowerCamelCase : Dict = self.backbone_config.to_dict()
__lowerCamelCase : Union[str, Any] = self.__class__.model_type
return output
| 708 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
__lowerCamelCase : List[str] = parent
__lowerCamelCase : Dict = batch_size
__lowerCamelCase : str = seq_length
__lowerCamelCase : Optional[int] = is_training
__lowerCamelCase : Dict = use_input_mask
__lowerCamelCase : Dict = use_token_type_ids
__lowerCamelCase : Dict = use_labels
__lowerCamelCase : Optional[Any] = vocab_size
__lowerCamelCase : Any = hidden_size
__lowerCamelCase : List[Any] = num_hidden_layers
__lowerCamelCase : Tuple = num_attention_heads
__lowerCamelCase : Any = intermediate_size
__lowerCamelCase : Optional[Any] = hidden_act
__lowerCamelCase : Any = hidden_dropout_prob
__lowerCamelCase : Optional[Any] = attention_probs_dropout_prob
__lowerCamelCase : List[Any] = max_position_embeddings
__lowerCamelCase : Optional[Any] = type_vocab_size
__lowerCamelCase : Dict = type_sequence_label_size
__lowerCamelCase : Any = initializer_range
__lowerCamelCase : Union[str, Any] = num_labels
__lowerCamelCase : Tuple = num_choices
__lowerCamelCase : str = relative_attention
__lowerCamelCase : Optional[int] = position_biased_input
__lowerCamelCase : int = pos_att_type
__lowerCamelCase : str = scope
def snake_case_ ( self ):
__lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase : int = None
if self.use_input_mask:
__lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__lowerCamelCase : Tuple = None
if self.use_token_type_ids:
__lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : int = None
if self.use_labels:
__lowerCamelCase : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ):
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def snake_case_ ( self , __a ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : int = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : str = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__lowerCamelCase : str = model(__a , token_type_ids=__a )[0]
__lowerCamelCase : Optional[Any] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : List[str] = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Union[str, Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[int] = self.num_labels
__lowerCamelCase : List[Any] = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCamelCase : Optional[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : int = self.num_labels
__lowerCamelCase : Dict = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : int = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[Any] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Any = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Any = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : List[Any] = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_ ( self ):
__lowerCamelCase : Any = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : List[str] = config_and_inputs
__lowerCamelCase : int = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __lowercase( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
__a : Dict = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
__a : Tuple = (
{
'feature-extraction': DebertaVaModel,
'fill-mask': DebertaVaForMaskedLM,
'question-answering': DebertaVaForQuestionAnswering,
'text-classification': DebertaVaForSequenceClassification,
'token-classification': DebertaVaForTokenClassification,
'zero-shot': DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : str = True
__a : Dict = False
__a : Tuple = False
__a : Optional[Any] = False
__a : List[Any] = False
def snake_case_ ( self ):
__lowerCamelCase : List[str] = DebertaVaModelTester(self )
__lowerCamelCase : Tuple = ConfigTester(self , config_class=__a , hidden_size=37 )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
__lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def snake_case_ ( self ):
__lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def snake_case_ ( self ):
__lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def snake_case_ ( self ):
__lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def snake_case_ ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : Tuple = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def snake_case_ ( self ):
pass
@slow
def snake_case_ ( self ):
__lowerCamelCase : Any = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__lowerCamelCase : Any = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase : Any = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowerCamelCase : Union[str, Any] = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__lowerCamelCase : str = torch.tensor(
[[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f'''{output[:, 1:4, 1:4]}''' )
| 263 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def A__ ( ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
_UpperCAmelCase = Image.open(requests.get(A__ , stream=A__ ).raw ).convert("RGB" )
return image
def A__ ( A__ ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias") )
# fmt: on
return rename_keys
def A__ ( A__ , A__ , A__ ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = dct.pop(A__ )
_UpperCAmelCase = val
def A__ ( A__ , A__ ) -> int:
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
_UpperCAmelCase = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias""" )
_UpperCAmelCase = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
_UpperCAmelCase = torch.cat((q_bias, torch.zeros_like(A__ , requires_grad=A__ ), v_bias) )
_UpperCAmelCase = qkv_bias
def A__ ( A__ ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = 364 if "coco" in model_name else 224
_UpperCAmelCase = InstructBlipVisionConfig(image_size=A__ ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
_UpperCAmelCase = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
_UpperCAmelCase = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
_UpperCAmelCase = LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf" , vocab_size=3_2001 ).to_dict()
elif "vicuna-13b" in model_name:
_UpperCAmelCase = LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf" , vocab_size=3_2001 ).to_dict()
else:
raise ValueError("Model name not supported" )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
_UpperCAmelCase = InstructBlipQFormerConfig(vocab_size=3_0523 ).to_dict()
_UpperCAmelCase = InstructBlipConfig(vision_config=A__ , text_config=A__ , qformer_config=A__ )
return config, image_size
@torch.no_grad()
def A__ ( A__ , A__=None , A__=False ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = AutoTokenizer.from_pretrained("bert-base-uncased" , truncation_side="left" )
qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"} )
if "t5" in model_name:
_UpperCAmelCase = TaTokenizerFast.from_pretrained("google/flan-t5-xl" , truncation_side="left" )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
_UpperCAmelCase = LlamaTokenizerFast.from_pretrained(
"huggyllama/llama-7b" , truncation_side="left" , bos_token="</s>" , unk_token="</s>" )
tokenizer.add_special_tokens({"pad_token": "[PAD]"} )
_UpperCAmelCase , _UpperCAmelCase = get_blipa_config(A__ )
_UpperCAmelCase = InstructBlipForConditionalGeneration(A__ ).eval()
_UpperCAmelCase = {
"instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
"instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
"instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
"instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
}
_UpperCAmelCase , _UpperCAmelCase = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
_UpperCAmelCase = "cuda:1" if torch.cuda.is_available() else "cpu"
_UpperCAmelCase = "cuda:2" if torch.cuda.is_available() else "cpu"
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = load_model_and_preprocess(
name=A__ , model_type=A__ , is_eval=A__ , device=A__ )
original_model.eval()
print("Done!" )
# update state dict keys
_UpperCAmelCase = original_model.state_dict()
_UpperCAmelCase = create_rename_keys(A__ )
for src, dest in rename_keys:
rename_key(A__ , A__ , A__ )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
_UpperCAmelCase = state_dict.pop(A__ )
if key.startswith("Qformer.bert" ):
_UpperCAmelCase = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
_UpperCAmelCase = key.replace("self" , "attention" )
if "llm_proj" in key:
_UpperCAmelCase = key.replace("llm_proj" , "language_projection" )
if "t5_proj" in key:
_UpperCAmelCase = key.replace("t5_proj" , "language_projection" )
if key.startswith("llm_model" ):
_UpperCAmelCase = key.replace("llm_model" , "language_model" )
if key.startswith("t5" ):
_UpperCAmelCase = key.replace("t5" , "language" )
_UpperCAmelCase = val
# read in qv biases
read_in_q_v_bias(A__ , A__ )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(A__ , strict=A__ )
_UpperCAmelCase = load_demo_image()
_UpperCAmelCase = "What is unusual about this image?"
# create processor
_UpperCAmelCase = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=A__ , image_std=A__ )
_UpperCAmelCase = InstructBlipProcessor(
image_processor=A__ , tokenizer=A__ , qformer_tokenizer=A__ , )
_UpperCAmelCase = processor(images=A__ , text=A__ , return_tensors="pt" ).to(A__ )
# make sure processor creates exact same pixel values
_UpperCAmelCase = vis_processors["eval"](A__ ).unsqueeze(0 ).to(A__ )
_UpperCAmelCase = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , A__ )
original_model.to(A__ )
hf_model.to(A__ )
with torch.no_grad():
if "vicuna" in model_name:
_UpperCAmelCase = original_model({"image": original_pixel_values, "text_input": [prompt]} ).logits
_UpperCAmelCase = hf_model(**A__ ).logits
else:
_UpperCAmelCase = original_model(
{"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]} ).logits
_UpperCAmelCase = tokenizer("\n" , return_tensors="pt" ).input_ids.to(A__ )
_UpperCAmelCase = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
_UpperCAmelCase = hf_model(**A__ , labels=A__ ).logits
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
_UpperCAmelCase = 1E-4 if "vicuna" in model_name else 1E-5
assert torch.allclose(original_logits.to(logits.device ) , A__ , atol=A__ )
print("Looks ok!" )
print("Generating with original model..." )
_UpperCAmelCase = original_model.generate({"image": original_pixel_values, "prompt": prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print("Generating with HF model..." )
_UpperCAmelCase = hf_model.generate(
**A__ , do_sample=A__ , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
_UpperCAmelCase = 2
print("Original generation:" , A__ )
_UpperCAmelCase = processor.batch_decode(A__ , skip_special_tokens=A__ )
_UpperCAmelCase = [text.strip() for text in output_text]
print("HF generation:" , A__ )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(A__ )
hf_model.save_pretrained(A__ )
if push_to_hub:
processor.push_to_hub(F"""Salesforce/{model_name}""" )
hf_model.push_to_hub(F"""Salesforce/{model_name}""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
SCREAMING_SNAKE_CASE_ = [
'''instructblip-vicuna-7b''',
'''instructblip-vicuna-13b''',
'''instructblip-flan-t5-xl''',
'''instructblip-flan-t5-xxl''',
]
parser.add_argument(
'''--model_name''',
default='''instructblip-flan-t5-xl''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 426 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a :
"""simple docstring"""
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=32 , snake_case_=3 , snake_case_=4 , snake_case_=[10, 20, 30, 40] , snake_case_=[2, 2, 3, 2] , snake_case_=True , snake_case_=True , snake_case_=37 , snake_case_="gelu" , snake_case_=10 , snake_case_=0.02 , snake_case_=["stage2", "stage3", "stage4"] , snake_case_=[2, 3, 4] , snake_case_=None , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = num_stages
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = depths
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = num_labels
_UpperCAmelCase = initializer_range
_UpperCAmelCase = out_features
_UpperCAmelCase = out_indices
_UpperCAmelCase = scope
def __A ( self ) -> Tuple:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def __A ( self ) -> int:
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=snake_case_ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def __A ( self , snake_case_ , snake_case_ , snake_case_ ) -> str:
_UpperCAmelCase = ConvNextVaModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __A ( self , snake_case_ , snake_case_ , snake_case_ ) -> Union[str, Any]:
_UpperCAmelCase = ConvNextVaForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self , snake_case_ , snake_case_ , snake_case_ ) -> str:
_UpperCAmelCase = ConvNextVaBackbone(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_UpperCAmelCase = None
_UpperCAmelCase = ConvNextVaBackbone(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def __A ( self ) -> List[Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
def __A ( self ) -> Tuple:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class a ( _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, unittest.TestCase ):
"""simple docstring"""
A__ : Dict = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
A__ : Optional[Any] = (
{"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
A__ : Any = False
A__ : str = False
A__ : List[str] = False
A__ : Optional[int] = False
A__ : Dict = False
def __A ( self ) -> List[Any]:
_UpperCAmelCase = ConvNextVaModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ , hidden_size=37 )
def __A ( self ) -> int:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __A ( self ) -> Any:
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def __A ( self ) -> int:
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def __A ( self ) -> Union[str, Any]:
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def __A ( self ) -> Optional[int]:
pass
def __A ( self ) -> Optional[Any]:
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_with_labels()
_UpperCAmelCase = True
if model_class.__name__ in [
*get_values(snake_case_ ),
*get_values(snake_case_ ),
]:
continue
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_UpperCAmelCase = model(**snake_case_ ).loss
loss.backward()
def __A ( self ) -> str:
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_with_labels()
_UpperCAmelCase = False
_UpperCAmelCase = True
if (
model_class.__name__
in [*get_values(snake_case_ ), *get_values(snake_case_ )]
or not model_class.supports_gradient_checkpointing
):
continue
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.gradient_checkpointing_enable()
model.train()
_UpperCAmelCase = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_UpperCAmelCase = model(**snake_case_ ).loss
loss.backward()
def __A ( self ) -> Tuple:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
def __A ( self ) -> int:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def __A ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ ):
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCAmelCase = self.model_tester.num_stages
self.assertEqual(len(snake_case_ ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def __A ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
@slow
def __A ( self ) -> Dict:
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = ConvNextVaModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def A__ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self ) -> Dict:
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def __A ( self ) -> int:
_UpperCAmelCase = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(snake_case_ )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = preprocessor(images=snake_case_ , return_tensors="pt" ).to(snake_case_ )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_UpperCAmelCase = torch.tensor([0.99_96, 0.19_66, -0.43_86] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1e-4 ) )
| 426 | 1 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
lowercase__ : str = {}
lowercase__ : Dict = job["started_at"]
lowercase__ : Tuple = job["completed_at"]
lowercase__ : Optional[int] = date_parser.parse(SCREAMING_SNAKE_CASE_ )
lowercase__ : Dict = date_parser.parse(SCREAMING_SNAKE_CASE_ )
lowercase__ : Optional[Any] = round((end_datetime - start_datetime).total_seconds() / 60.0 )
lowercase__ : List[str] = start
lowercase__ : int = end
lowercase__ : int = duration_in_min
return job_info
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=None ) -> List[str]:
lowercase__ : str = None
if token is not None:
lowercase__ : Optional[Any] = {"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}
lowercase__ : Dict = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
lowercase__ : Union[str, Any] = requests.get(SCREAMING_SNAKE_CASE_ ,headers=SCREAMING_SNAKE_CASE_ ).json()
lowercase__ : Tuple = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(SCREAMING_SNAKE_CASE_ ) for job in result["jobs"]} )
lowercase__ : Dict = math.ceil((result["total_count"] - 1_00) / 1_00 )
for i in range(SCREAMING_SNAKE_CASE_ ):
lowercase__ : int = requests.get(url + F"""&page={i + 2}""" ,headers=SCREAMING_SNAKE_CASE_ ).json()
job_time.update({job["name"]: extract_time_from_single_job(SCREAMING_SNAKE_CASE_ ) for job in result["jobs"]} )
return job_time
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
if __name__ == "__main__":
__a : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
__a : Optional[int] = parser.parse_args()
__a : Union[str, Any] = get_job_time(args.workflow_run_id)
__a : Optional[int] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f'{k}: {v["duration"]}') | 720 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
__a : str = logging.get_logger(__name__)
__a : Any = {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
),
}
class UpperCAmelCase( snake_case_ ):
"""simple docstring"""
a : Tuple = """longformer"""
def __init__( self , lowerCamelCase = 512 , lowerCamelCase = 2 , lowerCamelCase = 1 , lowerCamelCase = 0 , lowerCamelCase = 2 , lowerCamelCase = 30522 , lowerCamelCase = 768 , lowerCamelCase = 12 , lowerCamelCase = 12 , lowerCamelCase = 3072 , lowerCamelCase = "gelu" , lowerCamelCase = 0.1 , lowerCamelCase = 0.1 , lowerCamelCase = 512 , lowerCamelCase = 2 , lowerCamelCase = 0.02 , lowerCamelCase = 1E-12 , lowerCamelCase = False , **lowerCamelCase , ) -> List[Any]:
"""simple docstring"""
super().__init__(pad_token_id=lowerCamelCase , **lowerCamelCase )
lowercase__ : Dict = attention_window
lowercase__ : Optional[int] = sep_token_id
lowercase__ : List[Any] = bos_token_id
lowercase__ : List[str] = eos_token_id
lowercase__ : Union[str, Any] = vocab_size
lowercase__ : int = hidden_size
lowercase__ : Tuple = num_hidden_layers
lowercase__ : Tuple = num_attention_heads
lowercase__ : Optional[Any] = hidden_act
lowercase__ : int = intermediate_size
lowercase__ : Optional[int] = hidden_dropout_prob
lowercase__ : Optional[int] = attention_probs_dropout_prob
lowercase__ : Optional[Any] = max_position_embeddings
lowercase__ : Optional[int] = type_vocab_size
lowercase__ : Any = initializer_range
lowercase__ : Any = layer_norm_eps
lowercase__ : List[Any] = onnx_export
class UpperCAmelCase( snake_case_ ):
"""simple docstring"""
def __init__( self , lowerCamelCase , lowerCamelCase = "default" , lowerCamelCase = None ) -> Any:
"""simple docstring"""
super().__init__(lowerCamelCase , lowerCamelCase , lowerCamelCase )
lowercase__ : str = True
@property
def __a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ : List[str] = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowercase__ : Union[str, Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("global_attention_mask", dynamic_axis),
] )
@property
def __a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
lowercase__ : Optional[Any] = super().outputs
if self.task == "default":
lowercase__ : Optional[Any] = {0: "batch"}
return outputs
@property
def __a ( self ) -> float:
"""simple docstring"""
return 1E-4
@property
def __a ( self ) -> int:
"""simple docstring"""
return max(super().default_onnx_opset , 14 )
def __a ( self , lowerCamelCase , lowerCamelCase = -1 , lowerCamelCase = -1 , lowerCamelCase = False , lowerCamelCase = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowercase__ : int = super().generate_dummy_inputs(
preprocessor=lowerCamelCase , batch_size=lowerCamelCase , seq_length=lowerCamelCase , is_pair=lowerCamelCase , framework=lowerCamelCase )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
lowercase__ : Any = torch.zeros_like(inputs["input_ids"] )
# make every second token global
lowercase__ : List[Any] = 1
return inputs | 298 | 0 |
def _snake_case ( __snake_case , __snake_case ):
assert x is not None
assert y is not None
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = len(__snake_case )
# declaring the array for storing the dp values
_UpperCamelCase = [[0] * (n + 1) for _ in range(m + 1 )] # noqa: E741
for i in range(1 , m + 1 ):
for j in range(1 , n + 1 ):
_UpperCamelCase = 1 if x[i - 1] == y[j - 1] else 0
_UpperCamelCase = max(l[i - 1][j] , l[i][j - 1] , l[i - 1][j - 1] + match )
_UpperCamelCase = ''''''
_UpperCamelCase , _UpperCamelCase = m, n
while i > 0 and j > 0:
_UpperCamelCase = 1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
_UpperCamelCase = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
_lowerCAmelCase = "AGGTAB"
_lowerCAmelCase = "GXTXAYB"
_lowerCAmelCase = 4
_lowerCAmelCase = "GTAB"
_lowerCAmelCase, _lowerCAmelCase = longest_common_subsequence(a, b)
print("len =", ln, ", sub-sequence =", subseq)
import doctest
doctest.testmod()
| 10 | def _snake_case ( __snake_case = 100 ):
_UpperCamelCase = (n * (n + 1) // 2) ** 2
_UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 10 | 1 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
_lowerCamelCase : Any = logging.get_logger(__name__)
logging.set_verbosity_info()
def __lowerCamelCase ( A__ , A__ ) -> List[Any]:
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
UpperCamelCase = XLMProphetNetForConditionalGenerationOld.from_pretrained(A__ )
UpperCamelCase , UpperCamelCase = XLMProphetNetForConditionalGeneration.from_pretrained(
A__ , output_loading_info=A__ )
else:
UpperCamelCase = ProphetNetForConditionalGenerationOld.from_pretrained(A__ )
UpperCamelCase , UpperCamelCase = ProphetNetForConditionalGeneration.from_pretrained(
A__ , output_loading_info=A__ )
UpperCamelCase = ['key_proj', 'value_proj', 'query_proj']
UpperCamelCase = {
'self_attn': 'ngram_self_attn',
'cross_attn': 'encoder_attn',
'cross_attn_layer_norm': 'encoder_attn_layer_norm',
'feed_forward_layer_norm': 'final_layer_norm',
'feed_forward': '',
'intermediate': 'fc1',
'output': 'fc2',
'key_proj': 'k_proj',
'query_proj': 'q_proj',
'value_proj': 'v_proj',
'word_embeddings': 'embed_tokens',
'embeddings_layer_norm': 'emb_layer_norm',
'relative_pos_embeddings': 'relative_linear',
'ngram_embeddings': 'ngram_input_embed',
'position_embeddings': 'embed_positions',
}
for key in loading_info["missing_keys"]:
UpperCamelCase = key.split('.' )
if attributes[0] == "lm_head":
UpperCamelCase = prophet
UpperCamelCase = prophet_old
else:
UpperCamelCase = prophet.prophetnet
UpperCamelCase = prophet_old.model
UpperCamelCase = False
for attribute in attributes:
if attribute in mapping:
UpperCamelCase = mapping[attribute]
if not hasattr(A__ , A__ ) and len(A__ ) > 0:
UpperCamelCase = attribute
elif hasattr(A__ , A__ ):
UpperCamelCase = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
UpperCamelCase = old_model.weight
logger.info(F"""{attribute} is initialized.""" )
UpperCamelCase = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
UpperCamelCase = old_model.bias
logger.info(F"""{attribute} is initialized""" )
UpperCamelCase = True
break
elif attribute in special_keys and hasattr(A__ , 'in_proj_weight' ):
UpperCamelCase = old_model.in_proj_weight.shape[0] // 3
UpperCamelCase = getattr(A__ , A__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
UpperCamelCase = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
UpperCamelCase = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
UpperCamelCase = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
UpperCamelCase = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
UpperCamelCase = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
UpperCamelCase = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
UpperCamelCase = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
UpperCamelCase = nn.Parameter(old_model.embed_positions.weight[:512, :] )
UpperCamelCase = True
break
if attribute.isdigit():
UpperCamelCase = model[int(A__ )]
UpperCamelCase = old_model[int(A__ )]
else:
UpperCamelCase = getattr(A__ , A__ )
if old_attribute == "":
UpperCamelCase = old_model
else:
if not hasattr(A__ , A__ ):
raise ValueError(F"""{old_model} does not have {old_attribute}""" )
UpperCamelCase = getattr(A__ , A__ )
if not is_key_init:
raise ValueError(F"""{key} was not correctly initialized!""" )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
prophet.save_pretrained(A__ )
if __name__ == "__main__":
_lowerCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowerCamelCase : int = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 324 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any]=1_3 , UpperCamelCase__ : Optional[int]=7 , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Optional[int]=9_9 , UpperCamelCase__ : List[Any]=6_4 , UpperCamelCase__ : str=3_2 , UpperCamelCase__ : List[Any]=5 , UpperCamelCase__ : int=4 , UpperCamelCase__ : List[Any]=3_7 , UpperCamelCase__ : List[Any]="gelu" , UpperCamelCase__ : Optional[Any]=0.1 , UpperCamelCase__ : Optional[int]=0.1 , UpperCamelCase__ : Optional[int]=5_1_2 , UpperCamelCase__ : Optional[int]=1_6 , UpperCamelCase__ : str=2 , UpperCamelCase__ : List[str]=0.0_2 , UpperCamelCase__ : Optional[Any]=3 , UpperCamelCase__ : int=4 , UpperCamelCase__ : List[str]=None , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = embedding_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
def A ( self : int ):
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : Any ):
"""simple docstring"""
return MegatronBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , )
def A ( self : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = MegatronBertModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
UpperCamelCase = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
UpperCamelCase = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : Optional[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple ):
"""simple docstring"""
UpperCamelCase = MegatronBertForMaskedLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = MegatronBertForCausalLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : int , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : str ):
"""simple docstring"""
UpperCamelCase = MegatronBertForNextSentencePrediction(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any ):
"""simple docstring"""
UpperCamelCase = MegatronBertForPreTraining(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ , next_sentence_label=UpperCamelCase__ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Tuple , UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict ):
"""simple docstring"""
UpperCamelCase = MegatronBertForQuestionAnswering(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , start_positions=UpperCamelCase__ , end_positions=UpperCamelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = MegatronBertForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict , UpperCamelCase__ : int , UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple ):
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = MegatronBertForTokenClassification(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = self.num_choices
UpperCamelCase = MegatronBertForMultipleChoice(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : str ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( _a , _a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE = (
{
"""feature-extraction""": MegatronBertModel,
"""fill-mask""": MegatronBertForMaskedLM,
"""question-answering""": MegatronBertForQuestionAnswering,
"""text-classification""": MegatronBertForSequenceClassification,
"""text-generation""": MegatronBertForCausalLM,
"""token-classification""": MegatronBertForTokenClassification,
"""zero-shot""": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = True
# test_resize_embeddings = False
_SCREAMING_SNAKE_CASE = False
def A ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : str=False ):
"""simple docstring"""
UpperCamelCase = super()._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
if return_labels:
if model_class in get_values(UpperCamelCase__ ):
UpperCamelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=UpperCamelCase__ )
UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCamelCase__ )
return inputs_dict
def A ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = MegatronBertModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=3_7 )
def A ( self : List[str] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*UpperCamelCase__ )
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*UpperCamelCase__ )
def A ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*UpperCamelCase__ )
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*UpperCamelCase__ )
def A ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*UpperCamelCase__ )
def A ( self : str ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*UpperCamelCase__ )
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*UpperCamelCase__ )
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*UpperCamelCase__ )
def __lowerCamelCase ( A__ ) -> Union[str, Any]:
"""simple docstring"""
return torch.tensor(
A__ , dtype=torch.long , device=A__ , )
_lowerCamelCase : List[str] = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
@unittest.skip('Model is not available.' )
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = 'nvidia/megatron-bert-uncased-345m'
if "MYDIR" in os.environ:
UpperCamelCase = os.path.join(os.environ['MYDIR'] , UpperCamelCase__ )
UpperCamelCase = MegatronBertModel.from_pretrained(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.half()
UpperCamelCase = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] )
with torch.no_grad():
UpperCamelCase = model(UpperCamelCase__ )[0]
UpperCamelCase = torch.Size((1, 9, 1_0_2_4) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCamelCase = [-0.6_0_4_0, -0.2_5_1_7, -0.1_0_2_5, 0.3_4_2_0, -0.6_7_5_8, -0.0_0_1_7, -0.1_0_8_9, -0.1_9_9_0, 0.5_7_2_8]
for ii in range(3 ):
for jj in range(3 ):
UpperCamelCase = output[0, ii, jj]
UpperCamelCase = expected[3 * ii + jj]
UpperCamelCase = 'ii={} jj={} a={} b={}'.format(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.assertTrue(math.isclose(UpperCamelCase__ , UpperCamelCase__ , rel_tol=UpperCamelCase__ , abs_tol=UpperCamelCase__ ) , msg=UpperCamelCase__ )
| 324 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
__magic_name__ : Dict =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__magic_name__ : Union[str, Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(lowerCamelCase ) )
# The ratio of the area for circle to square is pi/4.
__magic_name__ : List[Any] =proportion * 4
print(F"The estimated value of pi is {pi_estimate}" )
print(F"The numpy value of pi is {pi}" )
print(F"The total error is {abs(pi - pi_estimate )}" )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ):
return mean(
function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ):
def identity_function(lowerCamelCase ) -> float:
return x
__magic_name__ : Optional[int] =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__magic_name__ : str =(max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {expected_value}" )
print(F"Total error is {abs(estimated_value - expected_value )}" )
print("""******************""" )
def lowerCAmelCase_ ( lowerCamelCase ):
def function_to_integrate(lowerCamelCase ) -> float:
return sqrt(4.0 - x * x )
__magic_name__ : Dict =area_under_curve_estimator(
lowerCamelCase , lowerCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F"Estimated value is {estimated_value}" )
print(F"Expected value is {pi}" )
print(F"Total error is {abs(estimated_value - pi )}" )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase: Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
lowerCAmelCase: Optional[int] = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCAmelCase: int = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class a__:
lowercase__ = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""} , )
lowercase__ = field(default=lowerCamelCase__ , metadata={"""help""": """A folder containing the training data."""} )
lowercase__ = field(default=lowerCamelCase__ , metadata={"""help""": """A folder containing the validation data."""} )
lowercase__ = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase__ = field(default=32 , metadata={"""help""": """The size of the square patches to use for masking."""} )
lowercase__ = field(
default=0.6 , metadata={"""help""": """Percentage of patches to mask."""} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def lowercase_ ( self : List[Any] ):
a : Any = {}
if self.train_dir is not None:
a : Dict = self.train_dir
if self.validation_dir is not None:
a : Union[str, Any] = self.validation_dir
a : Any = data_files if data_files else None
@dataclass
class a__:
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
"""checkpoint identifier on the hub. """
"""Don't set if you want to train a model from scratch."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCamelCase__ )} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""} , )
lowercase__ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase__ = field(default=lowerCamelCase__ , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """Stride to use for the encoder."""} , )
class a__:
def __init__( self : List[str] , __snake_case : int=1_92 , __snake_case : int=32 , __snake_case : List[str]=4 , __snake_case : Union[str, Any]=0.6 ):
a : Any = input_size
a : Union[str, Any] = mask_patch_size
a : int = model_patch_size
a : Tuple = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError('Input size must be divisible by mask patch size' )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError('Mask patch size must be divisible by model patch size' )
a : str = self.input_size // self.mask_patch_size
a : Union[str, Any] = self.mask_patch_size // self.model_patch_size
a : str = self.rand_size**2
a : Tuple = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self : str ):
a : List[str] = np.random.permutation(self.token_count )[: self.mask_count]
a : List[str] = np.zeros(self.token_count , dtype=__snake_case )
a : Any = 1
a : List[str] = mask.reshape((self.rand_size, self.rand_size) )
a : Tuple = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def lowerCamelCase__ ( _A ):
a : str = torch.stack([example['pixel_values'] for example in examples] )
a : List[str] = torch.stack([example['mask'] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def lowerCamelCase__ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
a : int = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
a , a , a : Dict = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
a , a , a : int = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mim' , _A , _A )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
a : int = training_args.get_process_log_level()
logger.setLevel(_A )
transformers.utils.logging.set_verbosity(_A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
a : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
a : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
a : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
a : Union[str, Any] = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _A ) and data_args.train_val_split > 0.0:
a : Any = ds['train'].train_test_split(data_args.train_val_split )
a : str = split['train']
a : Any = split['test']
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a : Tuple = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
a : Any = AutoConfig.from_pretrained(model_args.config_name_or_path , **_A )
elif model_args.model_name_or_path:
a : Optional[int] = AutoConfig.from_pretrained(model_args.model_name_or_path , **_A )
else:
a : Optional[int] = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(f"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(f"""New config: {config}""" )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(_A , 'decoder_type' ):
a : List[str] = 'simmim'
# adapt config
a : str = model_args.image_size if model_args.image_size is not None else config.image_size
a : Union[str, Any] = model_args.patch_size if model_args.patch_size is not None else config.patch_size
a : Any = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
'image_size': model_args.image_size,
'patch_size': model_args.patch_size,
'encoder_stride': model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
a : Union[str, Any] = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **_A )
elif model_args.model_name_or_path:
a : Union[str, Any] = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **_A )
else:
a : List[Any] = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
a : int = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
a : Tuple = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
a : Union[str, Any] = AutoModelForMaskedImageModeling.from_config(_A )
if training_args.do_train:
a : Tuple = ds['train'].column_names
else:
a : Dict = ds['validation'].column_names
if data_args.image_column_name is not None:
a : Optional[Any] = data_args.image_column_name
elif "image" in column_names:
a : str = 'image'
elif "img" in column_names:
a : Union[str, Any] = 'img'
else:
a : str = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
a : Optional[int] = Compose(
[
Lambda(lambda _A : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
a : Dict = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(_A ):
a : Dict = [transforms(_A ) for image in examples[image_column_name]]
a : Optional[int] = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
a : List[Any] = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_A )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
a : str = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_A )
# Initialize our trainer
a : List[str] = Trainer(
model=_A , args=_A , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_A , data_collator=_A , )
# Training
if training_args.do_train:
a : str = None
if training_args.resume_from_checkpoint is not None:
a : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
a : Dict = last_checkpoint
a : Optional[int] = trainer.train(resume_from_checkpoint=_A )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
a : List[Any] = trainer.evaluate()
trainer.log_metrics('eval' , _A )
trainer.save_metrics('eval' , _A )
# Write model card and (optionally) push to hub
a : str = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'masked-image-modeling',
'dataset': data_args.dataset_name,
'tags': ['masked-image-modeling'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_A )
else:
trainer.create_model_card(**_A )
if __name__ == "__main__":
main() | 526 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
UpperCAmelCase : str =logging.get_logger(__name__)
class _lowercase (SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
lowercase__ = ["""pixel_values"""]
def __init__( self , snake_case__ = True , snake_case__ = None , snake_case__ = None , snake_case__ = PILImageResampling.BILINEAR , snake_case__ = True , snake_case__ = 1 / 255 , snake_case__ = True , snake_case__ = None , snake_case__ = None , **snake_case__ , ):
'''simple docstring'''
super().__init__(**_lowercase )
UpperCamelCase_ = size if size is not None else {"""shortest_edge""": 384}
UpperCamelCase_ = get_size_dict(_lowercase , default_to_square=_lowercase )
UpperCamelCase_ = do_resize
UpperCamelCase_ = size
# Default value set here for backwards compatibility where the value in config is None
UpperCamelCase_ = crop_pct if crop_pct is not None else 224 / 256
UpperCamelCase_ = resample
UpperCamelCase_ = do_rescale
UpperCamelCase_ = rescale_factor
UpperCamelCase_ = do_normalize
UpperCamelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ = PILImageResampling.BICUBIC , snake_case__ = None , **snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(F"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}""" )
UpperCamelCase_ = size["""shortest_edge"""]
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
UpperCamelCase_ = int(shortest_edge / crop_pct )
UpperCamelCase_ = get_resize_output_image_size(_lowercase , size=_lowercase , default_to_square=_lowercase )
UpperCamelCase_ = resize(image=_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_lowercase , size=(shortest_edge, shortest_edge) , data_format=_lowercase , **_lowercase )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_lowercase , size=(shortest_edge, shortest_edge) , resample=_lowercase , data_format=_lowercase , **_lowercase )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ = None , **snake_case__ , ):
'''simple docstring'''
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , **snake_case__ , ):
'''simple docstring'''
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def _lowerCamelCase ( self , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = ChannelDimension.FIRST , **snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCamelCase_ = crop_pct if crop_pct is not None else self.crop_pct
UpperCamelCase_ = resample if resample is not None else self.resample
UpperCamelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCamelCase_ = image_std if image_std is not None else self.image_std
UpperCamelCase_ = size if size is not None else self.size
UpperCamelCase_ = get_size_dict(_lowercase , default_to_square=_lowercase )
UpperCamelCase_ = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError("crop_pct must be specified if size < 384." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
UpperCamelCase_ = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
UpperCamelCase_ = [self.resize(image=_lowercase , size=_lowercase , crop_pct=_lowercase , resample=_lowercase ) for image in images]
if do_rescale:
UpperCamelCase_ = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
UpperCamelCase_ = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
UpperCamelCase_ = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
UpperCamelCase_ = {"""pixel_values""": images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 721 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def _lowerCAmelCase (_lowerCAmelCase):
if not isinstance(_lowerCAmelCase , _lowerCAmelCase):
raise TypeError("Undefined for non-integers")
elif precision < 1:
raise ValueError("Undefined for non-natural numbers")
UpperCamelCase_ = precision
UpperCamelCase_ = ceil(precision / 14)
UpperCamelCase_ = 42_68_80 * Decimal(1_00_05).sqrt()
UpperCamelCase_ = 1
UpperCamelCase_ = 13_59_14_09
UpperCamelCase_ = Decimal(_lowerCAmelCase)
for k in range(1 , _lowerCAmelCase):
UpperCamelCase_ = factorial(6 * k) // (factorial(3 * k) * factorial(_lowerCAmelCase) ** 3)
linear_term += 5_45_14_01_34
exponential_term *= -26_25_37_41_26_40_76_80_00
partial_sum += Decimal(multinomial_term * linear_term) / exponential_term
return str(constant_term / partial_sum)[:-1]
if __name__ == "__main__":
UpperCAmelCase : Any =50
print(F"The first {n} digits of pi is: {pi(n)}")
| 504 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase__ ( _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
A__ : str = GPTaTokenizer
A__ : int = GPTaTokenizerFast
A__ : Optional[int] = True
A__ : List[str] = {"add_prefix_space": True}
A__ : List[str] = False
def snake_case__ ( self ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A__ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
A__ = dict(zip(SCREAMING_SNAKE_CASE__ , range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
A__ = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
A__ = {"unk_token": "<unk>"}
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(SCREAMING_SNAKE_CASE__ ) )
def snake_case__ ( self , **SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self , **SCREAMING_SNAKE_CASE__ ) -> Dict:
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
A__ = "lower newer"
A__ = "lower newer"
return input_text, output_text
def snake_case__ ( self ) -> Any:
A__ = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
A__ = "lower newer"
A__ = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
A__ = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A__ = tokens + [tokenizer.unk_token]
A__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self ) -> Union[str, Any]:
if not self.test_rust_tokenizer:
return
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__ )
A__ = "lower newer"
# Testing tokenization
A__ = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ )
A__ = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Testing conversion to ids without special tokens
A__ = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ )
A__ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Testing conversion to ids with special tokens
A__ = self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__ )
A__ = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ )
A__ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Testing the unknown token
A__ = tokens + [rust_tokenizer.unk_token]
A__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def snake_case__ ( self , SCREAMING_SNAKE_CASE__=15 ) -> Optional[int]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
A__ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
# Simple input
A__ = "This is a simple input"
A__ = ["This is a simple input 1", "This is a simple input 2"]
A__ = ("This is a simple input", "This is a pair")
A__ = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(SCREAMING_SNAKE_CASE__ , tokenizer_r.encode , SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding="max_length" )
# Simple input
self.assertRaises(SCREAMING_SNAKE_CASE__ , tokenizer_r.encode_plus , SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding="max_length" )
# Simple input
self.assertRaises(
SCREAMING_SNAKE_CASE__ , tokenizer_r.batch_encode_plus , SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding="max_length" , )
# Pair input
self.assertRaises(SCREAMING_SNAKE_CASE__ , tokenizer_r.encode , SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding="max_length" )
# Pair input
self.assertRaises(SCREAMING_SNAKE_CASE__ , tokenizer_r.encode_plus , SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding="max_length" )
# Pair input
self.assertRaises(
SCREAMING_SNAKE_CASE__ , tokenizer_r.batch_encode_plus , SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding="max_length" , )
def snake_case__ ( self ) -> Optional[int]:
A__ = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
A__ = "This is a simple input"
A__ = ["This is a simple input looooooooong", "This is a simple input"]
A__ = ("This is a simple input", "This is a pair")
A__ = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
A__ = tokenizer.pad_token_id
A__ = tokenizer(SCREAMING_SNAKE_CASE__ , padding="max_length" , max_length=30 , return_tensors="np" )
A__ = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncate=SCREAMING_SNAKE_CASE__ , return_tensors="np" )
A__ = tokenizer(*SCREAMING_SNAKE_CASE__ , padding="max_length" , max_length=60 , return_tensors="np" )
A__ = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncate=SCREAMING_SNAKE_CASE__ , return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def snake_case__ ( self ) -> Optional[Any]:
A__ = "$$$"
A__ = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=SCREAMING_SNAKE_CASE__ , add_bos_token=SCREAMING_SNAKE_CASE__ )
A__ = "This is a simple input"
A__ = ["This is a simple input 1", "This is a simple input 2"]
A__ = tokenizer.bos_token_id
A__ = tokenizer(SCREAMING_SNAKE_CASE__ )
A__ = tokenizer(SCREAMING_SNAKE_CASE__ )
self.assertEqual(out_s.input_ids[0] , SCREAMING_SNAKE_CASE__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
A__ = tokenizer.decode(out_s.input_ids )
A__ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , SCREAMING_SNAKE_CASE__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def snake_case__ ( self ) -> Any:
pass
def snake_case__ ( self ) -> Optional[int]:
# TODO: change to self.get_tokenizers() when the fast version is implemented
A__ = [self.get_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , add_bos_token=SCREAMING_SNAKE_CASE__ )]
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
A__ = "Encode this."
A__ = "This one too please."
A__ = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
encoded_sequence += tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
A__ = tokenizer.encode_plus(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_special_tokens_mask=SCREAMING_SNAKE_CASE__ , )
A__ = encoded_sequence_dict["input_ids"]
A__ = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , len(SCREAMING_SNAKE_CASE__ ) )
A__ = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(SCREAMING_SNAKE_CASE__ )
]
A__ = [x for x in filtered_sequence if x is not None]
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self ) -> List[str]:
# More context:
# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439
A__ = AutoTokenizer.from_pretrained("facebook/opt-350m" , from_slow=SCREAMING_SNAKE_CASE__ )
A__ = "A photo of a cat"
A__ = tokenizer.encode(
SCREAMING_SNAKE_CASE__ , )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [2, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained("test_opt" )
A__ = AutoTokenizer.from_pretrained("./test_opt" )
A__ = tokenizer.encode(
SCREAMING_SNAKE_CASE__ , )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [2, 250, 1345, 9, 10, 4758] )
def snake_case__ ( self ) -> List[str]:
A__ = AutoTokenizer.from_pretrained("facebook/opt-350m" , use_slow=SCREAMING_SNAKE_CASE__ )
A__ = "A photo of a cat"
A__ = tokenizer.encode(
SCREAMING_SNAKE_CASE__ , )
# Same as above
self.assertEqual(SCREAMING_SNAKE_CASE__ , [2, 250, 1345, 9, 10, 4758] )
@unittest.skip("This test is failing because of a bug in the fast tokenizer" )
def snake_case__ ( self ) -> List[Any]:
A__ = AutoTokenizer.from_pretrained("facebook/opt-350m" , from_slow=SCREAMING_SNAKE_CASE__ )
A__ = "bos"
A__ = tokenizer.get_vocab()["bos"]
A__ = "A photo of a cat"
A__ = tokenizer.encode(
SCREAMING_SNAKE_CASE__ , )
# We changed the bos token
self.assertEqual(SCREAMING_SNAKE_CASE__ , [31957, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained("./tok" )
A__ = AutoTokenizer.from_pretrained("./tok" )
self.assertTrue(tokenizer.is_fast )
A__ = tokenizer.encode(
SCREAMING_SNAKE_CASE__ , )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [31957, 250, 1345, 9, 10, 4758] )
| 104 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
lowerCAmelCase__ :Optional[Any] = get_tests_dir('''fixtures''')
class __a ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 500
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=_SCREAMING_SNAKE_CASE ) as mock_head:
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json' )
@is_staging_test
class __a ( unittest.TestCase ):
@classmethod
def UpperCAmelCase__ ( cls ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = TOKEN
HfFolder.save_token(_SCREAMING_SNAKE_CASE )
@classmethod
def UpperCAmelCase__ ( cls ) -> Tuple:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='test-feature-extractor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-feature-extractor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-feature-extractor' )
except HTTPError:
pass
def UpperCAmelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE )
feature_extractor.push_to_hub('test-feature-extractor' , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
_SCREAMING_SNAKE_CASE , repo_id='test-feature-extractor' , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE )
feature_extractor.push_to_hub('valid_org/test-feature-extractor' , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
_SCREAMING_SNAKE_CASE , repo_id='valid_org/test-feature-extractor-org' , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
CustomFeatureExtractor.register_for_auto_class()
_UpperCAmelCase = CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE )
feature_extractor.push_to_hub('test-dynamic-feature-extractor' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'} , )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
f'''{USER}/test-dynamic-feature-extractor''' , trust_remote_code=_SCREAMING_SNAKE_CASE )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , 'CustomFeatureExtractor' )
| 618 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : str = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlenderbotConfig""",
"""BlenderbotOnnxConfig""",
],
"""tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Any = ["""BlenderbotTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Any = [
"""BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlenderbotForCausalLM""",
"""BlenderbotForConditionalGeneration""",
"""BlenderbotModel""",
"""BlenderbotPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : List[Any] = [
"""TFBlenderbotForConditionalGeneration""",
"""TFBlenderbotModel""",
"""TFBlenderbotPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Any = [
"""FlaxBlenderbotForConditionalGeneration""",
"""FlaxBlenderbotModel""",
"""FlaxBlenderbotPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 425 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
("""input_blocks.0.0.weight""", """conv_in.weight"""),
("""input_blocks.0.0.bias""", """conv_in.bias"""),
("""out.0.weight""", """conv_norm_out.weight"""),
("""out.0.bias""", """conv_norm_out.bias"""),
("""out.2.weight""", """conv_out.weight"""),
("""out.2.bias""", """conv_out.bias"""),
]
lowerCAmelCase : Any = [
# (stable-diffusion, HF Diffusers)
("""in_layers.0""", """norm1"""),
("""in_layers.2""", """conv1"""),
("""out_layers.0""", """norm2"""),
("""out_layers.3""", """conv2"""),
("""emb_layers.1""", """time_emb_proj"""),
("""skip_connection""", """conv_shortcut"""),
]
lowerCAmelCase : Dict = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
lowerCAmelCase : Any = F'''down_blocks.{i}.resnets.{j}.'''
lowerCAmelCase : int = F'''input_blocks.{3*i + j + 1}.0.'''
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
lowerCAmelCase : Dict = F'''down_blocks.{i}.attentions.{j}.'''
lowerCAmelCase : Tuple = F'''input_blocks.{3*i + j + 1}.1.'''
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
lowerCAmelCase : str = F'''up_blocks.{i}.resnets.{j}.'''
lowerCAmelCase : List[str] = F'''output_blocks.{3*i + j}.0.'''
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
lowerCAmelCase : Optional[Any] = F'''up_blocks.{i}.attentions.{j}.'''
lowerCAmelCase : Any = F'''output_blocks.{3*i + j}.1.'''
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
lowerCAmelCase : Optional[int] = F'''down_blocks.{i}.downsamplers.0.conv.'''
lowerCAmelCase : List[str] = F'''input_blocks.{3*(i+1)}.0.op.'''
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
lowerCAmelCase : Union[str, Any] = F'''up_blocks.{i}.upsamplers.0.'''
lowerCAmelCase : Optional[Any] = F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
lowerCAmelCase : Optional[Any] = """mid_block.attentions.0."""
lowerCAmelCase : Optional[int] = """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
lowerCAmelCase : str = F'''mid_block.resnets.{j}.'''
lowerCAmelCase : int = F'''middle_block.{2*j}.'''
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def _A ( A ) -> str:
# buyer beware: this is a *brittle* function,
# and correct output requires that all of these pieces interact in
# the exact order in which I have arranged them.
lowercase : List[Any] = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
lowercase : Any = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
lowercase : Optional[Any] = v.replace(A ,A )
lowercase : List[Any] = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
lowercase : Any = v.replace(A ,A )
lowercase : Tuple = v
lowercase : Any = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
lowerCAmelCase : Optional[Any] = [
# (stable-diffusion, HF Diffusers)
("""nin_shortcut""", """conv_shortcut"""),
("""norm_out""", """conv_norm_out"""),
("""mid.attn_1.""", """mid_block.attentions.0."""),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
lowerCAmelCase : List[Any] = F'''encoder.down_blocks.{i}.resnets.{j}.'''
lowerCAmelCase : Optional[Any] = F'''encoder.down.{i}.block.{j}.'''
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
lowerCAmelCase : Tuple = F'''down_blocks.{i}.downsamplers.0.'''
lowerCAmelCase : Any = F'''down.{i}.downsample.'''
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
lowerCAmelCase : List[Any] = F'''up_blocks.{i}.upsamplers.0.'''
lowerCAmelCase : int = F'''up.{3-i}.upsample.'''
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
lowerCAmelCase : int = F'''decoder.up_blocks.{i}.resnets.{j}.'''
lowerCAmelCase : List[str] = F'''decoder.up.{3-i}.block.{j}.'''
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
lowerCAmelCase : Any = F'''mid_block.resnets.{i}.'''
lowerCAmelCase : Optional[int] = F'''mid.block_{i+1}.'''
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
lowerCAmelCase : int = [
# (stable-diffusion, HF Diffusers)
("""norm.""", """group_norm."""),
("""q.""", """query."""),
("""k.""", """key."""),
("""v.""", """value."""),
("""proj_out.""", """proj_attn."""),
]
def _A ( A ) -> Optional[Any]:
# convert HF linear weights to SD conv2d weights
return w.reshape(*w.shape ,1 ,1 )
def _A ( A ) -> List[str]:
lowercase : Tuple = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
lowercase : Union[str, Any] = v.replace(A ,A )
lowercase : Optional[Any] = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
lowercase : str = v.replace(A ,A )
lowercase : Dict = v
lowercase : int = {v: vae_state_dict[k] for k, v in mapping.items()}
lowercase : Tuple = ["q", "k", "v", "proj_out"]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F'''mid.attn_1.{weight_name}.weight''' in k:
print(F'''Reshaping {k} for SD format''' )
lowercase : List[Any] = reshape_weight_for_sd(A )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
lowerCAmelCase : Any = [
# (stable-diffusion, HF Diffusers)
("""resblocks.""", """text_model.encoder.layers."""),
("""ln_1""", """layer_norm1"""),
("""ln_2""", """layer_norm2"""),
(""".c_fc.""", """.fc1."""),
(""".c_proj.""", """.fc2."""),
(""".attn""", """.self_attn"""),
("""ln_final.""", """transformer.text_model.final_layer_norm."""),
("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
lowerCAmelCase : int = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase : List[Any] = re.compile("""|""".join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase : Optional[Any] = {"""q""": 0, """k""": 1, """v""": 2}
def _A ( A ) -> List[Any]:
lowercase : List[Any] = {}
lowercase : Optional[Any] = {}
lowercase : Optional[Any] = {}
for k, v in text_enc_dict.items():
if (
k.endswith(".self_attn.q_proj.weight" )
or k.endswith(".self_attn.k_proj.weight" )
or k.endswith(".self_attn.v_proj.weight" )
):
lowercase : int = k[: -len(".q_proj.weight" )]
lowercase : List[str] = k[-len("q_proj.weight" )]
if k_pre not in capture_qkv_weight:
lowercase : Tuple = [None, None, None]
lowercase : List[str] = v
continue
if (
k.endswith(".self_attn.q_proj.bias" )
or k.endswith(".self_attn.k_proj.bias" )
or k.endswith(".self_attn.v_proj.bias" )
):
lowercase : int = k[: -len(".q_proj.bias" )]
lowercase : str = k[-len("q_proj.bias" )]
if k_pre not in capture_qkv_bias:
lowercase : Any = [None, None, None]
lowercase : Tuple = v
continue
lowercase : str = textenc_pattern.sub(lambda A : protected[re.escape(m.group(0 ) )] ,A )
lowercase : Any = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
lowercase : List[str] = textenc_pattern.sub(lambda A : protected[re.escape(m.group(0 ) )] ,A )
lowercase : List[Any] = torch.cat(A )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
lowercase : Tuple = textenc_pattern.sub(lambda A : protected[re.escape(m.group(0 ) )] ,A )
lowercase : int = torch.cat(A )
return new_state_dict
def _A ( A ) -> Union[str, Any]:
return text_enc_dict
if __name__ == "__main__":
lowerCAmelCase : Dict = argparse.ArgumentParser()
parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
parser.add_argument(
"""--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
)
lowerCAmelCase : List[str] = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
lowerCAmelCase : List[str] = osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
lowerCAmelCase : List[Any] = osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
lowerCAmelCase : Optional[Any] = osp.join(args.model_path, """text_encoder""", """model.safetensors""")
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
lowerCAmelCase : str = load_file(unet_path, device="""cpu""")
else:
lowerCAmelCase : List[str] = osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
lowerCAmelCase : Any = torch.load(unet_path, map_location="""cpu""")
if osp.exists(vae_path):
lowerCAmelCase : Dict = load_file(vae_path, device="""cpu""")
else:
lowerCAmelCase : Optional[int] = osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
lowerCAmelCase : Dict = torch.load(vae_path, map_location="""cpu""")
if osp.exists(text_enc_path):
lowerCAmelCase : List[Any] = load_file(text_enc_path, device="""cpu""")
else:
lowerCAmelCase : Optional[Any] = osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
lowerCAmelCase : Optional[Any] = torch.load(text_enc_path, map_location="""cpu""")
# Convert the UNet model
lowerCAmelCase : Any = convert_unet_state_dict(unet_state_dict)
lowerCAmelCase : Union[str, Any] = {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
lowerCAmelCase : Any = convert_vae_state_dict(vae_state_dict)
lowerCAmelCase : Any = {"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
lowerCAmelCase : Optional[Any] = """text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
lowerCAmelCase : Optional[Any] = {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
lowerCAmelCase : List[str] = convert_text_enc_state_dict_vaa(text_enc_dict)
lowerCAmelCase : Any = {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
else:
lowerCAmelCase : str = convert_text_enc_state_dict(text_enc_dict)
lowerCAmelCase : Optional[Any] = {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
lowerCAmelCase : List[str] = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
lowerCAmelCase : Dict = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
lowerCAmelCase : Any = {"""state_dict""": state_dict}
torch.save(state_dict, args.checkpoint_path)
| 425 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __SCREAMING_SNAKE_CASE (__A ):
"""simple docstring"""
_a : int = '''megatron-bert'''
def __init__( self , UpperCamelCase__=29_056 , UpperCamelCase__=1_024 , UpperCamelCase__=24 , UpperCamelCase__=16 , UpperCamelCase__=4_096 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-12 , UpperCamelCase__=0 , UpperCamelCase__="absolute" , UpperCamelCase__=True , **UpperCamelCase__ , ):
"""simple docstring"""
super().__init__(pad_token_id=UpperCamelCase__ , **UpperCamelCase__ )
a_ = vocab_size
a_ = hidden_size
a_ = num_hidden_layers
a_ = num_attention_heads
a_ = hidden_act
a_ = intermediate_size
a_ = hidden_dropout_prob
a_ = attention_probs_dropout_prob
a_ = max_position_embeddings
a_ = type_vocab_size
a_ = initializer_range
a_ = layer_norm_eps
a_ = position_embedding_type
a_ = use_cache
| 536 |
'''simple docstring'''
import os
import sys
import unittest
__lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
__lowerCAmelCase = os.path.join("tests", "models", "bert", "test_modeling_bert.py")
__lowerCAmelCase = os.path.join("tests", "models", "blip", "test_modeling_blip.py")
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def _a ( self ):
"""simple docstring"""
a_ = get_test_to_tester_mapping(UpperCamelCase__ )
a_ = get_test_to_tester_mapping(UpperCamelCase__ )
a_ = {'BertModelTest': 'BertModelTester'}
a_ = {
'BlipModelTest': 'BlipModelTester',
'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
'BlipTextModelTest': 'BlipTextModelTester',
'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
'BlipVQAModelTest': 'BlipVQAModelTester',
'BlipVisionModelTest': 'BlipVisionModelTester',
}
self.assertEqual(get_test_info.to_json(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(get_test_info.to_json(UpperCamelCase__ ) , UpperCamelCase__ )
def _a ( self ):
"""simple docstring"""
a_ = get_model_to_test_mapping(UpperCamelCase__ )
a_ = get_model_to_test_mapping(UpperCamelCase__ )
a_ = {
'BertForMaskedLM': ['BertModelTest'],
'BertForMultipleChoice': ['BertModelTest'],
'BertForNextSentencePrediction': ['BertModelTest'],
'BertForPreTraining': ['BertModelTest'],
'BertForQuestionAnswering': ['BertModelTest'],
'BertForSequenceClassification': ['BertModelTest'],
'BertForTokenClassification': ['BertModelTest'],
'BertLMHeadModel': ['BertModelTest'],
'BertModel': ['BertModelTest'],
}
a_ = {
'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
'BlipForQuestionAnswering': ['BlipVQAModelTest'],
'BlipModel': ['BlipModelTest'],
'BlipTextModel': ['BlipTextModelTest'],
'BlipVisionModel': ['BlipVisionModelTest'],
}
self.assertEqual(get_test_info.to_json(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(get_test_info.to_json(UpperCamelCase__ ) , UpperCamelCase__ )
def _a ( self ):
"""simple docstring"""
a_ = get_model_to_tester_mapping(UpperCamelCase__ )
a_ = get_model_to_tester_mapping(UpperCamelCase__ )
a_ = {
'BertForMaskedLM': ['BertModelTester'],
'BertForMultipleChoice': ['BertModelTester'],
'BertForNextSentencePrediction': ['BertModelTester'],
'BertForPreTraining': ['BertModelTester'],
'BertForQuestionAnswering': ['BertModelTester'],
'BertForSequenceClassification': ['BertModelTester'],
'BertForTokenClassification': ['BertModelTester'],
'BertLMHeadModel': ['BertModelTester'],
'BertModel': ['BertModelTester'],
}
a_ = {
'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
'BlipForQuestionAnswering': ['BlipVQAModelTester'],
'BlipModel': ['BlipModelTester'],
'BlipTextModel': ['BlipTextModelTester'],
'BlipVisionModel': ['BlipVisionModelTester'],
}
self.assertEqual(get_test_info.to_json(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(get_test_info.to_json(UpperCamelCase__ ) , UpperCamelCase__ )
| 536 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def UpperCamelCase_ ( *__a ) -> Optional[int]:
if not isinstance(__a , __a ):
a__ : int = list(__a )
for i in range(len(__a ) ):
a__ : Any = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def UpperCamelCase_ ( __a ) -> bool:
a__ : Tuple = [
"CUDA out of memory.", # CUDA OOM
"cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.", # CUDNN SNAFU
"DefaultCPUAllocator: can't allocate memory", # CPU OOM
]
if isinstance(__a , __a ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def UpperCamelCase_ ( __a = None , __a = 128 ) -> List[Any]:
if function is None:
return functools.partial(__a , starting_batch_size=__a )
a__ : Union[str, Any] = starting_batch_size
def decorator(*__a , **__a ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
a__ : int = list(inspect.signature(__a ).parameters.keys() )
# Guard against user error
if len(__a ) < (len(__a ) + 1):
a__ : Optional[Any] = ", ".join([f'''{arg}={value}''' for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''' )
while True:
if batch_size == 0:
raise RuntimeError("No executable batch size found, reached zero." )
try:
return function(__a , *__a , **__a )
except Exception as e:
if should_reduce_batch_size(__a ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 713 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
UpperCamelCase : Optional[Any] = logging.getLogger(__name__)
UpperCamelCase : Any = """pytorch_model.bin"""
@dataclasses.dataclass
class A__ :
"""simple docstring"""
_lowercase = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
_lowercase = dataclasses.field(
default=A__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class A__ :
"""simple docstring"""
_lowercase = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
_lowercase = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
_lowercase = dataclasses.field(
default=A__ , metadata={'help': 'A csv or a json file containing the validation data.'} )
_lowercase = dataclasses.field(
default=A__ , metadata={'help': 'The name of the task to train on.'} , )
_lowercase = dataclasses.field(
default=A__ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class A__ :
"""simple docstring"""
_lowercase = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
_lowercase = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
_lowercase = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
} , )
_lowercase = dataclasses.field(
default=1_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
_lowercase = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
_lowercase = dataclasses.field(
default=A__ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
_lowercase = dataclasses.field(
default=A__ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
_lowercase = dataclasses.field(
default=A__ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
_lowercase = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
_lowercase = dataclasses.field(
default=1_0_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
_lowercase = dataclasses.field(
default=A__ , metadata={'help': 'Random seed for initialization.'} , )
def UpperCamelCase_ ( __a , __a , __a , __a , __a , __a ) -> Optional[Any]:
a__ : Any = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
a__ : str = dataset.filter(lambda __a : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
a__ : Tuple = int(eval_result * len(__a ) )
print(__a )
a__ : Optional[Any] = dataset.sort("probability" , reverse=__a )
a__ : Optional[int] = dataset.select(range(__a ) )
a__ : List[str] = dataset.remove_columns(["label", "probability"] )
a__ : Union[str, Any] = dataset.rename_column("prediction" , "label" )
a__ : int = dataset.map(lambda __a : {"label": idalabel[example["label"]]} )
a__ : Optional[int] = dataset.shuffle(seed=args.seed )
a__ : str = os.path.join(__a , f'''train_pseudo.{args.data_file_extension}''' )
if args.data_file_extension == "csv":
dataset.to_csv(__a , index=__a )
else:
dataset.to_json(__a )
def UpperCamelCase_ ( __a , __a , __a , __a , **__a ) -> Dict:
a__ : List[str] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
a__ : int = STModelArguments(model_name_or_path=__a )
a__ : Optional[int] = STDataArguments(train_file=__a , infer_file=__a )
a__ : List[Any] = STTrainingArguments(output_dir=__a )
a__ : Union[str, Any] = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(__a ).items():
setattr(__a , __a , __a )
for key, value in kwargs.items():
if hasattr(__a , __a ):
setattr(__a , __a , __a )
# Sanity checks
a__ : List[Any] = {}
a__ : Optional[Any] = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
a__ : Union[str, Any] = args.train_file
a__ : List[str] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
a__ : Tuple = args.eval_file
for key in data_files:
a__ : Optional[Any] = data_files[key].split("." )[-1]
assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
if args.data_file_extension is None:
a__ : List[Any] = extension
else:
assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''
assert (
args.eval_metric in datasets.list_metrics()
), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("Creating the initial data directory for self-training..." )
a__ : Any = f'''{args.output_dir}/self-train_iter-{{}}'''.format
a__ : List[str] = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=__a )
os.makedirs(__a , exist_ok=__a )
accelerator.wait_for_everyone()
a__ : Optional[int] = None
a__ : str = None
a__ : List[Any] = 0
a__ : List[Any] = False
# Show the progress bar
a__ : Any = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
a__ : Optional[int] = data_dir_format(__a )
assert os.path.exists(__a )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
a__ : Union[str, Any] = os.path.join(__a , "stage-1" )
a__ : str = {
"accelerator": accelerator,
"model_name_or_path": args.model_name_or_path,
"cache_dir": args.cache_dir,
"do_train": True,
"train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
"do_eval": True if args.eval_file is not None else False,
"eval_file": data_files["eval"],
"do_predict": True,
"infer_file": data_files["infer"],
"task_name": args.task_name,
"label_list": args.label_list,
"output_dir": current_output_dir,
"eval_metric": args.eval_metric,
"evaluation_strategy": args.evaluation_strategy,
"early_stopping_patience": args.early_stopping_patience,
"early_stopping_threshold": args.early_stopping_threshold,
"seed": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(__a , __a ):
arguments_dict.update({key: value} )
a__ : Tuple = os.path.join(__a , "best-checkpoint" , __a )
if os.path.exists(__a ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1." , __a , __a , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 1 *****" , __a )
finetune(**__a )
accelerator.wait_for_everyone()
assert os.path.exists(__a )
logger.info("Self-training job completed: iteration: %d, stage: 1." , __a )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
a__ : Any = os.path.join(__a , "best-checkpoint" )
a__ : Optional[int] = os.path.join(__a , "stage-2" )
# Update arguments_dict
a__ : Union[str, Any] = model_path
a__ : Union[str, Any] = data_files["train"]
a__ : Optional[Any] = current_output_dir
a__ : Optional[int] = os.path.join(__a , "best-checkpoint" , __a )
if os.path.exists(__a ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2." , __a , __a , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 2 *****" , __a )
finetune(**__a )
accelerator.wait_for_everyone()
assert os.path.exists(__a )
logger.info("Self-training job completed: iteration: %d, stage: 2." , __a )
a__ : Dict = iteration
a__ : List[str] = data_dir_format(iteration + 1 )
a__ : Union[str, Any] = AutoConfig.from_pretrained(os.path.join(__a , "best-checkpoint" ) )
a__ : str = config.idalabel
a__ : Union[str, Any] = os.path.join(__a , "eval_results_best-checkpoint.json" )
a__ : Dict = os.path.join(__a , "test_results_best-checkpoint.json" )
assert os.path.exists(__a )
with open(__a , "r" ) as f:
a__ : Optional[int] = float(json.load(__a )[args.eval_metric] )
a__ : Union[str, Any] = os.path.join(__a , "infer_output_best-checkpoint.csv" )
assert os.path.exists(__a )
# Loading the dataset from local csv or json files.
a__ : List[Any] = load_dataset(args.data_file_extension , data_files={"data": data_files["infer"]} )["data"]
a__ : Dict = load_dataset("csv" , data_files={"data": infer_output_file} )["data"]
if accelerator.is_main_process:
os.makedirs(__a , exist_ok=__a )
shutil.copy(__a , os.path.join(__a , f'''eval_results_iter-{iteration}.json''' ) )
if os.path.exists(__a ):
shutil.copy(__a , os.path.join(__a , f'''test_results_iter-{iteration}.json''' ) )
create_pseudo_labeled_data(__a , __a , __a , __a , __a , __a )
accelerator.wait_for_everyone()
a__ : Optional[int] = os.path.join(__a , f'''train_pseudo.{args.data_file_extension}''' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
a__ : str = eval_result
if best_iteration is None:
a__ : Union[str, Any] = new_iteration
a__ : Dict = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
a__ : List[str] = new_iteration
a__ : List[Any] = new_eval_result
a__ : Dict = 0
else:
if new_eval_result == best_eval_result:
a__ : Optional[int] = new_iteration
a__ : Any = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
a__ : Dict = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("Best iteration: %d" , __a )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , __a )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__a , f'''eval_results_iter-{iteration}.json''' ) , os.path.join(__a , "eval_results_best-iteration.json" ) , )
else:
# Assume that the last iteration is the best
logger.info("Best iteration: %d" , args.max_selftrain_iterations - 1 )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , __a )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__a , f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json''' ) , os.path.join(__a , "eval_results_best-iteration.json" ) , )
| 151 | 0 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
UpperCAmelCase = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
print("""Googling.....""")
UpperCAmelCase = f'''https://www.google.com/search?q={query}&num=100'''
UpperCAmelCase = requests.get(
url,
headers={"""User-Agent""": str(UserAgent().random)},
)
try:
UpperCAmelCase = (
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """yuRUbf"""})
.find("""a""")
.get("""href""")
)
except AttributeError:
UpperCAmelCase = parse_qs(
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """kCrYT"""})
.find("""a""")
.get("""href""")
)["""url"""][0]
webbrowser.open(link)
| 88 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModel.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModel.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForPreTraining.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForPreTraining.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForCausalLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForCausalLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForMaskedLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForMaskedLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSequenceClassification.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSequenceClassification.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForQuestionAnswering.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForQuestionAnswering.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
| 655 | 0 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__A =UNetaDModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =self.dummy_uncond_unet
__A =PNDMScheduler()
__A =PNDMPipeline(unet=lowercase__ , scheduler=lowercase__ )
pndm.to(lowercase__ )
pndm.set_progress_bar_config(disable=lowercase__ )
__A =torch.manual_seed(0 )
__A =pndm(generator=lowercase__ , num_inference_steps=2_0 , output_type='''numpy''' ).images
__A =torch.manual_seed(0 )
__A =pndm(generator=lowercase__ , num_inference_steps=2_0 , output_type='''numpy''' , return_dict=lowercase__ )[0]
__A =image[0, -3:, -3:, -1]
__A =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__A =np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self ):
'''simple docstring'''
__A ='''google/ddpm-cifar10-32'''
__A =UNetaDModel.from_pretrained(lowercase__ )
__A =PNDMScheduler()
__A =PNDMPipeline(unet=lowercase__ , scheduler=lowercase__ )
pndm.to(lowercase__ )
pndm.set_progress_bar_config(disable=lowercase__ )
__A =torch.manual_seed(0 )
__A =pndm(generator=lowercase__ , output_type='''numpy''' ).images
__A =image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__A =np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 712 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
'''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
'''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
'''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
'''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
'''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
'''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}
class lowerCAmelCase__ ( __magic_name__ ):
'''simple docstring'''
lowercase_ = """bloom"""
lowercase_ = ["""past_key_values"""]
lowercase_ = {
"""num_hidden_layers""": """n_layer""",
"""num_attention_heads""": """n_head""",
}
def __init__( self , lowercase__=2_5_0_8_8_0 , lowercase__=6_4 , lowercase__=2 , lowercase__=8 , lowercase__=1E-5 , lowercase__=0.02 , lowercase__=True , lowercase__=1 , lowercase__=2 , lowercase__=False , lowercase__=0.0 , lowercase__=0.0 , lowercase__=1 , lowercase__=False , **lowercase__ , ):
'''simple docstring'''
__A =vocab_size
# Backward compatibility with n_embed kwarg
__A =kwargs.pop('''n_embed''' , lowercase__ )
__A =hidden_size if n_embed is None else n_embed
__A =n_layer
__A =n_head
__A =layer_norm_epsilon
__A =initializer_range
__A =use_cache
__A =pretraining_tp
__A =apply_residual_connection_post_layernorm
__A =hidden_dropout
__A =attention_dropout
__A =bos_token_id
__A =eos_token_id
__A =slow_but_exact
super().__init__(bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
class lowerCAmelCase__ ( __magic_name__ ):
'''simple docstring'''
lowercase_ = version.parse("""1.12""" )
def __init__( self , lowercase__ , lowercase__ = "default" , lowercase__ = None , lowercase__ = False , ):
'''simple docstring'''
super().__init__(lowercase__ , task=lowercase__ , patching_specs=lowercase__ , use_past=lowercase__ )
if not getattr(self._config , '''pad_token_id''' , lowercase__ ):
# TODO: how to do that better?
__A =0
@property
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(lowercase__ , direction='''inputs''' , inverted_values_shape=lowercase__ )
__A ={0: '''batch''', 1: '''past_sequence + sequence'''}
else:
__A ={0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __UpperCamelCase ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def __UpperCamelCase ( self ):
'''simple docstring'''
return self._config.n_head
@property
def __UpperCamelCase ( self ):
'''simple docstring'''
return 1E-3
def __UpperCamelCase ( self , lowercase__ , lowercase__ = -1 , lowercase__ = -1 , lowercase__ = False , lowercase__ = None , ):
'''simple docstring'''
__A =super(lowercase__ , self ).generate_dummy_inputs(
lowercase__ , batch_size=lowercase__ , seq_length=lowercase__ , is_pair=lowercase__ , framework=lowercase__ )
# We need to order the input in the way they appears in the forward()
__A =OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__A , __A =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__A =seqlen + 2
__A =self._config.hidden_size // self.num_attention_heads
__A =(
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
__A =(
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
__A =[
(torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) for _ in range(self.num_layers )
]
__A =common_inputs['''attention_mask''']
if self.use_past:
__A =ordered_inputs['''attention_mask'''].dtype
__A =torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(lowercase__ , lowercase__ , dtype=lowercase__ )] , dim=1 )
return ordered_inputs
@property
def __UpperCamelCase ( self ):
'''simple docstring'''
return 1_3
| 516 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a ( a ) ->bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = int(number**0.5 )
return number == sq * sq
def a ( a , a , a , a , a , a ) ->tuple[int, int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
SCREAMING_SNAKE_CASE = x_den * y_den * z_den
SCREAMING_SNAKE_CASE = gcd(a , a )
top //= hcf
bottom //= hcf
return top, bottom
def a ( a = 35 ) ->int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = set()
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = Fraction(0 )
SCREAMING_SNAKE_CASE = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
SCREAMING_SNAKE_CASE = x_num * y_den + x_den * y_num
SCREAMING_SNAKE_CASE = x_den * y_den
SCREAMING_SNAKE_CASE = gcd(a , a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE = add_three(
a , a , a , a , a , a )
unique_s.add(a )
# n=2
SCREAMING_SNAKE_CASE = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
SCREAMING_SNAKE_CASE = x_den * x_den * y_den * y_den
if is_sq(a ) and is_sq(a ):
SCREAMING_SNAKE_CASE = int(sqrt(a ) )
SCREAMING_SNAKE_CASE = int(sqrt(a ) )
SCREAMING_SNAKE_CASE = gcd(a , a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE = add_three(
a , a , a , a , a , a )
unique_s.add(a )
# n=-1
SCREAMING_SNAKE_CASE = x_num * y_num
SCREAMING_SNAKE_CASE = x_den * y_num + x_num * y_den
SCREAMING_SNAKE_CASE = gcd(a , a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE = add_three(
a , a , a , a , a , a )
unique_s.add(a )
# n=2
SCREAMING_SNAKE_CASE = x_num * x_num * y_num * y_num
SCREAMING_SNAKE_CASE = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(a ) and is_sq(a ):
SCREAMING_SNAKE_CASE = int(sqrt(a ) )
SCREAMING_SNAKE_CASE = int(sqrt(a ) )
SCREAMING_SNAKE_CASE = gcd(a , a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE = add_three(
a , a , a , a , a , a )
unique_s.add(a )
for num, den in unique_s:
total += Fraction(a , a )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F'''{solution() = }''') | 201 |
from PIL import Image
def a ( a ) ->Image:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image.size
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = image.load()
for i in range(a ):
for j in range(a ):
SCREAMING_SNAKE_CASE = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(a ):
for i in range(a ):
SCREAMING_SNAKE_CASE = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
__lowerCAmelCase = mean_threshold(Image.open('path_to_image').convert('L'))
image.save('output_image_path') | 201 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if len(lowercase ) < 2:
return collection
def circle_sort_util(lowercase , lowercase , lowercase ) -> bool:
SCREAMING_SNAKE_CASE_: Optional[int] =False
if low == high:
return swapped
SCREAMING_SNAKE_CASE_: Union[str, Any] =low
SCREAMING_SNAKE_CASE_: Optional[int] =high
while left < right:
if collection[left] > collection[right]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] =(
collection[right],
collection[left],
)
SCREAMING_SNAKE_CASE_: List[Any] =True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] =(
collection[right + 1],
collection[left],
)
SCREAMING_SNAKE_CASE_: Tuple =True
SCREAMING_SNAKE_CASE_: Optional[int] =low + int((high - low) / 2 )
SCREAMING_SNAKE_CASE_: List[str] =circle_sort_util(lowercase , lowercase , lowercase )
SCREAMING_SNAKE_CASE_: Any =circle_sort_util(lowercase , mid + 1 , lowercase )
return swapped or left_swap or right_swap
SCREAMING_SNAKE_CASE_: Union[str, Any] =True
while is_not_sorted is True:
SCREAMING_SNAKE_CASE_: Dict =circle_sort_util(lowercase , 0 , len(lowercase ) - 1 )
return collection
if __name__ == "__main__":
_UpperCAmelCase = input("""Enter numbers separated by a comma:\n""").strip()
_UpperCAmelCase = [int(item) for item in user_input.split(""",""")]
print(circle_sort(unsorted))
| 36 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCAmelCase = [float(x) for x in input("""Enter the elements of first array: """).split()]
_UpperCAmelCase = [float(x) for x in input("""Enter the elements of second array: """).split()]
print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
| 36 | 1 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BridgeTowerImageProcessor
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , UpperCamelCase_ : Tuple , UpperCamelCase_ : bool = True , UpperCamelCase_ : Dict[str, int] = None , UpperCamelCase_ : int = 32 , UpperCamelCase_ : bool = True , UpperCamelCase_ : Union[int, float] = 1 / 255 , UpperCamelCase_ : bool = True , UpperCamelCase_ : bool = True , UpperCamelCase_ : Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073] , UpperCamelCase_ : Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711] , UpperCamelCase_ : bool = True , UpperCamelCase_ : Union[str, Any]=7 , UpperCamelCase_ : Optional[int]=30 , UpperCamelCase_ : Tuple=400 , UpperCamelCase_ : List[Any]=3 , ):
"""simple docstring"""
__A = parent
__A = do_resize
__A = size if size is not None else {"""shortest_edge""": 288}
__A = size_divisor
__A = do_rescale
__A = rescale_factor
__A = do_normalize
__A = do_center_crop
__A = image_mean
__A = image_std
__A = do_pad
__A = batch_size
__A = num_channels
__A = min_resolution
__A = max_resolution
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
}
def lowerCAmelCase_ ( self : List[Any] , UpperCamelCase_ : int , UpperCamelCase_ : Union[str, Any]=False ):
"""simple docstring"""
if not batched:
__A = self.size["""shortest_edge"""]
__A = image_inputs[0]
if isinstance(__lowerCAmelCase , Image.Image ):
__A , __A = image.size
else:
__A , __A = image.shape[1], image.shape[2]
__A = size / min(__lowerCAmelCase , __lowerCAmelCase )
if h < w:
__A , __A = size, scale * w
else:
__A , __A = scale * h, size
__A = int((1_333 / 800) * size )
if max(__lowerCAmelCase , __lowerCAmelCase ) > max_size:
__A = max_size / max(__lowerCAmelCase , __lowerCAmelCase )
__A = newh * scale
__A = neww * scale
__A , __A = int(newh + 0.5 ), int(neww + 0.5 )
__A , __A = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
__A = []
for image in image_inputs:
__A , __A = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__A = max(__lowerCAmelCase , key=lambda UpperCamelCase_ : item[0] )[0]
__A = max(__lowerCAmelCase , key=lambda UpperCamelCase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __lowercase ( __a , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = BridgeTowerImageProcessor if is_vision_available() else None
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
__A = BridgeTowerImageProcessingTester(self )
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
__A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCAmelCase , """image_mean""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """image_std""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """do_normalize""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """do_resize""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """size""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """size_divisor""" ) )
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
__A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input
__A = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
__A , __A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__A = image_processing(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values
__A , __A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
__A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input
__A = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
__A , __A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__A = image_processing(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values
__A , __A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
__A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , torchify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , torch.Tensor )
# Test not batched input
__A = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
__A , __A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__A = image_processing(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values
__A , __A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
| 637 | '''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case = get_logger(__name__)
snake_case = R'''
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
'''
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
@add_start_docstrings(__lowerCAmelCase )
def __call__( self : List[Any] , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray ):
"""simple docstring"""
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
@add_start_docstrings(__lowerCAmelCase )
def __call__( self : Any , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray ):
"""simple docstring"""
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
@add_start_docstrings(__lowerCAmelCase )
def __call__( self : Optional[Any] , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int , **__lowerCAmelCase : Tuple ):
"""simple docstring"""
for processor in self:
_lowerCAmelCase = inspect.signature(processor.__call__ ).parameters
if len(__lowerCAmelCase ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
F"Make sure that all the required parameters: {list(function_args.keys() )} for "
F"{processor.__class__} are passed to the logits processor." )
_lowerCAmelCase = processor(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
else:
_lowerCAmelCase = processor(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : Any , __lowerCAmelCase : float ):
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not (temperature > 0):
raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}" )
_lowerCAmelCase = temperature
def __call__( self : Optional[Any] , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = scores / self.temperature
return scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCAmelCase : float , __lowerCAmelCase : float = -float('Inf' ) , __lowerCAmelCase : int = 1 ):
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or (top_p < 0 or top_p > 1.0):
raise ValueError(F"`top_p` has to be a float > 0 and < 1, but is {top_p}" )
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or (min_tokens_to_keep < 1):
raise ValueError(F"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}" )
_lowerCAmelCase = top_p
_lowerCAmelCase = filter_value
_lowerCAmelCase = min_tokens_to_keep
def __call__( self : int , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = lax.top_k(__lowerCAmelCase , scores.shape[-1] )
_lowerCAmelCase = jnp.full_like(__lowerCAmelCase , self.filter_value )
_lowerCAmelCase = jax.nn.softmax(__lowerCAmelCase , axis=-1 ).cumsum(axis=-1 )
_lowerCAmelCase = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
_lowerCAmelCase = jnp.roll(__lowerCAmelCase , 1 )
score_mask |= score_mask.at[:, 0].set(__lowerCAmelCase )
# min tokens to keep
_lowerCAmelCase = score_mask.at[:, : self.min_tokens_to_keep].set(__lowerCAmelCase )
_lowerCAmelCase = jnp.where(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_lowerCAmelCase = jax.lax.sort_key_val(__lowerCAmelCase , __lowerCAmelCase )[-1]
return next_scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCAmelCase : int , __lowerCAmelCase : float = -float('Inf' ) , __lowerCAmelCase : int = 1 ):
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or top_k <= 0:
raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}" )
_lowerCAmelCase = max(__lowerCAmelCase , __lowerCAmelCase )
_lowerCAmelCase = filter_value
def __call__( self : Tuple , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = scores.shape
_lowerCAmelCase = jnp.full(batch_size * vocab_size , self.filter_value )
_lowerCAmelCase = min(self.top_k , scores.shape[-1] ) # Safety check
_lowerCAmelCase , _lowerCAmelCase = lax.top_k(__lowerCAmelCase , __lowerCAmelCase )
_lowerCAmelCase = jnp.broadcast_to((jnp.arange(__lowerCAmelCase ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
_lowerCAmelCase = topk_scores.flatten()
_lowerCAmelCase = topk_indices.flatten() + shift
_lowerCAmelCase = next_scores_flat.at[topk_indices_flat].set(__lowerCAmelCase )
_lowerCAmelCase = next_scores_flat.reshape(__lowerCAmelCase , __lowerCAmelCase )
return next_scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : List[Any] , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = bos_token_id
def __call__( self : List[str] , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = jnp.full(scores.shape , -float('inf' ) )
_lowerCAmelCase = 1 - jnp.bool_(cur_len - 1 )
_lowerCAmelCase = jnp.where(__lowerCAmelCase , new_scores.at[:, self.bos_token_id].set(0 ) , __lowerCAmelCase )
return scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : int , __lowerCAmelCase : int , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = max_length
_lowerCAmelCase = eos_token_id
def __call__( self : Optional[int] , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = jnp.full(scores.shape , -float('inf' ) )
_lowerCAmelCase = 1 - jnp.bool_(cur_len - self.max_length + 1 )
_lowerCAmelCase = jnp.where(__lowerCAmelCase , new_scores.at[:, self.eos_token_id].set(0 ) , __lowerCAmelCase )
return scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : int ):
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or min_length < 0:
raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}" )
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or eos_token_id < 0:
raise ValueError(F"`eos_token_id` has to be a positive integer, but is {eos_token_id}" )
_lowerCAmelCase = min_length
_lowerCAmelCase = eos_token_id
def __call__( self : str , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
_lowerCAmelCase = jnp.where(__lowerCAmelCase , scores.at[:, self.eos_token_id].set(-float('inf' ) ) , __lowerCAmelCase )
return scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCAmelCase = list(__lowerCAmelCase )
_lowerCAmelCase = begin_index
def __call__( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = 1 - jnp.bool_(cur_len - self.begin_index )
_lowerCAmelCase = jnp.where(__lowerCAmelCase , scores.at[:, self.begin_suppress_tokens].set(-float('inf' ) ) , __lowerCAmelCase )
return scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : Optional[Any] , __lowerCAmelCase : list ):
"""simple docstring"""
_lowerCAmelCase = list(__lowerCAmelCase )
def __call__( self : List[Any] , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = scores.at[..., self.suppress_tokens].set(-float('inf' ) )
return scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : Tuple , __lowerCAmelCase : str ):
"""simple docstring"""
_lowerCAmelCase = dict(__lowerCAmelCase )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
_lowerCAmelCase = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
_lowerCAmelCase = force_token_array.at[index].set(__lowerCAmelCase )
_lowerCAmelCase = jnp.intaa(__lowerCAmelCase )
def __call__( self : str , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : jnp.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
def _force_token(__lowerCAmelCase : int ):
_lowerCAmelCase = scores.shape[0]
_lowerCAmelCase = self.force_token_array[generation_idx]
_lowerCAmelCase = jnp.ones_like(__lowerCAmelCase , dtype=scores.dtype ) * -float('inf' )
_lowerCAmelCase = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
_lowerCAmelCase = lax.dynamic_update_slice(__lowerCAmelCase , __lowerCAmelCase , (0, current_token) )
return new_scores
_lowerCAmelCase = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(__lowerCAmelCase ) , lambda: scores , ) , )
return scores
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCAmelCase = generate_config.eos_token_id
_lowerCAmelCase = generate_config.no_timestamps_token_id
_lowerCAmelCase = generate_config.no_timestamps_token_id + 1
_lowerCAmelCase = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(__lowerCAmelCase , 'max_initial_timestamp_index' ):
_lowerCAmelCase = generate_config.max_initial_timestamp_index
else:
_lowerCAmelCase = model_config.vocab_size
if self.max_initial_timestamp_index is None:
_lowerCAmelCase = model_config.vocab_size
def __call__( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCAmelCase = scores.at[:, self.no_timestamps_token_id].set(-float('inf' ) )
def handle_pairs(__lowerCAmelCase : Dict , __lowerCAmelCase : Dict ):
_lowerCAmelCase = jnp.where((cur_len - self.begin_index) >= 1 , __lowerCAmelCase , __lowerCAmelCase )
_lowerCAmelCase = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , __lowerCAmelCase , )
_lowerCAmelCase = jnp.where((cur_len - self.begin_index) < 2 , __lowerCAmelCase , __lowerCAmelCase )
_lowerCAmelCase = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , __lowerCAmelCase , __lowerCAmelCase , )
return jnp.where(
__lowerCAmelCase , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float('inf' ) ) , scores_k.at[: self.eos_token_id].set(-float('inf' ) ) , ) , __lowerCAmelCase , )
_lowerCAmelCase = jax.vmap(__lowerCAmelCase )(__lowerCAmelCase , __lowerCAmelCase )
_lowerCAmelCase = jnp.where(cur_len == self.begin_index , __lowerCAmelCase , __lowerCAmelCase )
_lowerCAmelCase = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , __lowerCAmelCase , )
_lowerCAmelCase = self.timestamp_begin + self.max_initial_timestamp_index
_lowerCAmelCase = jnp.where(
__lowerCAmelCase , scores.at[:, last_allowed + 1 :].set(-float('inf' ) ) , __lowerCAmelCase , )
# if sum of probability over timestamps is above any other token, sample timestamp
_lowerCAmelCase = jax.nn.log_softmax(__lowerCAmelCase , axis=-1 )
def handle_cumulative_probs(__lowerCAmelCase : Any , __lowerCAmelCase : str ):
_lowerCAmelCase = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
_lowerCAmelCase = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float('inf' ) ) , __lowerCAmelCase , )
_lowerCAmelCase = jax.vmap(__lowerCAmelCase )(__lowerCAmelCase , __lowerCAmelCase )
return scores
| 309 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : List[str] = '\\n\n'
_a : Any = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'
_a : List[Any] = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class UpperCamelCase_ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase_ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""input_texts""": datasets.Value("""string""" ),
} ) , reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""] , )
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1_6 , UpperCAmelCase = True , UpperCAmelCase=None ):
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__lowerCamelCase = """cuda"""
else:
__lowerCamelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(UpperCAmelCase )
__lowerCamelCase = model.to(UpperCAmelCase )
__lowerCamelCase = AutoTokenizer.from_pretrained(UpperCAmelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__lowerCamelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(UpperCAmelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__lowerCamelCase = model.config.max_length - 1
else:
__lowerCamelCase = model.config.max_length
__lowerCamelCase = tokenizer(
UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , return_tensors="""pt""" , return_attention_mask=UpperCAmelCase , ).to(UpperCAmelCase )
__lowerCamelCase = encodings["""input_ids"""]
__lowerCamelCase = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__lowerCamelCase = []
__lowerCamelCase = CrossEntropyLoss(reduction="""none""" )
for start_index in logging.tqdm(range(0 , len(UpperCAmelCase ) , UpperCAmelCase ) ):
__lowerCamelCase = min(start_index + batch_size , len(UpperCAmelCase ) )
__lowerCamelCase = encoded_texts[start_index:end_index]
__lowerCamelCase = attn_masks[start_index:end_index]
if add_start_token:
__lowerCamelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(UpperCAmelCase )
__lowerCamelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
__lowerCamelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(UpperCAmelCase ), attn_mask] , dim=1 )
__lowerCamelCase = encoded_batch
with torch.no_grad():
__lowerCamelCase = model(UpperCAmelCase , attention_mask=UpperCAmelCase ).logits
__lowerCamelCase = out_logits[..., :-1, :].contiguous()
__lowerCamelCase = labels[..., 1:].contiguous()
__lowerCamelCase = attn_mask[..., 1:].contiguous()
__lowerCamelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , UpperCAmelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(UpperCAmelCase )}
| 713 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""
A = ['''image_processor''', '''tokenizer''']
A = '''ViltImageProcessor'''
A = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase ):
__lowerCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , UpperCAmelCase , )
__lowerCamelCase = kwargs.pop("""feature_extractor""" )
__lowerCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase = self.image_processor
def __call__( self , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase = True , UpperCAmelCase = False , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = 0 , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = False , UpperCAmelCase = False , UpperCAmelCase = False , UpperCAmelCase = False , UpperCAmelCase = True , UpperCAmelCase = None , **UpperCAmelCase , ):
__lowerCamelCase = self.tokenizer(
text=UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , stride=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_token_type_ids=UpperCAmelCase , return_attention_mask=UpperCAmelCase , return_overflowing_tokens=UpperCAmelCase , return_special_tokens_mask=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , return_length=UpperCAmelCase , verbose=UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase , )
# add pixel_values + pixel_mask
__lowerCamelCase = self.image_processor(UpperCAmelCase , return_tensors=UpperCAmelCase )
encoding.update(UpperCAmelCase )
return encoding
def lowerCamelCase_ ( self , *UpperCAmelCase , **UpperCAmelCase ):
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def lowerCamelCase_ ( self , *UpperCAmelCase , **UpperCAmelCase ):
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@property
def lowerCamelCase_ ( self ):
__lowerCamelCase = self.tokenizer.model_input_names
__lowerCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def lowerCamelCase_ ( self ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , UpperCAmelCase , )
return self.image_processor_class
@property
def lowerCamelCase_ ( self ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , UpperCAmelCase , )
return self.image_processor
| 571 | 0 |
'''simple docstring'''
import functools
from typing import Any
def _a ( _lowerCamelCase , _lowerCamelCase ) -> bool:
"""simple docstring"""
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or len(_lowerCamelCase ) == 0:
raise ValueError("""the string should be not empty string""" )
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not all(
isinstance(_lowerCamelCase , _lowerCamelCase ) and len(_lowerCamelCase ) > 0 for item in words ):
raise ValueError("""the words should be a list of non-empty strings""" )
# Build trie
__snake_case : dict[str, Any] = {}
__snake_case : str = """WORD_KEEPER"""
for word in words:
__snake_case : str = trie
for c in word:
if c not in trie_node:
__snake_case : List[Any] = {}
__snake_case : Optional[int] = trie_node[c]
__snake_case : List[str] = True
__snake_case : List[str] = len(_lowerCamelCase )
# Dynamic programming method
@functools.cache
def is_breakable(_lowerCamelCase ) -> bool:
if index == len_string:
return True
__snake_case : Optional[int] = trie
for i in range(_lowerCamelCase , _lowerCamelCase ):
__snake_case : Dict = trie_node.get(string[i] , _lowerCamelCase )
if trie_node is None:
return False
if trie_node.get(_lowerCamelCase , _lowerCamelCase ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 26 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
__UpperCamelCase = "bart"
__UpperCamelCase = True
@st.cache(allow_output_mutation=_lowerCamelCase )
def _a ( ) -> Union[str, Any]:
"""simple docstring"""
if LOAD_DENSE_INDEX:
__snake_case : int = AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased""" )
__snake_case : Tuple = AutoModel.from_pretrained("""yjernite/retribert-base-uncased""" ).to("""cuda:0""" )
__snake_case : List[Any] = qar_model.eval()
else:
__snake_case , __snake_case : Optional[Any] = (None, None)
if MODEL_TYPE == "bart":
__snake_case : List[str] = AutoTokenizer.from_pretrained("""yjernite/bart_eli5""" )
__snake_case : Any = AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5""" ).to("""cuda:0""" )
__snake_case : int = torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth""" )
sas_model.load_state_dict(save_dict["""model"""] )
__snake_case : int = sas_model.eval()
else:
__snake_case , __snake_case : Dict = make_qa_sas_model(
model_name="""t5-small""" , from_file="""seq2seq_models/eli5_t5_model_1024_4.pth""" , device="""cuda:0""" )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=_lowerCamelCase )
def _a ( ) -> Tuple:
"""simple docstring"""
if LOAD_DENSE_INDEX:
__snake_case : Tuple = faiss.StandardGpuResources()
__snake_case : Optional[Any] = datasets.load_dataset(path="""wiki_snippets""" , name="""wiki40b_en_100_0""" )["""train"""]
__snake_case : str = np.memmap(
"""wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat""" , dtype="""float32""" , mode="""r""" , shape=(wikiaab_passages.num_rows, 128) , )
__snake_case : Optional[int] = faiss.IndexFlatIP(128 )
__snake_case : Any = faiss.index_cpu_to_gpu(_lowerCamelCase , 1 , _lowerCamelCase )
wikiaab_gpu_index_flat.add(_lowerCamelCase ) # TODO fix for larger GPU
else:
__snake_case , __snake_case : Tuple = (None, None)
__snake_case : List[str] = Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=_lowerCamelCase )
def _a ( ) -> List[Any]:
"""simple docstring"""
__snake_case : Tuple = datasets.load_dataset("""eli5""" , name="""LFQA_reddit""" )
__snake_case : Dict = elia["""train_eli5"""]
__snake_case : int = np.memmap(
"""eli5_questions_reps.dat""" , dtype="""float32""" , mode="""r""" , shape=(elia_train.num_rows, 128) )
__snake_case : Dict = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(_lowerCamelCase )
return (elia_train, eli5_train_q_index)
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = load_indexes()
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = load_models()
__UpperCamelCase , __UpperCamelCase = load_train_data()
def _a ( _lowerCamelCase , _lowerCamelCase=10 ) -> int:
"""simple docstring"""
__snake_case : Optional[int] = embed_questions_for_retrieval([question] , _lowerCamelCase , _lowerCamelCase )
__snake_case , __snake_case : Tuple = eli5_train_q_index.search(_lowerCamelCase , _lowerCamelCase )
__snake_case : Tuple = [elia_train[int(_lowerCamelCase )] for i in I[0]]
return nn_examples
def _a ( _lowerCamelCase , _lowerCamelCase="wiki40b" , _lowerCamelCase="dense" , _lowerCamelCase=10 ) -> Optional[Any]:
"""simple docstring"""
if source == "none":
__snake_case , __snake_case : Dict = (""" <P> """.join(["""""" for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
__snake_case , __snake_case : Dict = query_qa_dense_index(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else:
__snake_case , __snake_case : str = query_es_index(
_lowerCamelCase , _lowerCamelCase , index_name="""english_wiki40b_snippets_100w""" , n_results=_lowerCamelCase , )
__snake_case : Optional[int] = [
(res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
]
__snake_case : Optional[Any] = """question: {} context: {}""".format(_lowerCamelCase , _lowerCamelCase )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda _lowerCamelCase : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowerCamelCase : None),
} )
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=64 , _lowerCamelCase=256 , _lowerCamelCase=False , _lowerCamelCase=2 , _lowerCamelCase=0.95 , _lowerCamelCase=0.8 ) -> List[str]:
"""simple docstring"""
with torch.no_grad():
__snake_case : Union[str, Any] = qa_sas_generate(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , num_answers=1 , num_beams=_lowerCamelCase , min_len=_lowerCamelCase , max_len=_lowerCamelCase , do_sample=_lowerCamelCase , temp=_lowerCamelCase , top_p=_lowerCamelCase , top_k=_lowerCamelCase , max_input_length=1024 , device="""cuda:0""" , )[0]
return (answer, support_list)
st.title("Long Form Question Answering with ELI5")
# Start sidebar
__UpperCamelCase = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
__UpperCamelCase = "\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class=\"img-container\"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
__UpperCamelCase = "\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)
__UpperCamelCase = [
"Answer the question",
"View the retrieved document only",
"View the most similar ELI5 question and answer",
"Show me everything, please!",
]
__UpperCamelCase = st.sidebar.checkbox("Demo options")
if demo_options:
__UpperCamelCase = st.sidebar.selectbox(
"",
action_list,
index=3,
)
__UpperCamelCase = action_list.index(action_st)
__UpperCamelCase = st.sidebar.selectbox(
"",
["Show full text of passages", "Show passage section titles"],
index=0,
)
__UpperCamelCase = show_type == "Show full text of passages"
else:
__UpperCamelCase = 3
__UpperCamelCase = True
__UpperCamelCase = st.sidebar.checkbox("Retrieval options")
if retrieval_options:
__UpperCamelCase = "\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n "
st.sidebar.markdown(retriever_info)
__UpperCamelCase = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
__UpperCamelCase = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
__UpperCamelCase = "wiki40b"
__UpperCamelCase = "dense"
__UpperCamelCase = "beam"
__UpperCamelCase = 2
__UpperCamelCase = 64
__UpperCamelCase = 256
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = st.sidebar.checkbox("Generation options")
if generate_options:
__UpperCamelCase = "\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder's output probabilities.\n "
st.sidebar.markdown(generate_info)
__UpperCamelCase = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
__UpperCamelCase = st.sidebar.slider(
"Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
__UpperCamelCase = st.sidebar.slider(
"Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
__UpperCamelCase = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
__UpperCamelCase = st.sidebar.slider(
"Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
__UpperCamelCase = st.sidebar.slider(
"Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
__UpperCamelCase = None
# start main text
__UpperCamelCase = [
"<MY QUESTION>",
"How do people make chocolate?",
"Why do we get a fever when we are sick?",
"How can different animals perceive different colors?",
"What is natural language processing?",
"What's the best way to treat a sunburn?",
"What exactly are vitamins ?",
"How does nuclear energy provide electricity?",
"What's the difference between viruses and bacteria?",
"Why are flutes classified as woodwinds when most of them are made out of metal ?",
"Why do people like drinking coffee even though it tastes so bad?",
"What happens when wine ages? How does it make the wine taste better?",
"If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
"How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
"How does New Zealand have so many large bird predators?",
]
__UpperCamelCase = st.selectbox(
"What would you like to ask? ---- select <MY QUESTION> to enter a new query",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
__UpperCamelCase = st.text_input("Enter your question here:", "")
else:
__UpperCamelCase = question_s
if st.button("Show me!"):
if action in [0, 1, 3]:
if index_type == "mixed":
__UpperCamelCase , __UpperCamelCase = make_support(question, source=wiki_source, method="dense", n_results=10)
__UpperCamelCase , __UpperCamelCase = make_support(question, source=wiki_source, method="sparse", n_results=10)
__UpperCamelCase = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
__UpperCamelCase = support_list[:10]
__UpperCamelCase = "<P> " + " <P> ".join([res[-1] for res in support_list])
else:
__UpperCamelCase , __UpperCamelCase = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
__UpperCamelCase , __UpperCamelCase = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == "sampled"),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("### The model generated answer is:")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
for i, res in enumerate(support_list):
__UpperCamelCase = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
__UpperCamelCase = res[1].strip()
if sec_titles == "":
__UpperCamelCase = "[{}]({})".format(res[0], wiki_url)
else:
__UpperCamelCase = sec_titles.split(" & ")
__UpperCamelCase = " & ".join(
["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
)
st.markdown(
"{0:02d} - **Article**: {1:<18} <br> _Section_: {2}".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"> <span style=\"font-family:arial; font-size:10pt;\">" + res[-1] + "</span>", unsafe_allow_html=True
)
if action in [2, 3]:
__UpperCamelCase = find_nearest_training(question)
__UpperCamelCase = nn_train_list[0]
st.markdown(
"--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
)
__UpperCamelCase = [
"{}. {}".format(i + 1, " \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
if i == 0 or sc > 2
]
st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))
__UpperCamelCase = "\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 26 | 1 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = "▁"
_a = {"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}
_a = {
"sentencepiece_model_file": "sentencepiece.bpe.model",
"vocab_file": "vocab.txt",
}
_a = {
"vocab_file": {
"ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
"ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
},
"sentencepiece_model_file": {
"ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
"ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
},
}
_a = {
"ernie-m-base": 514,
"ernie-m-large": 514,
}
_a = {
"ernie-m-base": {"do_lower_case": False},
"ernie-m-large": {"do_lower_case": False},
}
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = ["""input_ids"""]
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = RESOURCE_FILES_NAMES
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase="utf8" , __lowerCAmelCase="[UNK]" , __lowerCAmelCase="[SEP]" , __lowerCAmelCase="[PAD]" , __lowerCAmelCase="[CLS]" , __lowerCAmelCase="[MASK]" , __lowerCAmelCase = None , **__lowerCAmelCase , ):
'''simple docstring'''
lowerCamelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , vocab_file=__lowerCAmelCase , encoding=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCAmelCase , )
lowerCamelCase__ = do_lower_case
lowerCamelCase__ = sentencepiece_model_ckpt
lowerCamelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__lowerCAmelCase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowerCamelCase__ = self.load_vocab(filepath=__lowerCAmelCase )
else:
lowerCamelCase__ = {self.sp_model.id_to_piece(__lowerCAmelCase ): id for id in range(self.sp_model.get_piece_size() )}
lowerCamelCase__ = {v: k for k, v in self.vocab.items()}
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
if text is None:
return None
lowerCamelCase__ = self.tokenize(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = '''''', []
for i, ch in enumerate(__lowerCAmelCase ):
if ch in self.SP_CHAR_MAPPING:
lowerCamelCase__ = self.SP_CHAR_MAPPING.get(__lowerCAmelCase )
else:
lowerCamelCase__ = unicodedata.normalize('''NFKC''' , __lowerCAmelCase )
if self.is_whitespace(__lowerCAmelCase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(__lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = normalized_text, [], 0
if self.do_lower_case:
lowerCamelCase__ = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowerCamelCase__ = token[1:]
lowerCamelCase__ = text[offset:].index(__lowerCAmelCase ) + offset
lowerCamelCase__ = start + len(__lowerCAmelCase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowerCamelCase__ = end
return token_mapping
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return len(self.vocab )
def __lowerCamelCase ( self ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self ):
'''simple docstring'''
lowerCamelCase__ = self.__dict__.copy()
lowerCamelCase__ = None
return state
def __setstate__( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowerCamelCase__ = {}
lowerCamelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(__lowerCAmelCase , __lowerCAmelCase ) for c in text) )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=False , __lowerCAmelCase=6_4 , __lowerCAmelCase=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get('''enable_sampling''' ) is True:
lowerCamelCase__ = True
if self.sp_model_kwargs.get('''alpha''' ) is not None:
lowerCamelCase__ = self.sp_model_kwargs.get('''alpha''' )
if self.sp_model_kwargs.get('''nbest_size''' ) is not None:
lowerCamelCase__ = self.sp_model_kwargs.get('''nbest_size''' )
if not enable_sampling:
lowerCamelCase__ = self.sp_model.EncodeAsPieces(__lowerCAmelCase )
else:
lowerCamelCase__ = self.sp_model.SampleEncodeAsPieces(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = []
for pi, piece in enumerate(__lowerCAmelCase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(__lowerCAmelCase ) and pi != 0:
new_pieces.append(__lowerCAmelCase )
continue
else:
continue
lowerCamelCase__ = 0
for i, chunk in enumerate(__lowerCAmelCase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(__lowerCAmelCase ) or self.is_punct(__lowerCAmelCase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(__lowerCAmelCase )
lowerCamelCase__ = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCamelCase__ = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCamelCase__ = i
if len(__lowerCAmelCase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase , ''' ''' ).strip()
return out_string
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = self.convert_ids_to_tokens(__lowerCAmelCase )
lowerCamelCase__ = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase , ''' ''' ).strip()
return out_string
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
return self.vocab.get(__lowerCAmelCase , self.vocab.get(self.unk_token ) )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
return self.reverse_vocab.get(__lowerCAmelCase , self.unk_token )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase__ = [self.cls_token_id]
lowerCamelCase__ = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(__lowerCAmelCase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(__lowerCAmelCase ) + 1) + [1] * (len(__lowerCAmelCase ) + 3)
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(__lowerCAmelCase ) == 1:
lowerCamelCase__ = unicodedata.category(__lowerCAmelCase )
if cat == "Zs":
return True
return False
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = {}
with io.open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
for index, line in enumerate(__lowerCAmelCase ):
lowerCamelCase__ = line.rstrip('''\n''' )
lowerCamelCase__ = int(__lowerCAmelCase )
return token_to_idx
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
'''simple docstring'''
lowerCamelCase__ = 0
if os.path.isdir(__lowerCAmelCase ):
lowerCamelCase__ = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
lowerCamelCase__ = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda __lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
''' Please check that the vocabulary is not corrupted!''' )
lowerCamelCase__ = token_index
writer.write(token + '''\n''' )
index += 1
lowerCamelCase__ = os.path.join(__lowerCAmelCase , '''sentencepiece.bpe.model''' )
with open(__lowerCAmelCase , '''wb''' ) as fi:
lowerCamelCase__ = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
return (vocab_file,)
| 705 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase , ):
'''simple docstring'''
lowerCamelCase__ = parent
lowerCamelCase__ = 1_3
lowerCamelCase__ = 7
lowerCamelCase__ = True
lowerCamelCase__ = True
lowerCamelCase__ = True
lowerCamelCase__ = 9_9
lowerCamelCase__ = 3_2
lowerCamelCase__ = 2
lowerCamelCase__ = 4
lowerCamelCase__ = 3_7
lowerCamelCase__ = '''gelu'''
lowerCamelCase__ = 0.1
lowerCamelCase__ = 0.1
lowerCamelCase__ = 5_1_2
lowerCamelCase__ = 1_6
lowerCamelCase__ = 2
lowerCamelCase__ = 0.02
lowerCamelCase__ = 3
lowerCamelCase__ = 4
lowerCamelCase__ = None
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase__ = None
if self.use_input_mask:
lowerCamelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
if self.use_labels:
lowerCamelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase__ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase__ = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self ):
'''simple docstring'''
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) = self.prepare_config_and_inputs()
lowerCamelCase__ = True
lowerCamelCase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowerCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = TFEsmModel(config=__lowerCAmelCase )
lowerCamelCase__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
lowerCamelCase__ = model(__lowerCAmelCase )
lowerCamelCase__ = [input_ids, input_mask]
lowerCamelCase__ = model(__lowerCAmelCase )
lowerCamelCase__ = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
'''simple docstring'''
lowerCamelCase__ = True
lowerCamelCase__ = TFEsmModel(config=__lowerCAmelCase )
lowerCamelCase__ = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''encoder_hidden_states''': encoder_hidden_states,
'''encoder_attention_mask''': encoder_attention_mask,
}
lowerCamelCase__ = model(__lowerCAmelCase )
lowerCamelCase__ = [input_ids, input_mask]
lowerCamelCase__ = model(__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase )
# Also check the case where encoder outputs are not passed
lowerCamelCase__ = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = TFEsmForMaskedLM(config=__lowerCAmelCase )
lowerCamelCase__ = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = self.num_labels
lowerCamelCase__ = TFEsmForTokenClassification(config=__lowerCAmelCase )
lowerCamelCase__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
lowerCamelCase__ = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) = config_and_inputs
lowerCamelCase__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __A ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCAmelCase_ = (
{
"""feature-extraction""": TFEsmModel,
"""fill-mask""": TFEsmForMaskedLM,
"""text-classification""": TFEsmForSequenceClassification,
"""token-classification""": TFEsmForTokenClassification,
"""zero-shot""": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = TFEsmModelTester(self )
lowerCamelCase__ = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=3_7 )
def __lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
@slow
def __lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ = TFEsmModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@unittest.skip('''Protein models do not support embedding resizing.''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('''Protein models do not support embedding resizing.''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(__lowerCAmelCase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
lowerCamelCase__ = model.get_bias()
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
for k, v in name.items():
assert isinstance(__lowerCAmelCase , tf.Variable )
else:
lowerCamelCase__ = model.get_output_embeddings()
assert x is None
lowerCamelCase__ = model.get_bias()
assert name is None
@require_tf
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
lowerCamelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase__ = model(__lowerCAmelCase )[0]
lowerCamelCase__ = [1, 6, 3_3]
self.assertEqual(list(output.numpy().shape ) , __lowerCAmelCase )
# compare the actual values for a slice.
lowerCamelCase__ = tf.constant(
[
[
[8.92_1518, -10.58_9814, -6.467_1307],
[-6.396_7156, -13.91_1377, -1.121_1915],
[-7.78_1247, -13.95_1557, -3.74_0592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2 ) )
@slow
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
lowerCamelCase__ = tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
lowerCamelCase__ = model(__lowerCAmelCase )[0]
# compare the actual values for a slice.
lowerCamelCase__ = tf.constant(
[
[
[0.1444_3092, 0.5412_5327, 0.324_7739],
[0.3034_0484, 0.0052_6676, 0.3107_7722],
[0.3227_8043, -0.2498_7096, 0.341_4628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 29 | 0 |
import numpy as np
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
'''simple docstring'''
return np.where(vector > 0 , A__ , (alpha * (np.exp(A__ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 537 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowercase (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase = ["""image_processor""", """tokenizer"""]
_UpperCAmelCase = """Pix2StructImageProcessor"""
_UpperCAmelCase = ("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = False
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __call__( self , lowerCAmelCase__=None , lowerCAmelCase__ = None , lowerCAmelCase__ = True , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 2_0_4_8 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = True , lowerCAmelCase__ = None , **lowerCAmelCase__ , ):
"""simple docstring"""
if images is None and text is None:
raise ValueError('You have to specify either images or text.' )
# Get only text
if images is None and not self.image_processor.is_vqa:
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.tokenizer
SCREAMING_SNAKE_CASE_ : str = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
SCREAMING_SNAKE_CASE_ : List[Any] = self.image_processor(
lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , max_patches=lowerCAmelCase__ , **lowerCAmelCase__ )
else:
# add pixel_values and bbox
SCREAMING_SNAKE_CASE_ : Any = self.image_processor(
lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , max_patches=lowerCAmelCase__ , header_text=lowerCAmelCase__ , **lowerCAmelCase__ )
if text is not None and not self.image_processor.is_vqa:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.tokenizer(
text=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
if "attention_mask" in text_encoding:
SCREAMING_SNAKE_CASE_ : Any = text_encoding.pop('attention_mask' )
if "input_ids" in text_encoding:
SCREAMING_SNAKE_CASE_ : List[str] = text_encoding.pop('input_ids' )
else:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = None
if text_encoding is not None:
encoding_image_processor.update(lowerCAmelCase__ )
return encoding_image_processor
def UpperCamelCase__ ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def UpperCamelCase__ ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ : Optional[int] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 101 | 0 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__ = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
'''
UpperCamelCase__ = '''\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
'''
UpperCamelCase__ = '''
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
predictions: list of predictions to score (as int64),
except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).
references: list of ground truth labels corresponding to the predictions (as int64),
except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
"accuracy": Accuracy
"f1": F1 score
"precision": Precision@10
Examples:
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\') # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')
>>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'precision@10\': 1.0}
'''
def UpperCAmelCase ( snake_case : str , snake_case : Union[str, Any] ):
return float((preds == labels).mean() )
def UpperCAmelCase ( snake_case : int , snake_case : str ):
_lowerCAmelCase:int = simple_accuracy(snake_case , snake_case )
_lowerCAmelCase:Optional[int] = float(fa_score(y_true=snake_case , y_pred=snake_case ) )
return {
"accuracy": acc,
"f1": fa,
}
def UpperCAmelCase ( snake_case : int , snake_case : Tuple ):
_lowerCAmelCase:Union[str, Any] = np.array(snake_case )
_lowerCAmelCase:str = np.array(snake_case )
_lowerCAmelCase:Union[str, Any] = en_sentvecs.shape[0]
# mean centering
_lowerCAmelCase:Optional[int] = en_sentvecs - np.mean(snake_case , axis=0 )
_lowerCAmelCase:List[str] = in_sentvecs - np.mean(snake_case , axis=0 )
_lowerCAmelCase:int = cdist(snake_case , snake_case , '''cosine''' )
_lowerCAmelCase:List[Any] = np.array(range(snake_case ) )
_lowerCAmelCase:str = sim.argsort(axis=1 )[:, :10]
_lowerCAmelCase:Tuple = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def __UpperCamelCase ( self : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
'''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
'''"wiki-ner"]''')
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''int64''')
if self.config_name != '''cvit-mkb-clsr'''
else datasets.Sequence(datasets.Value('''float32''')),
'''references''': datasets.Value('''int64''')
if self.config_name != '''cvit-mkb-clsr'''
else datasets.Sequence(datasets.Value('''float32''')),
}) ,codebase_urls=[] ,reference_urls=[] ,format='''numpy''' if self.config_name != '''cvit-mkb-clsr''' else None ,)
def __UpperCamelCase ( self : Dict ,a__ : Any ,a__ : Dict) -> Tuple:
"""simple docstring"""
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(a__ ,a__)}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(a__ ,a__)
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(a__ ,a__)}
else:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
'''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
'''"wiki-ner"]''')
| 439 |
"""simple docstring"""
def UpperCAmelCase ( snake_case : int , snake_case : int ):
return x if y == 0 else greatest_common_divisor(snake_case , x % y )
def UpperCAmelCase ( snake_case : int , snake_case : int ):
return (x * y) // greatest_common_divisor(snake_case , snake_case )
def UpperCAmelCase ( snake_case : int = 20 ):
_lowerCAmelCase:List[Any] = 1
for i in range(1 , n + 1 ):
_lowerCAmelCase:List[str] = lcm(snake_case , snake_case )
return g
if __name__ == "__main__":
print(F"{solution() = }")
| 439 | 1 |
_lowercase : str ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_lowercase : List[str] =[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_lowercase : int ={
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 305 | import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def A__ ( lowercase: Any ) -> Tuple:
A : Tuple =FileLock(str(tmpdir / 'foo.lock' ) )
A : Optional[int] =FileLock(str(tmpdir / 'foo.lock' ) )
A : Union[str, Any] =0.01
with locka.acquire():
with pytest.raises(lowercase ):
A : Optional[Any] =time.time()
locka.acquire(lowercase )
assert time.time() - _start > timeout
def A__ ( lowercase: int ) -> Optional[int]:
A : Any ='a' * 1_000 + '.lock'
A : Optional[Any] =FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith('.lock' )
assert not locka._lock_file.endswith(lowercase )
assert len(os.path.basename(locka._lock_file ) ) <= 255
A : str =FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(lowercase ):
locka.acquire(0 )
| 305 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ : int = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
"""GroupViTTextConfig""",
"""GroupViTVisionConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Optional[int] = [
"""GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GroupViTModel""",
"""GroupViTPreTrainedModel""",
"""GroupViTTextModel""",
"""GroupViTVisionModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : str = [
"""TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFGroupViTModel""",
"""TFGroupViTPreTrainedModel""",
"""TFGroupViTTextModel""",
"""TFGroupViTVisionModel""",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowercase__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 317 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowercase__ : Union[str, Any] = list[list[float | int]]
def UpperCamelCase_ ( lowerCAmelCase__ : Matrix , lowerCAmelCase__ : Matrix ) -> Matrix:
"""simple docstring"""
lowerCAmelCase_ : int = len(lowerCAmelCase__ )
lowerCAmelCase_ : Matrix = [[0 for _ in range(size + 1 )] for _ in range(lowerCAmelCase__ )]
lowerCAmelCase_ : int
lowerCAmelCase_ : int
lowerCAmelCase_ : int
lowerCAmelCase_ : int
lowerCAmelCase_ : int
lowerCAmelCase_ : float
for row in range(lowerCAmelCase__ ):
for col in range(lowerCAmelCase__ ):
lowerCAmelCase_ : Dict = matrix[row][col]
lowerCAmelCase_ : List[str] = vector[row][0]
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Optional[Any] = 0
while row < size and col < size:
# pivoting
lowerCAmelCase_ : Optional[Any] = max((abs(augmented[rowa][col] ), rowa) for rowa in range(lowerCAmelCase__ , lowerCAmelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
lowerCAmelCase_ ,lowerCAmelCase_ : Union[str, Any] = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , lowerCAmelCase__ ):
lowerCAmelCase_ : Union[str, Any] = augmented[rowa][col] / augmented[row][col]
lowerCAmelCase_ : List[str] = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , lowerCAmelCase__ ):
for row in range(lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[Any] = augmented[row][col] / augmented[col][col]
for cola in range(lowerCAmelCase__ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(lowerCAmelCase__ )
]
def UpperCamelCase_ ( lowerCAmelCase__ : list[int] ) -> Callable[[int], int]:
"""simple docstring"""
lowerCAmelCase_ : int = len(lowerCAmelCase__ )
lowerCAmelCase_ : Matrix = [[0 for _ in range(lowerCAmelCase__ )] for _ in range(lowerCAmelCase__ )]
lowerCAmelCase_ : Matrix = [[0] for _ in range(lowerCAmelCase__ )]
lowerCAmelCase_ : Matrix
lowerCAmelCase_ : int
lowerCAmelCase_ : int
lowerCAmelCase_ : int
for x_val, y_val in enumerate(lowerCAmelCase__ ):
for col in range(lowerCAmelCase__ ):
lowerCAmelCase_ : List[str] = (x_val + 1) ** (size - col - 1)
lowerCAmelCase_ : List[Any] = y_val
lowerCAmelCase_ : List[str] = solve(lowerCAmelCase__ , lowerCAmelCase__ )
def interpolated_func(lowerCAmelCase__ : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(lowerCAmelCase__ ) )
return interpolated_func
def UpperCamelCase_ ( lowerCAmelCase__ : int ) -> int:
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def UpperCamelCase_ ( lowerCAmelCase__ : Callable[[int], int] = question_function , lowerCAmelCase__ : int = 10 ) -> int:
"""simple docstring"""
lowerCAmelCase_ : list[int] = [func(lowerCAmelCase__ ) for x_val in range(1 , order + 1 )]
lowerCAmelCase_ : list[Callable[[int], int]] = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
lowerCAmelCase_ : int = 0
lowerCAmelCase_ : Callable[[int], int]
lowerCAmelCase_ : int
for poly in polynomials:
lowerCAmelCase_ : Union[str, Any] = 1
while func(lowerCAmelCase__ ) == poly(lowerCAmelCase__ ):
x_val += 1
ret += poly(lowerCAmelCase__ )
return ret
if __name__ == "__main__":
print(f'{solution() = }')
| 317 | 1 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 392 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
lowerCamelCase : List[Any] =logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None ) -> str:
UpperCamelCase__ : Any = tesseract_config if tesseract_config is not None else ""
# apply OCR
UpperCamelCase__ : int = to_pil_image(__lowerCAmelCase )
UpperCamelCase__ , UpperCamelCase__ : Dict = pil_image.size
UpperCamelCase__ : Optional[Any] = pytesseract.image_to_data(__lowerCAmelCase , lang=__lowerCAmelCase , output_type="dict" , config=__lowerCAmelCase )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[str] = data["text"], data["left"], data["top"], data["width"], data["height"]
# filter empty words and corresponding coordinates
UpperCamelCase__ : Tuple = [idx for idx, word in enumerate(__lowerCAmelCase ) if not word.strip()]
UpperCamelCase__ : Tuple = [word for idx, word in enumerate(__lowerCAmelCase ) if idx not in irrelevant_indices]
UpperCamelCase__ : Union[str, Any] = [coord for idx, coord in enumerate(__lowerCAmelCase ) if idx not in irrelevant_indices]
UpperCamelCase__ : List[str] = [coord for idx, coord in enumerate(__lowerCAmelCase ) if idx not in irrelevant_indices]
UpperCamelCase__ : Union[str, Any] = [coord for idx, coord in enumerate(__lowerCAmelCase ) if idx not in irrelevant_indices]
UpperCamelCase__ : str = [coord for idx, coord in enumerate(__lowerCAmelCase ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
UpperCamelCase__ : List[Any] = []
for x, y, w, h in zip(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ : Optional[int] = [x, y, x + w, y + h]
actual_boxes.append(__lowerCAmelCase )
# finally, normalize the bounding boxes
UpperCamelCase__ : int = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class __a ( A__ ):
_lowerCAmelCase : int = ['''pixel_values''']
def __init__( self : Dict , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Dict[str, int] = None , SCREAMING_SNAKE_CASE : PILImageResampling = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Optional[str] = None , SCREAMING_SNAKE_CASE : Optional[str] = "" , **SCREAMING_SNAKE_CASE : Any , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = size if size is not None else {"height": 2_24, "width": 2_24}
UpperCamelCase__ : str = get_size_dict(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = do_resize
UpperCamelCase__ : Union[str, Any] = size
UpperCamelCase__ : List[str] = resample
UpperCamelCase__ : Dict = apply_ocr
UpperCamelCase__ : str = ocr_lang
UpperCamelCase__ : List[str] = tesseract_config
def __lowercase ( self : List[str] , SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : Dict[str, int] , SCREAMING_SNAKE_CASE : PILImageResampling = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE : Dict , ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE )
if "height" not in size or "width" not in size:
raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase__ : Union[str, Any] = (size["height"], size["width"])
return resize(SCREAMING_SNAKE_CASE , size=SCREAMING_SNAKE_CASE , resample=SCREAMING_SNAKE_CASE , data_format=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __lowercase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : ImageInput , SCREAMING_SNAKE_CASE : bool = None , SCREAMING_SNAKE_CASE : Dict[str, int] = None , SCREAMING_SNAKE_CASE : PILImageResampling = None , SCREAMING_SNAKE_CASE : bool = None , SCREAMING_SNAKE_CASE : Optional[str] = None , SCREAMING_SNAKE_CASE : Optional[str] = None , SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE : ChannelDimension = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE : Tuple , ):
'''simple docstring'''
UpperCamelCase__ : List[str] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase__ : Tuple = size if size is not None else self.size
UpperCamelCase__ : Any = get_size_dict(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = resample if resample is not None else self.resample
UpperCamelCase__ : Any = apply_ocr if apply_ocr is not None else self.apply_ocr
UpperCamelCase__ : Any = ocr_lang if ocr_lang is not None else self.ocr_lang
UpperCamelCase__ : str = tesseract_config if tesseract_config is not None else self.tesseract_config
UpperCamelCase__ : Dict = make_list_of_images(SCREAMING_SNAKE_CASE )
if not valid_images(SCREAMING_SNAKE_CASE ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
# All transformations expect numpy arrays.
UpperCamelCase__ : Optional[int] = [to_numpy_array(SCREAMING_SNAKE_CASE ) for image in images]
if apply_ocr:
requires_backends(self , "pytesseract" )
UpperCamelCase__ : Dict = []
UpperCamelCase__ : List[Any] = []
for image in images:
UpperCamelCase__ , UpperCamelCase__ : Any = apply_tesseract(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
words_batch.append(SCREAMING_SNAKE_CASE )
boxes_batch.append(SCREAMING_SNAKE_CASE )
if do_resize:
UpperCamelCase__ : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE , size=SCREAMING_SNAKE_CASE , resample=SCREAMING_SNAKE_CASE ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
UpperCamelCase__ : Any = [flip_channel_order(SCREAMING_SNAKE_CASE ) for image in images]
UpperCamelCase__ : str = [to_channel_dimension_format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for image in images]
UpperCamelCase__ : Optional[Any] = BatchFeature(data={"pixel_values": images} , tensor_type=SCREAMING_SNAKE_CASE )
if apply_ocr:
UpperCamelCase__ : Tuple = words_batch
UpperCamelCase__ : Dict = boxes_batch
return data | 228 | 0 |
import copy
import re
class __snake_case :
'''simple docstring'''
_snake_case = 'hp'
_snake_case = {}
_snake_case = None
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : List[Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Any) ->Tuple:
"""simple docstring"""
_lowerCamelCase : Optional[int] = prefix
_lowerCamelCase : Union[str, Any] = defaults
cls.build_naming_info()
@staticmethod
def _SCREAMING_SNAKE_CASE ( _UpperCamelCase : Any , _UpperCamelCase : List[Any]) ->int:
"""simple docstring"""
if len(_UpperCamelCase) == 0:
return ""
_lowerCamelCase : Optional[Any] = None
if any(char.isdigit() for char in word):
raise Exception(F"""Parameters should not contain numbers: '{word}' contains a number""")
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(_UpperCamelCase) + 1):
_lowerCamelCase : List[str] = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
_lowerCamelCase : int = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(_UpperCamelCase : Dict):
_lowerCamelCase : Union[str, Any] = """"""
while integer != 0:
_lowerCamelCase : Any = chr(ord("""A""") + integer % 10) + s
integer //= 10
return s
_lowerCamelCase : int = 0
while True:
_lowerCamelCase : int = word + """#""" + int_to_alphabetic(_UpperCamelCase)
if sword in info["reverse_short_word"]:
continue
else:
_lowerCamelCase : Optional[Any] = sword
break
_lowerCamelCase : Tuple = short_word
_lowerCamelCase : Union[str, Any] = word
return short_word
@staticmethod
def _SCREAMING_SNAKE_CASE ( _UpperCamelCase : List[str] , _UpperCamelCase : Optional[Any]) ->Any:
"""simple docstring"""
_lowerCamelCase : Optional[Any] = param_name.split("""_""")
_lowerCamelCase : int = [TrialShortNamer.shortname_for_word(_UpperCamelCase , _UpperCamelCase) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
_lowerCamelCase : str = ["""""", """_"""]
for separator in separators:
_lowerCamelCase : Optional[Any] = separator.join(_UpperCamelCase)
if shortname not in info["reverse_short_param"]:
_lowerCamelCase : Any = shortname
_lowerCamelCase : Optional[int] = param_name
return shortname
return param_name
@staticmethod
def _SCREAMING_SNAKE_CASE ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Union[str, Any]) ->Dict:
"""simple docstring"""
_lowerCamelCase : str = TrialShortNamer.shortname_for_key(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : int = short_name
_lowerCamelCase : Optional[int] = param_name
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Tuple) ->int:
"""simple docstring"""
if cls.NAMING_INFO is not None:
return
_lowerCamelCase : str = {
"""short_word""": {},
"""reverse_short_word""": {},
"""short_param""": {},
"""reverse_short_param""": {},
}
_lowerCamelCase : int = list(cls.DEFAULTS.keys())
for k in field_keys:
cls.add_new_param_name(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : List[Any] = info
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Dict , _UpperCamelCase : List[str]) ->Dict:
"""simple docstring"""
cls.build_naming_info()
assert cls.PREFIX is not None
_lowerCamelCase : List[str] = [copy.copy(cls.PREFIX)]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(F"""You should provide a default value for the param name {k} with value {v}""")
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
_lowerCamelCase : Any = cls.NAMING_INFO["""short_param"""][k]
if isinstance(_UpperCamelCase , _UpperCamelCase):
_lowerCamelCase : int = 1 if v else 0
_lowerCamelCase : List[str] = """""" if isinstance(_UpperCamelCase , (int, float)) else """-"""
_lowerCamelCase : Dict = F"""{key}{sep}{v}"""
name.append(_UpperCamelCase)
return "_".join(_UpperCamelCase)
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : int , _UpperCamelCase : Tuple) ->Tuple:
"""simple docstring"""
_lowerCamelCase : Tuple = repr[len(cls.PREFIX) + 1 :]
if repr == "":
_lowerCamelCase : List[Any] = []
else:
_lowerCamelCase : Dict = repr.split("""_""")
_lowerCamelCase : Tuple = {}
for value in values:
if "-" in value:
_lowerCamelCase , _lowerCamelCase : Tuple = value.split("""-""")
else:
_lowerCamelCase : int = re.sub("""[0-9.]""" , """""" , _UpperCamelCase)
_lowerCamelCase : Tuple = float(re.sub("""[^0-9.]""" , """""" , _UpperCamelCase))
_lowerCamelCase : Dict = cls.NAMING_INFO["""reverse_short_param"""][p_k]
_lowerCamelCase : int = p_v
for k in cls.DEFAULTS:
if k not in parameters:
_lowerCamelCase : List[Any] = cls.DEFAULTS[k]
return parameters
| 15 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase : Any =logging.get_logger(__name__)
lowerCAmelCase : List[Any] ={
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class __snake_case ( __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
_snake_case = 'swin'
_snake_case = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Optional[int] , _UpperCamelCase : List[str]=224 , _UpperCamelCase : List[str]=4 , _UpperCamelCase : List[Any]=3 , _UpperCamelCase : Dict=96 , _UpperCamelCase : Any=[2, 2, 6, 2] , _UpperCamelCase : Any=[3, 6, 12, 24] , _UpperCamelCase : Tuple=7 , _UpperCamelCase : Tuple=4.0 , _UpperCamelCase : Dict=True , _UpperCamelCase : Tuple=0.0 , _UpperCamelCase : Any=0.0 , _UpperCamelCase : Optional[int]=0.1 , _UpperCamelCase : Any="gelu" , _UpperCamelCase : str=False , _UpperCamelCase : str=0.0_2 , _UpperCamelCase : Dict=1E-5 , _UpperCamelCase : List[str]=32 , _UpperCamelCase : Optional[Any]=None , _UpperCamelCase : List[Any]=None , **_UpperCamelCase : List[Any] , ) ->Tuple:
"""simple docstring"""
super().__init__(**_UpperCamelCase)
_lowerCamelCase : List[str] = image_size
_lowerCamelCase : Tuple = patch_size
_lowerCamelCase : Dict = num_channels
_lowerCamelCase : Union[str, Any] = embed_dim
_lowerCamelCase : str = depths
_lowerCamelCase : str = len(_UpperCamelCase)
_lowerCamelCase : Optional[Any] = num_heads
_lowerCamelCase : Tuple = window_size
_lowerCamelCase : int = mlp_ratio
_lowerCamelCase : Optional[int] = qkv_bias
_lowerCamelCase : List[str] = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : Tuple = drop_path_rate
_lowerCamelCase : List[str] = hidden_act
_lowerCamelCase : Dict = use_absolute_embeddings
_lowerCamelCase : int = layer_norm_eps
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Dict = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_lowerCamelCase : int = int(embed_dim * 2 ** (len(_UpperCamelCase) - 1))
_lowerCamelCase : Dict = ["""stem"""] + [F"""stage{idx}""" for idx in range(1 , len(_UpperCamelCase) + 1)]
_lowerCamelCase , _lowerCamelCase : List[str] = get_aligned_output_features_output_indices(
out_features=_UpperCamelCase , out_indices=_UpperCamelCase , stage_names=self.stage_names)
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = version.parse('1.11' )
@property
def _SCREAMING_SNAKE_CASE ( self : Tuple) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
])
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->float:
"""simple docstring"""
return 1E-4
| 15 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
UpperCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Union[str, Any] , *__lowerCAmelCase : Tuple , **__lowerCAmelCase : str ) -> None:
warnings.warn(
'''The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use VideoMAEImageProcessor instead.''' , __lowerCAmelCase , )
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
| 2 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
_UpperCamelCase = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os, _PatchedModuleObj )
assert isinstance(_test_patching.os.path, _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path, _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os, _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path, _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
assert _test_patching.open is open
_UpperCamelCase = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching, '''open''', __snake_case ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ):
pass
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching, '''len''', __snake_case ) is None
with patch_submodule(_test_patching, '''len''', __snake_case ):
assert _test_patching.len is mock
assert _test_patching.len is len
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__'''
_UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Optional[int]:
"""simple docstring"""
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
_UpperCamelCase = '''__test_patch_submodule_successive_join__'''
_UpperCamelCase = '''__test_patch_submodule_successive_dirname__'''
_UpperCamelCase = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ):
pass
with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ):
pass
| 19 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : dict[str, float] ={
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_9344,
"knot": 1.852,
}
SCREAMING_SNAKE_CASE__ : dict[str, float] ={
"km/h": 1.0,
"m/s": 0.2_7777_7778,
"mph": 0.6_2137_1192,
"knot": 0.5_3995_6803,
}
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->float:
if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
_lowerCamelCase : Any = (
F'''Incorrect \'from_type\' or \'to_type\' value: {unit_from!r}, {unit_to!r}\n'''
F'''Valid values are: {", ".join(SCREAMING_SNAKE_CASE_ )}'''
)
raise ValueError(SCREAMING_SNAKE_CASE_ )
return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] , 3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 558 | """simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def a__ ( self ) -> Tuple:
_lowerCamelCase : List[Any] = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
_lowerCamelCase : str = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
_lowerCamelCase : Tuple = {
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
_lowerCamelCase : Tuple = {
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 16000,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
_lowerCamelCase : List[str] = tempfile.mkdtemp()
_lowerCamelCase : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase : int = os.path.join(self.tmpdirname , _lowercase )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_lowercase ) + '''\n''' )
with open(self.feature_extraction_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_lowercase ) + '''\n''' )
# load decoder from hub
_lowerCamelCase : Optional[Any] = '''hf-internal-testing/ngram-beam-search-decoder'''
def a__ ( self , **_lowercase ) -> Optional[Any]:
_lowerCamelCase : str = self.add_kwargs_tokens_map.copy()
kwargs.update(_lowercase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def a__ ( self , **_lowercase ) -> Dict:
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **_lowercase )
def a__ ( self , **_lowercase ) -> int:
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **_lowercase )
def a__ ( self ) -> List[str]:
shutil.rmtree(self.tmpdirname )
def a__ ( self ) -> Optional[int]:
_lowerCamelCase : Optional[int] = self.get_tokenizer()
_lowerCamelCase : Dict = self.get_feature_extractor()
_lowerCamelCase : Any = self.get_decoder()
_lowerCamelCase : str = WavaVecaProcessorWithLM(tokenizer=_lowercase , feature_extractor=_lowercase , decoder=_lowercase )
processor.save_pretrained(self.tmpdirname )
_lowerCamelCase : Optional[int] = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowercase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , _lowercase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , _lowercase )
def a__ ( self ) -> Dict:
_lowerCamelCase : List[Any] = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
_lowerCamelCase : Dict = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def a__ ( self ) -> str:
_lowerCamelCase : Dict = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(_lowercase , '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=_lowercase , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def a__ ( self ) -> List[str]:
_lowerCamelCase : List[str] = self.get_feature_extractor()
_lowerCamelCase : Union[str, Any] = self.get_tokenizer()
_lowerCamelCase : Tuple = self.get_decoder()
_lowerCamelCase : Dict = WavaVecaProcessorWithLM(tokenizer=_lowercase , feature_extractor=_lowercase , decoder=_lowercase )
_lowerCamelCase : str = floats_list((3, 1000) )
_lowerCamelCase : Union[str, Any] = feature_extractor(_lowercase , return_tensors='''np''' )
_lowerCamelCase : Union[str, Any] = processor(_lowercase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def a__ ( self ) -> List[Any]:
_lowerCamelCase : Optional[Any] = self.get_feature_extractor()
_lowerCamelCase : Tuple = self.get_tokenizer()
_lowerCamelCase : Dict = self.get_decoder()
_lowerCamelCase : int = WavaVecaProcessorWithLM(tokenizer=_lowercase , feature_extractor=_lowercase , decoder=_lowercase )
_lowerCamelCase : List[Any] = '''This is a test string'''
_lowerCamelCase : Tuple = processor(text=_lowercase )
_lowerCamelCase : List[Any] = tokenizer(_lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def a__ ( self , _lowercase=(2, 10, 16) , _lowercase=77 ) -> Tuple:
np.random.seed(_lowercase )
return np.random.rand(*_lowercase )
def a__ ( self ) -> Optional[int]:
_lowerCamelCase : Optional[Any] = self.get_feature_extractor()
_lowerCamelCase : Optional[Any] = self.get_tokenizer()
_lowerCamelCase : List[str] = self.get_decoder()
_lowerCamelCase : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=_lowercase , feature_extractor=_lowercase , decoder=_lowercase )
_lowerCamelCase : List[str] = self._get_dummy_logits(shape=(10, 16) , seed=13 )
_lowerCamelCase : Optional[Any] = processor.decode(_lowercase )
_lowerCamelCase : Union[str, Any] = decoder.decode_beams(_lowercase )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('''</s> <s> </s>''' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def a__ ( self , _lowercase ) -> Any:
_lowerCamelCase : List[Any] = self.get_feature_extractor()
_lowerCamelCase : Union[str, Any] = self.get_tokenizer()
_lowerCamelCase : Any = self.get_decoder()
_lowerCamelCase : List[str] = WavaVecaProcessorWithLM(tokenizer=_lowercase , feature_extractor=_lowercase , decoder=_lowercase )
_lowerCamelCase : Any = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
_lowerCamelCase : List[str] = processor.batch_decode(_lowercase )
else:
with get_context(_lowercase ).Pool() as pool:
_lowerCamelCase : Optional[int] = processor.batch_decode(_lowercase , _lowercase )
_lowerCamelCase : Optional[int] = list(_lowercase )
with get_context('''fork''' ).Pool() as p:
_lowerCamelCase : Optional[Any] = decoder.decode_beams_batch(_lowercase , _lowercase )
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Dict = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(_lowercase , decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] , decoded_processor.text )
self.assertListEqual(_lowercase , decoded_processor.logit_score )
self.assertListEqual(_lowercase , decoded_processor.lm_score )
def a__ ( self ) -> Any:
_lowerCamelCase : Any = self.get_feature_extractor()
_lowerCamelCase : List[Any] = self.get_tokenizer()
_lowerCamelCase : int = self.get_decoder()
_lowerCamelCase : Any = WavaVecaProcessorWithLM(tokenizer=_lowercase , feature_extractor=_lowercase , decoder=_lowercase )
_lowerCamelCase : List[str] = self._get_dummy_logits()
_lowerCamelCase : int = 15
_lowerCamelCase : Union[str, Any] = -20.0
_lowerCamelCase : Optional[Any] = -4.0
_lowerCamelCase : str = processor.batch_decode(
_lowercase , beam_width=_lowercase , beam_prune_logp=_lowercase , token_min_logp=_lowercase , )
_lowerCamelCase : Optional[Any] = decoded_processor_out.text
_lowerCamelCase : str = list(_lowercase )
with get_context('''fork''' ).Pool() as pool:
_lowerCamelCase : List[Any] = decoder.decode_beams_batch(
_lowercase , _lowercase , beam_width=_lowercase , beam_prune_logp=_lowercase , token_min_logp=_lowercase , )
_lowerCamelCase : Tuple = [d[0][0] for d in decoded_decoder_out]
_lowerCamelCase : Any = [d[0][2] for d in decoded_decoder_out]
_lowerCamelCase : Optional[Any] = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(_lowercase , _lowercase )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] , _lowercase )
self.assertTrue(np.array_equal(_lowercase , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , _lowercase , atol=1E-3 ) )
self.assertTrue(np.array_equal(_lowercase , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , _lowercase , atol=1E-3 ) )
def a__ ( self ) -> str:
_lowerCamelCase : Any = self.get_feature_extractor()
_lowerCamelCase : Optional[int] = self.get_tokenizer()
_lowerCamelCase : List[str] = self.get_decoder()
_lowerCamelCase : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=_lowercase , feature_extractor=_lowercase , decoder=_lowercase )
_lowerCamelCase : Optional[int] = self._get_dummy_logits()
_lowerCamelCase : Optional[int] = 2.0
_lowerCamelCase : Optional[Any] = 5.0
_lowerCamelCase : int = -20.0
_lowerCamelCase : Any = True
_lowerCamelCase : List[Any] = processor.batch_decode(
_lowercase , alpha=_lowercase , beta=_lowercase , unk_score_offset=_lowercase , lm_score_boundary=_lowercase , )
_lowerCamelCase : Optional[Any] = decoded_processor_out.text
_lowerCamelCase : Optional[int] = list(_lowercase )
decoder.reset_params(
alpha=_lowercase , beta=_lowercase , unk_score_offset=_lowercase , lm_score_boundary=_lowercase , )
with get_context('''fork''' ).Pool() as pool:
_lowerCamelCase : Tuple = decoder.decode_beams_batch(
_lowercase , _lowercase , )
_lowerCamelCase : Optional[int] = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(_lowercase , _lowercase )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] , _lowercase )
_lowerCamelCase : int = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , _lowercase )
def a__ ( self ) -> Dict:
_lowerCamelCase : List[Any] = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase : Dict = processor.decoder.model_container[processor.decoder._model_key]
_lowerCamelCase : Union[str, Any] = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_lowerCamelCase : Optional[Any] = os.listdir(_lowercase )
_lowerCamelCase : str = ['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(_lowercase , _lowercase )
def a__ ( self ) -> int:
_lowerCamelCase : Union[str, Any] = snapshot_download('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase : Dict = WavaVecaProcessorWithLM.from_pretrained(_lowercase )
_lowerCamelCase : str = processor.decoder.model_container[processor.decoder._model_key]
_lowerCamelCase : Dict = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_lowerCamelCase : Optional[Any] = os.listdir(_lowercase )
_lowerCamelCase : Optional[Any] = os.listdir(_lowercase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(_lowercase , _lowercase )
def a__ ( self ) -> Optional[int]:
_lowerCamelCase : List[str] = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase : Optional[Any] = AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase : int = floats_list((3, 1000) )
_lowerCamelCase : Any = processor_wavaveca(_lowercase , return_tensors='''np''' )
_lowerCamelCase : List[str] = processor_auto(_lowercase , return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
_lowerCamelCase : List[Any] = self._get_dummy_logits()
_lowerCamelCase : Any = processor_wavaveca.batch_decode(_lowercase )
_lowerCamelCase : str = processor_auto.batch_decode(_lowercase )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def a__ ( self ) -> Dict:
_lowerCamelCase : List[str] = self.get_feature_extractor()
_lowerCamelCase : Dict = self.get_tokenizer()
_lowerCamelCase : Tuple = self.get_decoder()
_lowerCamelCase : str = WavaVecaProcessorWithLM(tokenizer=_lowercase , feature_extractor=_lowercase , decoder=_lowercase )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
@staticmethod
def a__ ( _lowercase , _lowercase ) -> Tuple:
_lowerCamelCase : Dict = [d[key] for d in offsets]
return retrieved_list
def a__ ( self ) -> Tuple:
_lowerCamelCase : Dict = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase : int = self._get_dummy_logits()[0]
_lowerCamelCase : str = processor.decode(_lowercase , output_word_offsets=_lowercase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(_lowercase , _lowercase ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''end_offset''' ) , [1, 3, 5] )
def a__ ( self ) -> Tuple:
_lowerCamelCase : List[str] = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase : str = self._get_dummy_logits()
_lowerCamelCase : Any = processor.batch_decode(_lowercase , output_word_offsets=_lowercase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(_lowercase , _lowercase ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(_lowercase , '''word''' ) ) for o in outputs['''word_offsets''']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''end_offset''' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def a__ ( self ) -> List[str]:
import torch
_lowerCamelCase : Union[str, Any] = load_dataset('''common_voice''' , '''en''' , split='''train''' , streaming=_lowercase )
_lowerCamelCase : Union[str, Any] = ds.cast_column('''audio''' , datasets.Audio(sampling_rate=16000 ) )
_lowerCamelCase : List[str] = iter(_lowercase )
_lowerCamelCase : List[str] = next(_lowercase )
_lowerCamelCase : int = AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
_lowerCamelCase : Tuple = WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
_lowerCamelCase : List[str] = processor(sample['''audio''']['''array'''] , return_tensors='''pt''' ).input_values
with torch.no_grad():
_lowerCamelCase : List[str] = model(_lowercase ).logits.cpu().numpy()
_lowerCamelCase : Optional[Any] = processor.decode(logits[0] , output_word_offsets=_lowercase )
_lowerCamelCase : Tuple = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
_lowerCamelCase : Union[str, Any] = [
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
_lowerCamelCase : Dict = '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(_lowercase , '''word''' ) ) , _lowercase )
self.assertEqual(''' '''.join(self.get_from_offsets(_lowercase , '''word''' ) ) , output.text )
# output times
_lowerCamelCase : str = torch.tensor(self.get_from_offsets(_lowercase , '''start_time''' ) )
_lowerCamelCase : Optional[int] = torch.tensor(self.get_from_offsets(_lowercase , '''end_time''' ) )
# fmt: off
_lowerCamelCase : Tuple = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
_lowerCamelCase : Optional[Any] = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(_lowercase , _lowercase , atol=0.01 ) )
self.assertTrue(torch.allclose(_lowercase , _lowercase , atol=0.01 ) )
| 558 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""post_extract_proj""": """feature_projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[Any]:
"""simple docstring"""
for attribute in key.split(""".""" ):
lowercase_ : str = getattr(lowercase , lowercase )
if weight_type is not None:
lowercase_ : Dict = getattr(lowercase , lowercase ).shape
else:
lowercase_ : Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
lowercase_ : Dict = value
elif weight_type == "weight_g":
lowercase_ : Union[str, Any] = value
elif weight_type == "weight_v":
lowercase_ : Dict = value
elif weight_type == "bias":
lowercase_ : Optional[Any] = value
else:
lowercase_ : int = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __magic_name__ ( lowercase , lowercase , lowercase ) -> int:
"""simple docstring"""
lowercase_ : Any = []
lowercase_ : Optional[Any] = fairseq_model.state_dict()
lowercase_ : List[str] = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
lowercase_ : List[Any] = False
if "conv_layers" in name:
load_conv_layer(
lowercase , lowercase , lowercase , lowercase , hf_model.config.feat_extract_norm == """group""" , )
lowercase_ : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
lowercase_ : Union[str, Any] = """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
lowercase_ : Any = True
if "*" in mapped_key:
lowercase_ : Tuple = name.split(lowercase )[0].split(""".""" )[-2]
lowercase_ : int = mapped_key.replace("""*""" , lowercase )
if "weight_g" in name:
lowercase_ : Optional[Any] = """weight_g"""
elif "weight_v" in name:
lowercase_ : Optional[int] = """weight_v"""
elif "weight" in name:
lowercase_ : Any = """weight"""
elif "bias" in name:
lowercase_ : Tuple = """bias"""
else:
lowercase_ : List[str] = None
set_recursively(lowercase , lowercase , lowercase , lowercase , lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[Any]:
"""simple docstring"""
lowercase_ : Any = full_name.split("""conv_layers.""" )[-1]
lowercase_ : Union[str, Any] = name.split(""".""" )
lowercase_ : List[str] = int(items[0] )
lowercase_ : Any = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
lowercase_ : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
lowercase_ : Optional[Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
lowercase_ : Optional[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
lowercase_ : Dict = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowercase )
def __magic_name__ ( lowercase , lowercase ) -> Optional[int]:
"""simple docstring"""
lowercase_ : str = SEWConfig()
if is_finetuned:
lowercase_ : Optional[int] = model.wav_encoder.wav_model.cfg
else:
lowercase_ : Any = model.cfg
lowercase_ : str = fs_config.conv_bias
lowercase_ : List[Any] = eval(fs_config.conv_feature_layers )
lowercase_ : int = [x[0] for x in conv_layers]
lowercase_ : Optional[Any] = [x[1] for x in conv_layers]
lowercase_ : Dict = [x[2] for x in conv_layers]
lowercase_ : Optional[int] = """gelu"""
lowercase_ : int = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
lowercase_ : Optional[int] = 0.0
lowercase_ : Optional[Any] = fs_config.activation_fn.name
lowercase_ : Union[str, Any] = fs_config.encoder_embed_dim
lowercase_ : Optional[Any] = 0.02
lowercase_ : List[Any] = fs_config.encoder_ffn_embed_dim
lowercase_ : Optional[Any] = 1E-5
lowercase_ : str = fs_config.encoder_layerdrop
lowercase_ : List[str] = fs_config.encoder_attention_heads
lowercase_ : str = fs_config.conv_pos_groups
lowercase_ : List[str] = fs_config.conv_pos
lowercase_ : Tuple = len(lowercase )
lowercase_ : List[str] = fs_config.encoder_layers
lowercase_ : List[Any] = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
lowercase_ : Union[str, Any] = model.cfg
lowercase_ : List[Any] = fs_config.final_dropout
lowercase_ : Optional[int] = fs_config.layerdrop
lowercase_ : Union[str, Any] = fs_config.activation_dropout
lowercase_ : Optional[Any] = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
lowercase_ : List[str] = fs_config.attention_dropout
lowercase_ : List[str] = fs_config.dropout_input
lowercase_ : Tuple = fs_config.dropout
lowercase_ : Optional[Any] = fs_config.mask_channel_length
lowercase_ : Optional[int] = fs_config.mask_channel_prob
lowercase_ : Union[str, Any] = fs_config.mask_length
lowercase_ : Union[str, Any] = fs_config.mask_prob
lowercase_ : Tuple = """Wav2Vec2FeatureExtractor"""
lowercase_ : str = """Wav2Vec2CTCTokenizer"""
return config
@torch.no_grad()
def __magic_name__ ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=True ) -> str:
"""simple docstring"""
if is_finetuned:
lowercase_ , lowercase_ , lowercase_ : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
lowercase_ , lowercase_ , lowercase_ : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
lowercase_ : int = SEWConfig.from_pretrained(lowercase )
else:
lowercase_ : Optional[int] = convert_config(model[0] , lowercase )
lowercase_ : Tuple = model[0].eval()
lowercase_ : Optional[Any] = True if config.feat_extract_norm == """layer""" else False
lowercase_ : Optional[int] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowercase , return_attention_mask=lowercase , )
if is_finetuned:
if dict_path:
lowercase_ : Optional[Any] = Dictionary.load(lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase_ : Union[str, Any] = target_dict.pad_index
lowercase_ : List[Any] = target_dict.bos_index
lowercase_ : int = target_dict.pad_index
lowercase_ : str = target_dict.bos_index
lowercase_ : Dict = target_dict.eos_index
lowercase_ : Optional[int] = len(target_dict.symbols )
lowercase_ : Any = os.path.join(lowercase , """vocab.json""" )
if not os.path.isdir(lowercase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase ) )
return
os.makedirs(lowercase , exist_ok=lowercase )
with open(lowercase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , lowercase )
lowercase_ : Optional[int] = WavaVecaCTCTokenizer(
lowercase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=lowercase , )
lowercase_ : Optional[Any] = WavaVecaProcessor(feature_extractor=lowercase , tokenizer=lowercase )
processor.save_pretrained(lowercase )
lowercase_ : Any = SEWForCTC(lowercase )
else:
lowercase_ : Any = SEWModel(lowercase )
feature_extractor.save_pretrained(lowercase )
recursively_load_weights(lowercase , lowercase , lowercase )
hf_model.save_pretrained(lowercase )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase_ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
) | 458 |
from string import ascii_uppercase
UpperCAmelCase_ = {char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_ = dict(enumerate(ascii_uppercase))
def __magic_name__ ( lowercase , lowercase ) -> str:
"""simple docstring"""
lowercase_ : Dict = len(lowercase )
lowercase_ : Tuple = 0
while True:
if x == i:
lowercase_ : Dict = 0
if len(lowercase ) == len(lowercase ):
break
key += key[i]
i += 1
return key
def __magic_name__ ( lowercase , lowercase ) -> str:
"""simple docstring"""
lowercase_ : Any = """"""
lowercase_ : Optional[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
lowercase_ : Any = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __magic_name__ ( lowercase , lowercase ) -> str:
"""simple docstring"""
lowercase_ : Optional[Any] = """"""
lowercase_ : Optional[Any] = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
lowercase_ : Optional[int] = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __magic_name__ ( ) -> None:
"""simple docstring"""
lowercase_ : Union[str, Any] = """THE GERMAN ATTACK"""
lowercase_ : str = """SECRET"""
lowercase_ : int = generate_key(lowercase , lowercase )
lowercase_ : List[str] = cipher_text(lowercase , lowercase )
print(f"""Encrypted Text = {s}""" )
print(f"""Original Text = {original_text(lowercase , lowercase )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 458 | 1 |
'''simple docstring'''
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ) -> str:
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Optional[Any] = {}
def __lowercase ( self : Union[str, Any] , lowerCamelCase : Any ) -> Optional[Any]:
if vertex not in self.adjacency:
lowerCAmelCase_ : List[Any] = {}
self.num_vertices += 1
def __lowercase ( self : List[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any] ) -> Dict:
self.add_vertex(lowerCamelCase )
self.add_vertex(lowerCamelCase )
if head == tail:
return
lowerCAmelCase_ : List[Any] = weight
lowerCAmelCase_ : Tuple = weight
def __lowercase ( self : Union[str, Any] ) -> Optional[Any]:
lowerCAmelCase_ : Any = self.get_edges()
for edge in edges:
lowerCAmelCase_ : Optional[int] = edge
edges.remove((tail, head, weight) )
for i in range(len(lowerCamelCase ) ):
lowerCAmelCase_ : List[str] = list(edges[i] )
edges.sort(key=lambda lowerCamelCase : e[2] )
for i in range(len(lowerCamelCase ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
lowerCAmelCase_ : Any = edges[i][2] + 1
for edge in edges:
lowerCAmelCase_ : List[str] = edge
lowerCAmelCase_ : Optional[int] = weight
lowerCAmelCase_ : Optional[int] = weight
def __str__( self : Optional[Any] ) -> Any:
lowerCAmelCase_ : List[str] = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
lowerCAmelCase_ : List[Any] = self.adjacency[head][tail]
string += F'{head} -> {tail} == {weight}\n'
return string.rstrip("""\n""" )
def __lowercase ( self : Tuple ) -> Dict:
lowerCAmelCase_ : Optional[Any] = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def __lowercase ( self : int ) -> int:
return self.adjacency.keys()
@staticmethod
def __lowercase ( lowerCamelCase : Tuple=None , lowerCamelCase : Union[str, Any]=None ) -> Any:
lowerCAmelCase_ : str = Graph()
if vertices is None:
lowerCAmelCase_ : Dict = []
if edges is None:
lowerCAmelCase_ : List[str] = []
for vertex in vertices:
g.add_vertex(lowerCamelCase )
for edge in edges:
g.add_edge(*lowerCamelCase )
return g
class __snake_case :
"""simple docstring"""
def __init__( self : str ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = {}
lowerCAmelCase_ : Optional[Any] = {}
def __len__( self : Optional[Any] ) -> int:
return len(self.parent )
def __lowercase ( self : Optional[Any] , lowerCamelCase : Optional[Any] ) -> List[str]:
if item in self.parent:
return self.find(lowerCamelCase )
lowerCAmelCase_ : List[Any] = item
lowerCAmelCase_ : Dict = 0
return item
def __lowercase ( self : Optional[Any] , lowerCamelCase : int ) -> Optional[Any]:
if item not in self.parent:
return self.make_set(lowerCamelCase )
if item != self.parent[item]:
lowerCAmelCase_ : List[str] = self.find(self.parent[item] )
return self.parent[item]
def __lowercase ( self : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Dict ) -> List[str]:
lowerCAmelCase_ : List[str] = self.find(lowerCamelCase )
lowerCAmelCase_ : List[str] = self.find(lowerCamelCase )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
lowerCAmelCase_ : Optional[Any] = roota
return roota
if self.rank[roota] < self.rank[roota]:
lowerCAmelCase_ : int = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
lowerCAmelCase_ : Any = roota
return roota
return None
@staticmethod
def __lowercase ( lowerCamelCase : int ) -> List[str]:
lowerCAmelCase_ : Optional[int] = graph.num_vertices
lowerCAmelCase_ : Tuple = Graph.UnionFind()
lowerCAmelCase_ : int = []
while num_components > 1:
lowerCAmelCase_ : str = {}
for vertex in graph.get_vertices():
lowerCAmelCase_ : int = -1
lowerCAmelCase_ : int = graph.get_edges()
for edge in edges:
lowerCAmelCase_ : List[Any] = edge
edges.remove((tail, head, weight) )
for edge in edges:
lowerCAmelCase_ : List[Any] = edge
lowerCAmelCase_ : List[str] = union_find.find(lowerCamelCase )
lowerCAmelCase_ : List[str] = union_find.find(lowerCamelCase )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowerCAmelCase_ : List[Any] = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowerCAmelCase_ : str = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
lowerCAmelCase_ : Dict = cheap_edge[vertex]
if union_find.find(lowerCamelCase ) != union_find.find(lowerCamelCase ):
union_find.union(lowerCamelCase , lowerCamelCase )
mst_edges.append(cheap_edge[vertex] )
lowerCAmelCase_ : Tuple = num_components - 1
lowerCAmelCase_ : Tuple = Graph.build(edges=lowerCamelCase )
return mst
| 702 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = "▁"
__A : Tuple = {"vocab_file": "sentencepiece.bpe.model"}
__A : Tuple = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
__A : int = {
"facebook/xglm-564M": 2048,
}
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ['input_ids', 'attention_mask']
def __init__( self : Any , lowerCamelCase : Any , lowerCamelCase : str="<s>" , lowerCamelCase : Optional[int]="</s>" , lowerCamelCase : Optional[Any]="</s>" , lowerCamelCase : List[Any]="<s>" , lowerCamelCase : Optional[Any]="<unk>" , lowerCamelCase : int="<pad>" , lowerCamelCase : Optional[Dict[str, Any]] = None , **lowerCamelCase : Optional[Any] , ) -> None:
lowerCAmelCase_ : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
lowerCAmelCase_ : str = 7
lowerCAmelCase_ : Any = [F'<madeupword{i}>' for i in range(self.num_madeup_words )]
lowerCAmelCase_ : Optional[Any] = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase , )
lowerCAmelCase_ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCamelCase ) )
lowerCAmelCase_ : int = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowerCAmelCase_ : List[str] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
lowerCAmelCase_ : Any = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
lowerCAmelCase_ : Union[str, Any] = len(self.sp_model )
lowerCAmelCase_ : Any = {F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(lowerCamelCase )
lowerCAmelCase_ : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : int ) -> Union[str, Any]:
lowerCAmelCase_ : Union[str, Any] = self.__dict__.copy()
lowerCAmelCase_ : str = None
lowerCAmelCase_ : List[str] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Dict , lowerCamelCase : List[Any] ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCAmelCase_ : int = {}
lowerCAmelCase_ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def __lowercase ( self : List[Any] , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
lowerCAmelCase_ : List[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def __lowercase ( self : Tuple , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase , token_ids_a=lowerCamelCase , already_has_special_tokens=lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase ))
return [1] + ([0] * len(lowerCamelCase )) + [1, 1] + ([0] * len(lowerCamelCase ))
def __lowercase ( self : str , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ) -> List[int]:
lowerCAmelCase_ : Dict = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def __lowercase ( self : str ) -> Union[str, Any]:
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def __lowercase ( self : Optional[Any] ) -> Dict:
lowerCAmelCase_ : Optional[Any] = {self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowercase ( self : int , lowerCamelCase : str ) -> List[str]:
return self.sp_model.encode(lowerCamelCase , out_type=lowerCamelCase )
def __lowercase ( self : int , lowerCamelCase : Dict ) -> Any:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowerCAmelCase_ : int = self.sp_model.PieceToId(lowerCamelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __lowercase ( self : Dict , lowerCamelCase : Optional[int] ) -> Any:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __lowercase ( self : List[str] , lowerCamelCase : Optional[Any] ) -> Optional[Any]:
lowerCAmelCase_ : str = """""".join(lowerCamelCase ).replace(lowerCamelCase , """ """ ).strip()
return out_string
def __lowercase ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCamelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase_ : List[str] = os.path.join(
lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase , """wb""" ) as fi:
lowerCAmelCase_ : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase )
return (out_vocab_file,)
| 398 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list:
if n_term == "":
return []
lowercase : str = []
for temp in range(int(snake_case__ ) ):
series.append(f"1/{temp + 1}" if series else """1""" )
return series
if __name__ == "__main__":
lowercase : List[Any] = input("""Enter the last number (nth term) of the Harmonic Series""")
print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
print(harmonic_series(nth_term))
| 336 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = ['''image_processor''', '''tokenizer''']
__UpperCAmelCase = '''ViltImageProcessor'''
__UpperCAmelCase = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , lowercase_=None , lowercase_=None , **lowercase_) -> List[Any]:
__snake_case = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , lowercase_ , )
__snake_case = kwargs.pop('feature_extractor')
__snake_case = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.')
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.')
super().__init__(lowercase_ , lowercase_)
__snake_case = self.image_processor
def __call__( self , lowercase_ , lowercase_ = None , lowercase_ = True , lowercase_ = False , lowercase_ = None , lowercase_ = None , lowercase_ = 0 , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = False , lowercase_ = False , lowercase_ = False , lowercase_ = False , lowercase_ = True , lowercase_ = None , **lowercase_ , ) -> BatchEncoding:
__snake_case = self.tokenizer(
text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_token_type_ids=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
# add pixel_values + pixel_mask
__snake_case = self.image_processor(lowercase_ , return_tensors=lowercase_)
encoding.update(lowercase_)
return encoding
def _a ( self , *lowercase_ , **lowercase_) -> Optional[Any]:
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_)
def _a ( self , *lowercase_ , **lowercase_) -> Dict:
return self.tokenizer.decode(*lowercase_ , **lowercase_)
@property
def _a ( self) -> Tuple:
__snake_case = self.tokenizer.model_input_names
__snake_case = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@property
def _a ( self) -> Optional[int]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , lowercase_ , )
return self.image_processor_class
@property
def _a ( self) -> List[str]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , lowercase_ , )
return self.image_processor
| 313 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _lowerCamelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCAmelCase_ = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def snake_case_ (self , __a=0 ) -> str:
UpperCamelCase = np.random.RandomState(_a )
UpperCamelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ (self ) -> Optional[Any]:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = pipe(**_a ).images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
UpperCamelCase = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ (self ) -> str:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_a )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = pipe(**_a ).images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
UpperCamelCase = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ (self ) -> Tuple:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = pipe(**_a ).images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
UpperCamelCase = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ (self ) -> List[Any]:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = pipe(**_a ).images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
UpperCamelCase = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ (self ) -> List[Any]:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = pipe(**_a ).images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
UpperCamelCase = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ (self ) -> int:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = pipe(**_a ).images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
UpperCamelCase = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ (self ) -> int:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase = pipe(**_a )
UpperCamelCase = output.images[0, -3:, -3:, -1]
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = 3 * [inputs.pop("prompt" )]
UpperCamelCase = pipe.tokenizer(
_a , padding="max_length" , max_length=pipe.tokenizer.model_max_length , truncation=_a , return_tensors="np" , )
UpperCamelCase = text_inputs["""input_ids"""]
UpperCamelCase = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
UpperCamelCase = prompt_embeds
# forward
UpperCamelCase = pipe(**_a )
UpperCamelCase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def snake_case_ (self ) -> Any:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = 3 * ["""this is a negative prompt"""]
UpperCamelCase = negative_prompt
UpperCamelCase = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase = pipe(**_a )
UpperCamelCase = output.images[0, -3:, -3:, -1]
UpperCamelCase = self.get_dummy_inputs()
UpperCamelCase = 3 * [inputs.pop("prompt" )]
UpperCamelCase = []
for p in [prompt, negative_prompt]:
UpperCamelCase = pipe.tokenizer(
_a , padding="max_length" , max_length=pipe.tokenizer.model_max_length , truncation=_a , return_tensors="np" , )
UpperCamelCase = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
UpperCamelCase = embeds
# forward
UpperCamelCase = pipe(**_a )
UpperCamelCase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class _lowerCamelCase ( unittest.TestCase ):
@property
def snake_case_ (self ) -> Optional[int]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def snake_case_ (self ) -> int:
UpperCamelCase = ort.SessionOptions()
UpperCamelCase = False
return options
def snake_case_ (self ) -> Optional[int]:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
UpperCamelCase = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="np" )
UpperCamelCase = output.images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
UpperCamelCase = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case_ (self ) -> Dict:
UpperCamelCase = DDIMScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=_a , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = """open neural network exchange"""
UpperCamelCase = np.random.RandomState(0 )
UpperCamelCase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_a , output_type="np" )
UpperCamelCase = output.images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
UpperCamelCase = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case_ (self ) -> Dict:
UpperCamelCase = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=_a , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = """open neural network exchange"""
UpperCamelCase = np.random.RandomState(0 )
UpperCamelCase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_a , output_type="np" )
UpperCamelCase = output.images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
UpperCamelCase = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case_ (self ) -> Any:
UpperCamelCase = 0
def test_callback_fn(__a , __a , __a ) -> None:
UpperCamelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
UpperCamelCase = latents[0, -3:, -3:, -1]
UpperCamelCase = np.array(
[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
UpperCamelCase = latents[0, -3:, -3:, -1]
UpperCamelCase = np.array(
[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
UpperCamelCase = False
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = """Andromeda galaxy in a bottle"""
UpperCamelCase = np.random.RandomState(0 )
pipe(
prompt=_a , num_inference_steps=5 , guidance_scale=7.5 , generator=_a , callback=_a , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def snake_case_ (self ) -> Union[str, Any]:
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(_a , _a )
assert pipe.safety_checker is None
UpperCamelCase = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_a )
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(_a )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
UpperCamelCase = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
| 705 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
"`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
F"{test_file} instead." )
UpperCamelCase = components[-1]
if not test_fn.endswith("py" ):
raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead." )
if not test_fn.startswith("test_modeling_" ):
raise ValueError(
F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead." )
UpperCamelCase = components[:-1] + [test_fn.replace(".py" , "" )]
UpperCamelCase = ".".join(_SCREAMING_SNAKE_CASE )
return test_module_path
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_module_path(_SCREAMING_SNAKE_CASE )
UpperCamelCase = importlib.import_module(_SCREAMING_SNAKE_CASE )
return test_module
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = get_test_module(_SCREAMING_SNAKE_CASE )
for attr in dir(_SCREAMING_SNAKE_CASE ):
if attr.endswith("ModelTester" ):
tester_classes.append(getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = get_test_module(_SCREAMING_SNAKE_CASE )
for attr in dir(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , "all_model_classes" , [] )
if len(_SCREAMING_SNAKE_CASE ) > 0:
test_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_test_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = test_class()
if hasattr(_SCREAMING_SNAKE_CASE , "setUp" ):
test.setUp()
UpperCamelCase = None
if hasattr(_SCREAMING_SNAKE_CASE , "model_tester" ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
UpperCamelCase = test.model_tester.__class__
return model_tester
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_test_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_test_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = []
for test_class in test_classes:
UpperCamelCase = get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE )
if tester_class is not None:
tester_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_test_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE ) for test_class in test_classes}
return test_tester_mapping
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_model_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for model_class in model_classes
}
return model_test_mapping
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_model_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for model_class in model_classes
}
return model_to_tester_mapping
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return o
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return o.__name__
elif isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ):
return [to_json(_SCREAMING_SNAKE_CASE ) for x in o]
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return {to_json(_SCREAMING_SNAKE_CASE ): to_json(_SCREAMING_SNAKE_CASE ) for k, v in o.items()}
else:
return o
| 544 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class SCREAMING_SNAKE_CASE :
def __init__( self : str , A__ : str , A__ : Union[str, Any]=13 , A__ : str=30 , A__ : Optional[int]=2 , A__ : Optional[Any]=3 , A__ : List[str]=True , A__ : Union[str, Any]=True , A__ : Any=32 , A__ : List[str]=2 , A__ : Any=4 , A__ : Optional[Any]=37 , A__ : int="gelu" , A__ : List[Any]=0.1 , A__ : Tuple=0.1 , A__ : Any=10 , A__ : List[Any]=0.02 , A__ : List[Any]=3 , A__ : List[str]=None , ):
"""simple docstring"""
__lowerCamelCase : Optional[int] = parent
__lowerCamelCase : Union[str, Any] = batch_size
__lowerCamelCase : List[str] = image_size
__lowerCamelCase : str = patch_size
__lowerCamelCase : List[str] = num_channels
__lowerCamelCase : List[str] = is_training
__lowerCamelCase : Optional[Any] = use_labels
__lowerCamelCase : Optional[Any] = hidden_size
__lowerCamelCase : Optional[int] = num_hidden_layers
__lowerCamelCase : Optional[int] = num_attention_heads
__lowerCamelCase : str = intermediate_size
__lowerCamelCase : Union[str, Any] = hidden_act
__lowerCamelCase : Dict = hidden_dropout_prob
__lowerCamelCase : Tuple = attention_probs_dropout_prob
__lowerCamelCase : List[Any] = type_sequence_label_size
__lowerCamelCase : int = initializer_range
__lowerCamelCase : List[Any] = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowerCamelCase : List[Any] = (image_size // patch_size) ** 2
__lowerCamelCase : Tuple = num_patches + 1
def a_ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase : Dict = None
if self.use_labels:
__lowerCamelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def a_ ( self : Tuple ):
"""simple docstring"""
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A__ , initializer_range=self.initializer_range , )
def a_ ( self : Optional[int] , A__ : Optional[int] , A__ : List[Any] , A__ : int ):
"""simple docstring"""
__lowerCamelCase : int = TFViTModel(config=A__ )
__lowerCamelCase : Union[str, Any] = model(A__ , training=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
__lowerCamelCase : Dict = self.image_size // 2
__lowerCamelCase : Optional[Any] = pixel_values[:, :, :image_size, :image_size]
__lowerCamelCase : Union[str, Any] = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
__lowerCamelCase : int = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def a_ ( self : List[Any] , A__ : Dict , A__ : str , A__ : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase : Dict = self.type_sequence_label_size
__lowerCamelCase : List[Any] = TFViTForImageClassification(A__ )
__lowerCamelCase : Union[str, Any] = model(A__ , labels=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
__lowerCamelCase : str = self.image_size // 2
__lowerCamelCase : int = pixel_values[:, :, :image_size, :image_size]
__lowerCamelCase : Optional[Any] = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowerCamelCase : List[str] = 1
__lowerCamelCase : Union[str, Any] = TFViTForImageClassification(A__ )
__lowerCamelCase : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCamelCase : Dict = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self : str ):
"""simple docstring"""
__lowerCamelCase : int = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Optional[Any] = config_and_inputs
__lowerCamelCase : Optional[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
snake_case__ : List[str] = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
snake_case__ : Tuple = (
{'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
if is_tf_available()
else {}
)
snake_case__ : Any = False
snake_case__ : Dict = False
snake_case__ : Any = False
def a_ ( self : str ):
"""simple docstring"""
__lowerCamelCase : List[Any] = TFViTModelTester(self )
__lowerCamelCase : Optional[int] = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def a_ ( self : Dict ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def a_ ( self : Optional[int] ):
"""simple docstring"""
pass
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def a_ ( self : int ):
"""simple docstring"""
pass
def a_ ( self : Any ):
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase : str = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
__lowerCamelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , tf.keras.layers.Layer ) )
def a_ ( self : Any ):
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase : Optional[int] = model_class(A__ )
__lowerCamelCase : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase : Optional[int] = [*signature.parameters.keys()]
__lowerCamelCase : Optional[int] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A__ )
def a_ ( self : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def a_ ( self : Any ):
"""simple docstring"""
__lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def a_ ( self : Dict ):
"""simple docstring"""
__lowerCamelCase : str = TFViTModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(A__ )
def __lowercase () -> int:
"""simple docstring"""
__lowerCamelCase : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def a_ ( self : Any ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def a_ ( self : Any ):
"""simple docstring"""
__lowerCamelCase : List[Any] = TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" )
__lowerCamelCase : int = self.default_image_processor
__lowerCamelCase : Optional[int] = prepare_img()
__lowerCamelCase : Any = image_processor(images=A__ , return_tensors="""tf""" )
# forward pass
__lowerCamelCase : Optional[int] = model(**A__ )
# verify the logits
__lowerCamelCase : List[Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , A__ )
__lowerCamelCase : List[Any] = tf.constant([-0.2744, 0.8215, -0.0836] )
tf.debugging.assert_near(outputs.logits[0, :3] , A__ , atol=1e-4 )
| 150 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , unittest.TestCase ):
snake_case__ : Dict = PhobertTokenizer
snake_case__ : Optional[Any] = False
def a_ ( self : Optional[int] ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowerCamelCase : Optional[int] = ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
__lowerCamelCase : List[Any] = dict(zip(A__ , range(len(A__ ) ) ) )
__lowerCamelCase : Optional[Any] = ["""#version: 0.2""", """l à</w>"""]
__lowerCamelCase : Any = {"""unk_token""": """<unk>"""}
__lowerCamelCase : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
__lowerCamelCase : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A__ ) )
def a_ ( self : List[str] , **A__ : Union[str, Any] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **A__ )
def a_ ( self : Union[str, Any] , A__ : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase : int = """Tôi là VinAI Research"""
__lowerCamelCase : int = """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
return input_text, output_text
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase : List[str] = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__lowerCamelCase : Dict = """Tôi là VinAI Research"""
__lowerCamelCase : Any = """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
__lowerCamelCase : str = tokenizer.tokenize(A__ )
print(A__ )
self.assertListEqual(A__ , A__ )
__lowerCamelCase : Any = tokens + [tokenizer.unk_token]
__lowerCamelCase : int = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , A__ )
| 150 | 1 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
UpperCAmelCase = []
for data in source_data:
for i, el in enumerate(lowerCAmelCase ):
if len(lowerCAmelCase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowerCAmelCase ) )
return data_lists
def lowercase__ ( lowerCAmelCase : list[list[float]] , lowerCAmelCase : list[int] ) -> list[list[float]]:
"""simple docstring"""
UpperCAmelCase = []
for dlist, weight in zip(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase = min(lowerCAmelCase )
UpperCAmelCase = max(lowerCAmelCase )
UpperCAmelCase = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
UpperCAmelCase = F"Invalid weight of {weight:f} provided"
raise ValueError(lowerCAmelCase )
score_lists.append(lowerCAmelCase )
return score_lists
def lowercase__ ( lowerCAmelCase : list[list[float]] ) -> list[float]:
"""simple docstring"""
UpperCAmelCase = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(lowerCAmelCase ):
UpperCAmelCase = final_scores[j] + ele
return final_scores
def lowercase__ ( lowerCAmelCase : list[list[float]] , lowerCAmelCase : list[int] ) -> list[list[float]]:
"""simple docstring"""
UpperCAmelCase = get_data(lowerCAmelCase )
UpperCAmelCase = calculate_each_score(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase = generate_final_scores(lowerCAmelCase )
# append scores to source data
for i, ele in enumerate(lowerCAmelCase ):
source_data[i].append(lowerCAmelCase )
return source_data
| 183 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _UpperCAmelCase ( SCREAMING_SNAKE_CASE_ ):
@slow
@require_torch
def a_ ( self ) -> List[Any]:
UpperCAmelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny' , 'prajjwal1/bert-tiny' )
UpperCAmelCase = BertTokenizer.from_pretrained('bert-base-uncased' )
UpperCAmelCase = bertabert.config.encoder.vocab_size
UpperCAmelCase = tokenizer.sep_token_id
UpperCAmelCase = tokenizer.cls_token_id
UpperCAmelCase = 1_2_8
UpperCAmelCase = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='train[:1%]' )
UpperCAmelCase = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='validation[:1%]' )
UpperCAmelCase = train_dataset.select(range(3_2 ) )
UpperCAmelCase = val_dataset.select(range(1_6 ) )
UpperCAmelCase = 4
def _map_to_encoder_decoder_inputs(lowercase_ ):
# Tokenizer will automatically set [BOS] <text> [EOS]
UpperCAmelCase = tokenizer(batch['article'] , padding='max_length' , truncation=lowercase_ , max_length=5_1_2 )
UpperCAmelCase = tokenizer(batch['highlights'] , padding='max_length' , truncation=lowercase_ , max_length=1_2_8 )
UpperCAmelCase = inputs.input_ids
UpperCAmelCase = inputs.attention_mask
UpperCAmelCase = outputs.input_ids
UpperCAmelCase = outputs.input_ids.copy()
UpperCAmelCase = [
[-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
]
UpperCAmelCase = outputs.attention_mask
assert all(len(lowercase_ ) == 5_1_2 for x in inputs.input_ids )
assert all(len(lowercase_ ) == 1_2_8 for x in outputs.input_ids )
return batch
def _compute_metrics(lowercase_ ):
UpperCAmelCase = pred.label_ids
UpperCAmelCase = pred.predictions
# all unnecessary tokens are removed
UpperCAmelCase = tokenizer.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )
UpperCAmelCase = tokenizer.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )
UpperCAmelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(lowercase_ ) )] ) / len(lowercase_ )
return {"accuracy": accuracy}
# map train dataset
UpperCAmelCase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=lowercase_ , batch_size=lowercase_ , remove_columns=['article', 'highlights'] , )
train_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
# same for validation dataset
UpperCAmelCase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=lowercase_ , batch_size=lowercase_ , remove_columns=['article', 'highlights'] , )
val_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = SeqaSeqTrainingArguments(
output_dir=lowercase_ , per_device_train_batch_size=lowercase_ , per_device_eval_batch_size=lowercase_ , predict_with_generate=lowercase_ , evaluation_strategy='steps' , do_train=lowercase_ , do_eval=lowercase_ , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
UpperCAmelCase = SeqaSeqTrainer(
model=lowercase_ , args=lowercase_ , compute_metrics=_compute_metrics , train_dataset=lowercase_ , eval_dataset=lowercase_ , tokenizer=lowercase_ , )
# start training
trainer.train()
| 183 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=UpperCAmelCase ):
'''simple docstring'''
_UpperCAmelCase : Tuple = ["transformers", "torch", "note_seq"]
def __init__( self : List[Any] , *lowercase : List[Any] , **lowercase : Dict ):
'''simple docstring'''
requires_backends(self , ['transformers', 'torch', 'note_seq'] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : List[str] , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : List[str] , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] ) | 686 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
_lowerCamelCase : Union[str, Any] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
_lowerCamelCase : Union[str, Any] = {'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}
_lowerCamelCase : Optional[int] = '''zero2'''
_lowerCamelCase : List[Any] = '''zero3'''
_lowerCamelCase : Dict = [ZEROa, ZEROa]
def a_ ( __lowercase : Union[str, Any] , __lowercase : Union[str, Any] , __lowercase : Tuple ) -> Dict:
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
_snake_case = parameterized.to_safe_name('_'.join(str(__lowercase ) for x in param.args ) )
return f'''{func.__name__}_{param_based_name}'''
# Cartesian-product of zero stages with models to test
_lowerCamelCase : Dict = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
@parameterized.expand(lowercase , name_func=lowercase )
def A ( self : List[str] , lowercase : List[Any] , lowercase : Dict ):
'''simple docstring'''
self.run_and_check(
stage=lowercase , model=lowercase , distributed=lowercase , fpaa=lowercase , )
@require_torch_multi_gpu
@parameterized.expand(lowercase , name_func=lowercase )
def A ( self : Any , lowercase : str , lowercase : List[str] ):
'''simple docstring'''
self.run_and_check(
stage=lowercase , model=lowercase , distributed=lowercase , fpaa=lowercase , )
@parameterized.expand(lowercase , name_func=lowercase )
def A ( self : List[str] , lowercase : Optional[Any] , lowercase : Optional[int] ):
'''simple docstring'''
self.run_and_check(
stage=lowercase , model=lowercase , distributed=lowercase , fpaa=lowercase , )
@require_torch_multi_gpu
@parameterized.expand(lowercase , name_func=lowercase )
def A ( self : Optional[int] , lowercase : Union[str, Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
self.run_and_check(
stage=lowercase , model=lowercase , distributed=lowercase , fpaa=lowercase , )
def A ( self : List[str] , lowercase : Optional[Any] ):
'''simple docstring'''
pass
def A ( self : str , lowercase : str , lowercase : str , lowercase : int = 10 , lowercase : bool = True , lowercase : bool = True , lowercase : bool = True , ):
'''simple docstring'''
_snake_case = models[model]
_snake_case = self.run_trainer(
stage=lowercase , model_name=lowercase , eval_steps=lowercase , num_train_epochs=1 , distributed=lowercase , fpaa=lowercase , )
self.do_checks(lowercase )
return output_dir
def A ( self : Any , lowercase : str , lowercase : str , lowercase : int = 10 , lowercase : int = 1 , lowercase : bool = True , lowercase : bool = True , ):
'''simple docstring'''
_snake_case = self.get_auto_remove_tmp_dir('./xxx' , after=lowercase )
_snake_case = f'''
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(lowercase )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
'''.split()
if fpaa:
args.extend(['--fp16'] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
_snake_case = f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
_snake_case = [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
_snake_case = self.get_launcher(lowercase )
_snake_case = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(lowercase , env=self.get_env() )
return output_dir
def A ( self : List[str] , lowercase : Any=False ):
'''simple docstring'''
_snake_case = min(2 , get_gpu_count() ) if distributed else 1
return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split() | 686 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def UpperCAmelCase_ ( __UpperCamelCase = True, *__UpperCamelCase, **__UpperCamelCase ):
if not is_tqdm_available():
raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.""" )
SCREAMING_SNAKE_CASE__ =False
if main_process_only:
SCREAMING_SNAKE_CASE__ =PartialState().local_process_index == 0
return _tqdm(*__UpperCamelCase, **__UpperCamelCase, disable=__UpperCamelCase )
| 588 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class __a ( __lowerCamelCase ):
"""simple docstring"""
_A : List[Any] = "trocr"
_A : Optional[int] = ["past_key_values"]
_A : Dict = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : str ,_UpperCamelCase : Dict=5_0_2_6_5 ,_UpperCamelCase : int=1_0_2_4 ,_UpperCamelCase : Union[str, Any]=1_2 ,_UpperCamelCase : Union[str, Any]=1_6 ,_UpperCamelCase : List[Any]=4_0_9_6 ,_UpperCamelCase : str="gelu" ,_UpperCamelCase : Dict=5_1_2 ,_UpperCamelCase : List[Any]=0.1 ,_UpperCamelCase : Dict=0.0 ,_UpperCamelCase : Optional[int]=0.0 ,_UpperCamelCase : Union[str, Any]=2 ,_UpperCamelCase : str=0.02 ,_UpperCamelCase : Any=0.0 ,_UpperCamelCase : Optional[int]=True ,_UpperCamelCase : str=False ,_UpperCamelCase : int=True ,_UpperCamelCase : Tuple=True ,_UpperCamelCase : str=1 ,_UpperCamelCase : Optional[Any]=0 ,_UpperCamelCase : str=2 ,**_UpperCamelCase : Any ,) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ =vocab_size
SCREAMING_SNAKE_CASE__ =d_model
SCREAMING_SNAKE_CASE__ =decoder_layers
SCREAMING_SNAKE_CASE__ =decoder_attention_heads
SCREAMING_SNAKE_CASE__ =decoder_ffn_dim
SCREAMING_SNAKE_CASE__ =activation_function
SCREAMING_SNAKE_CASE__ =max_position_embeddings
SCREAMING_SNAKE_CASE__ =dropout
SCREAMING_SNAKE_CASE__ =attention_dropout
SCREAMING_SNAKE_CASE__ =activation_dropout
SCREAMING_SNAKE_CASE__ =init_std
SCREAMING_SNAKE_CASE__ =decoder_layerdrop
SCREAMING_SNAKE_CASE__ =use_cache
SCREAMING_SNAKE_CASE__ =scale_embedding
SCREAMING_SNAKE_CASE__ =use_learned_position_embeddings
SCREAMING_SNAKE_CASE__ =layernorm_embedding
super().__init__(
pad_token_id=_UpperCamelCase ,bos_token_id=_UpperCamelCase ,eos_token_id=_UpperCamelCase ,decoder_start_token_id=_UpperCamelCase ,**_UpperCamelCase ,)
| 588 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__UpperCAmelCase = logging.get_logger(__name__)
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : List[str] = ["input_features", "is_longer"]
def __init__( self , lowerCamelCase_=64 , lowerCamelCase_=4_80_00 , lowerCamelCase_=4_80 , lowerCamelCase_=10 , lowerCamelCase_=10_24 , lowerCamelCase_=0.0 , lowerCamelCase_=False , lowerCamelCase_ = 0 , lowerCamelCase_ = 1_40_00 , lowerCamelCase_ = None , lowerCamelCase_ = "fusion" , lowerCamelCase_ = "repeatpad" , **lowerCamelCase_ , ) -> Tuple:
super().__init__(
feature_size=lowerCamelCase_ , sampling_rate=lowerCamelCase_ , padding_value=lowerCamelCase_ , return_attention_mask=lowerCamelCase_ , **lowerCamelCase_ , )
lowerCAmelCase__ = top_db
lowerCAmelCase__ = truncation
lowerCAmelCase__ = padding
lowerCAmelCase__ = fft_window_size
lowerCAmelCase__ = (fft_window_size >> 1) + 1
lowerCAmelCase__ = hop_length
lowerCAmelCase__ = max_length_s
lowerCAmelCase__ = max_length_s * sampling_rate
lowerCAmelCase__ = sampling_rate
lowerCAmelCase__ = frequency_min
lowerCAmelCase__ = frequency_max
lowerCAmelCase__ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=lowerCamelCase_ , min_frequency=lowerCamelCase_ , max_frequency=lowerCamelCase_ , sampling_rate=lowerCamelCase_ , norm=lowerCamelCase_ , mel_scale='''htk''' , )
lowerCAmelCase__ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=lowerCamelCase_ , min_frequency=lowerCamelCase_ , max_frequency=lowerCamelCase_ , sampling_rate=lowerCamelCase_ , norm='''slaney''' , mel_scale='''slaney''' , )
def __SCREAMING_SNAKE_CASE ( self ) -> Dict[str, Any]:
lowerCAmelCase__ = copy.deepcopy(self.__dict__ )
lowerCAmelCase__ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> np.ndarray:
lowerCAmelCase__ = spectrogram(
lowerCamelCase_ , window_function(self.fft_window_size , '''hann''' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=lowerCamelCase_ , log_mel='''dB''' , )
return log_mel_spectrogram.T
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> str:
lowerCAmelCase__ = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase__ = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase__ = [0]
# randomly choose index for each part
lowerCAmelCase__ = np.random.choice(ranges[0] )
lowerCAmelCase__ = np.random.choice(ranges[1] )
lowerCAmelCase__ = np.random.choice(ranges[2] )
lowerCAmelCase__ = mel[idx_front : idx_front + chunk_frames, :]
lowerCAmelCase__ = mel[idx_middle : idx_middle + chunk_frames, :]
lowerCAmelCase__ = mel[idx_back : idx_back + chunk_frames, :]
lowerCAmelCase__ = torch.tensor(mel[None, None, :] )
lowerCAmelCase__ = torch.nn.functional.interpolate(
lowerCamelCase_ , size=[chunk_frames, 64] , mode='''bilinear''' , align_corners=lowerCamelCase_ )
lowerCAmelCase__ = mel_shrink[0][0].numpy()
lowerCAmelCase__ = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> np.array:
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
lowerCAmelCase__ = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
lowerCAmelCase__ = len(lowerCamelCase_ ) - max_length
lowerCAmelCase__ = np.random.randint(0 , overflow + 1 )
lowerCAmelCase__ = waveform[idx : idx + max_length]
lowerCAmelCase__ = self._np_extract_fbank_features(lowerCamelCase_ , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
lowerCAmelCase__ = self._np_extract_fbank_features(lowerCamelCase_ , self.mel_filters )
lowerCAmelCase__ = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
lowerCAmelCase__ = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
lowerCAmelCase__ = np.stack([mel, mel, mel, mel] , axis=0 )
lowerCAmelCase__ = False
else:
lowerCAmelCase__ = self._random_mel_fusion(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ = True
else:
raise NotImplementedError(F"""data_truncating {truncation} not implemented""" )
else:
lowerCAmelCase__ = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
lowerCAmelCase__ = int(max_length / len(lowerCamelCase_ ) )
lowerCAmelCase__ = np.stack(np.tile(lowerCamelCase_ , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
lowerCAmelCase__ = int(max_length / len(lowerCamelCase_ ) )
lowerCAmelCase__ = np.stack(np.tile(lowerCamelCase_ , lowerCamelCase_ ) )
lowerCAmelCase__ = np.pad(lowerCamelCase_ , (0, max_length - waveform.shape[0]) , mode='''constant''' , constant_values=0 )
if truncation == "fusion":
lowerCAmelCase__ = self._np_extract_fbank_features(lowerCamelCase_ , self.mel_filters )
lowerCAmelCase__ = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
lowerCAmelCase__ = self._np_extract_fbank_features(lowerCamelCase_ , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self , lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> BatchFeature:
lowerCAmelCase__ = truncation if truncation is not None else self.truncation
lowerCAmelCase__ = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
lowerCAmelCase__ = isinstance(lowerCamelCase_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
lowerCAmelCase__ = is_batched_numpy or (
isinstance(lowerCamelCase_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase__ = [np.asarray(lowerCamelCase_ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCamelCase_ , np.ndarray ):
lowerCAmelCase__ = np.asarray(lowerCamelCase_ , dtype=np.floataa )
elif isinstance(lowerCamelCase_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase__ = [np.asarray(lowerCamelCase_ )]
# convert to mel spectrogram, truncate and pad if needed.
lowerCAmelCase__ = [
self._get_input_mel(lowerCamelCase_ , max_length if max_length else self.nb_max_samples , lowerCamelCase_ , lowerCamelCase_ )
for waveform in raw_speech
]
lowerCAmelCase__ = []
lowerCAmelCase__ = []
for mel, longer in padded_inputs:
input_mel.append(lowerCamelCase_ )
is_longer.append(lowerCamelCase_ )
if truncation == "fusion" and sum(lowerCamelCase_ ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
lowerCAmelCase__ = np.random.randint(0 , len(lowerCamelCase_ ) )
lowerCAmelCase__ = True
if isinstance(input_mel[0] , lowerCamelCase_ ):
lowerCAmelCase__ = [np.asarray(lowerCamelCase_ , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
lowerCAmelCase__ = [[longer] for longer in is_longer]
lowerCAmelCase__ = {'''input_features''': input_mel, '''is_longer''': is_longer}
lowerCAmelCase__ = BatchFeature(lowerCamelCase_ )
if return_tensors is not None:
lowerCAmelCase__ = input_features.convert_to_tensors(lowerCamelCase_ )
return input_features | 90 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''T''')
class __snake_case ( Generic[T]):
def __init__( self : int , __lowerCAmelCase : T ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = data
_lowerCamelCase : Node[T] | None = None
def __str__( self : Optional[Any] ):
"""simple docstring"""
return f'''{self.data}'''
class __snake_case ( Generic[T]):
def __init__( self : int ):
"""simple docstring"""
_lowerCamelCase : Node[T] | None = None
def __iter__( self : str ):
"""simple docstring"""
_lowerCamelCase : List[str] = self.top
while node:
yield node.data
_lowerCamelCase : Any = node.next
def __str__( self : int ):
"""simple docstring"""
return "->".join([str(__lowerCAmelCase ) for item in self] )
def __len__( self : int ):
"""simple docstring"""
return len(tuple(iter(self ) ) )
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
return self.top is None
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : T ):
"""simple docstring"""
_lowerCamelCase : Tuple = Node(__lowerCAmelCase )
if not self.is_empty():
_lowerCamelCase : Optional[int] = self.top
_lowerCamelCase : List[str] = node
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
if self.is_empty():
raise IndexError('''pop from empty stack''' )
assert isinstance(self.top , __lowerCAmelCase )
_lowerCamelCase : Any = self.top
_lowerCamelCase : Any = self.top.next
return pop_node.data
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
if self.is_empty():
raise IndexError('''peek from empty stack''' )
assert self.top is not None
return self.top.data
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
_lowerCamelCase : List[str] = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 83 | 0 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _a ( UpperCAmelCase__ = "isbn/0140328726" ) -> dict:
__SCREAMING_SNAKE_CASE = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('''/''' ) != 1:
__SCREAMING_SNAKE_CASE = f"""{olid} is not a valid Open Library olid"""
raise ValueError(UpperCAmelCase__ )
return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()
def _a ( UpperCAmelCase__ ) -> dict:
__SCREAMING_SNAKE_CASE = {
'''title''': '''Title''',
'''publish_date''': '''Publish date''',
'''authors''': '''Authors''',
'''number_of_pages''': '''Number of pages:''',
'''first_sentence''': '''First sentence''',
'''isbn_10''': '''ISBN (10)''',
'''isbn_13''': '''ISBN (13)''',
}
__SCREAMING_SNAKE_CASE = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
__SCREAMING_SNAKE_CASE = [
get_openlibrary_data(author['''key'''] )['''name'''] for author in data['''Authors''']
]
__SCREAMING_SNAKE_CASE = data['''First sentence''']['''value''']
for key, value in data.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = ''', '''.join(UpperCAmelCase__ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
lowerCAmelCase__ =input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(F'''Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.''')
continue
print(F'''\nSearching Open Library for ISBN: {isbn}...\n''')
try:
lowerCAmelCase__ =summarize_book(get_openlibrary_data(F'''isbn/{isbn}'''))
print("\n".join(F'''{key}: {value}''' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(F'''Sorry, there are no results for ISBN: {isbn}.''')
| 690 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase__ =list[list[float | int]]
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Matrix:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for row in range(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = matrix[row][col]
__SCREAMING_SNAKE_CASE = vector[row][0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
__SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCAmelCase__ , UpperCAmelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
__SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , UpperCAmelCase__ ):
for row in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(UpperCAmelCase__ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(UpperCAmelCase__ )
]
def _a ( UpperCAmelCase__ ) -> Callable[[int], int]:
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = [[0] for _ in range(UpperCAmelCase__ )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(UpperCAmelCase__ ):
for col in range(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
__SCREAMING_SNAKE_CASE = y_val
__SCREAMING_SNAKE_CASE = solve(UpperCAmelCase__ , UpperCAmelCase__ )
def interpolated_func(UpperCAmelCase__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCAmelCase__ ) )
return interpolated_func
def _a ( UpperCAmelCase__ ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def _a ( UpperCAmelCase__ = question_function , UpperCAmelCase__ = 10 ) -> int:
__SCREAMING_SNAKE_CASE = [func(UpperCAmelCase__ ) for x_val in range(1 , order + 1 )]
__SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
__SCREAMING_SNAKE_CASE = 1
while func(UpperCAmelCase__ ) == poly(UpperCAmelCase__ ):
x_val += 1
ret += poly(UpperCAmelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 690 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class _snake_case :
def __init__( self , a , a , a = True , a = False) -> Optional[int]:
SCREAMING_SNAKE_CASE = scheduler
SCREAMING_SNAKE_CASE = optimizers if isinstance(a , (list, tuple)) else [optimizers]
SCREAMING_SNAKE_CASE = split_batches
SCREAMING_SNAKE_CASE = step_with_optimizer
SCREAMING_SNAKE_CASE = GradientState()
def SCREAMING_SNAKE_CASE__ ( self , *a , **a) -> List[Any]:
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*a , **a)
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*a , **a)
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
SCREAMING_SNAKE_CASE = AcceleratorState().num_processes
for _ in range(a):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps'):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*a , **a)
else:
self.scheduler.step(*a , **a)
def SCREAMING_SNAKE_CASE__ ( self) -> Any:
return self.scheduler.get_last_lr()
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
return self.scheduler.state_dict()
def SCREAMING_SNAKE_CASE__ ( self , a) -> Optional[Any]:
self.scheduler.load_state_dict(a)
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
return self.scheduler.get_lr()
def SCREAMING_SNAKE_CASE__ ( self , *a , **a) -> str:
return self.scheduler.print_lr(*a , **a)
| 73 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(__UpperCAmelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
a : int = change_brightness(img, 100)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 640 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class A__ ( a__ ):
lowerCAmelCase__ : int = ["""pixel_values"""]
def __init__( self : Optional[int] , _UpperCAmelCase : Dict = True , _UpperCAmelCase : Union[str, Any] = None , _UpperCAmelCase : Dict = PILImageResampling.BICUBIC , _UpperCAmelCase : Union[str, Any] = True , _UpperCAmelCase : Any = None , _UpperCAmelCase : str = True , _UpperCAmelCase : str = 1 / 2_55 , _UpperCAmelCase : List[Any] = True , _UpperCAmelCase : Optional[int] = IMAGENET_DEFAULT_MEAN , _UpperCAmelCase : Union[str, Any] = IMAGENET_DEFAULT_STD , **_UpperCAmelCase : Union[str, Any] , ) -> None:
"""simple docstring"""
super().__init__(**lowercase__ )
__lowercase = size if size is not None else {'''shortest_edge''': 2_24}
__lowercase = get_size_dict(lowercase__ , default_to_square=lowercase__ )
__lowercase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
__lowercase = get_size_dict(lowercase__ , param_name='crop_size' )
__lowercase = do_resize
__lowercase = size
__lowercase = resample
__lowercase = do_center_crop
__lowercase = crop_size
__lowercase = do_rescale
__lowercase = rescale_factor
__lowercase = do_normalize
__lowercase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
__lowercase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def a__ ( self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict = PILImageResampling.BICUBIC , _UpperCAmelCase : List[Any] = None , **_UpperCAmelCase : Any , ) -> np.ndarray:
"""simple docstring"""
__lowercase = get_size_dict(lowercase__ , default_to_square=lowercase__ )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
__lowercase = int((2_56 / 2_24) * size['shortest_edge'] )
__lowercase = get_resize_output_image_size(lowercase__ , size=lowercase__ , default_to_square=lowercase__ )
__lowercase = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}""" )
return resize(
lowercase__ , size=(size_dict['height'], size_dict['width']) , resample=lowercase__ , data_format=lowercase__ , **lowercase__ )
def a__ ( self : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] = None , **_UpperCAmelCase : Optional[int] , ) -> np.ndarray:
"""simple docstring"""
__lowercase = get_size_dict(lowercase__ )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}""" )
return center_crop(lowercase__ , size=(size['height'], size['width']) , data_format=lowercase__ , **lowercase__ )
def a__ ( self : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] = None , **_UpperCAmelCase : Tuple , ) -> np.ndarray:
"""simple docstring"""
return rescale(lowercase__ , scale=lowercase__ , data_format=lowercase__ , **lowercase__ )
def a__ ( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] = None , **_UpperCAmelCase : Union[str, Any] , ) -> np.ndarray:
"""simple docstring"""
return normalize(lowercase__ , mean=lowercase__ , std=lowercase__ , data_format=lowercase__ , **lowercase__ )
def a__ ( self : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any = None , _UpperCAmelCase : Union[str, Any] = None , _UpperCAmelCase : Optional[Any] = None , _UpperCAmelCase : List[Any] = None , _UpperCAmelCase : List[Any] = None , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Any = None , _UpperCAmelCase : Any = None , _UpperCAmelCase : str = None , _UpperCAmelCase : int = None , _UpperCAmelCase : str = None , _UpperCAmelCase : Union[str, Any] = ChannelDimension.FIRST , **_UpperCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
__lowercase = do_resize if do_resize is not None else self.do_resize
__lowercase = resample if resample is not None else self.resample
__lowercase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowercase = do_rescale if do_rescale is not None else self.do_rescale
__lowercase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowercase = do_normalize if do_normalize is not None else self.do_normalize
__lowercase = image_mean if image_mean is not None else self.image_mean
__lowercase = image_std if image_std is not None else self.image_std
__lowercase = size if size is not None else self.size
__lowercase = get_size_dict(lowercase__ , default_to_square=lowercase__ )
__lowercase = crop_size if crop_size is not None else self.crop_size
__lowercase = get_size_dict(lowercase__ , param_name='crop_size' )
__lowercase = make_list_of_images(lowercase__ )
if not valid_images(lowercase__ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__lowercase = [to_numpy_array(lowercase__ ) for image in images]
if do_resize:
__lowercase = [self.resize(lowercase__ , lowercase__ , lowercase__ ) for image in images]
if do_center_crop:
__lowercase = [self.center_crop(lowercase__ , lowercase__ ) for image in images]
if do_rescale:
__lowercase = [self.rescale(lowercase__ , lowercase__ ) for image in images]
if do_normalize:
__lowercase = [self.normalize(lowercase__ , lowercase__ , lowercase__ ) for image in images]
__lowercase = [to_channel_dimension_format(lowercase__ , lowercase__ ) for image in images]
__lowercase = {'''pixel_values''': images}
return BatchFeature(data=lowercase__ , tensor_type=lowercase__ )
| 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"""configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
"""tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ["""BertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"""BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BertForMaskedLM""",
"""BertForMultipleChoice""",
"""BertForNextSentencePrediction""",
"""BertForPreTraining""",
"""BertForQuestionAnswering""",
"""BertForSequenceClassification""",
"""BertForTokenClassification""",
"""BertLayer""",
"""BertLMHeadModel""",
"""BertModel""",
"""BertPreTrainedModel""",
"""load_tf_weights_in_bert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"""TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFBertEmbeddings""",
"""TFBertForMaskedLM""",
"""TFBertForMultipleChoice""",
"""TFBertForNextSentencePrediction""",
"""TFBertForPreTraining""",
"""TFBertForQuestionAnswering""",
"""TFBertForSequenceClassification""",
"""TFBertForTokenClassification""",
"""TFBertLMHeadModel""",
"""TFBertMainLayer""",
"""TFBertModel""",
"""TFBertPreTrainedModel""",
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ["""TFBertTokenizer"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"""FlaxBertForCausalLM""",
"""FlaxBertForMaskedLM""",
"""FlaxBertForMultipleChoice""",
"""FlaxBertForNextSentencePrediction""",
"""FlaxBertForPreTraining""",
"""FlaxBertForQuestionAnswering""",
"""FlaxBertForSequenceClassification""",
"""FlaxBertForTokenClassification""",
"""FlaxBertModel""",
"""FlaxBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 688 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class __UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase_ = IFPipeline
lowerCAmelCase_ = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
lowerCAmelCase_ = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase_ = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
return self._get_dummy_components()
def UpperCAmelCase__ ( self : Optional[int] , _A : Dict , _A : Dict=0 ):
"""simple docstring"""
if str(_A ).startswith('''mps''' ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(_A )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.Generator(device=_A ).manual_seed(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
self._test_save_load_local()
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa )
__SCREAMING_SNAKE_CASE : int = IFSuperResolutionPipeline.from_pretrained(
'''DeepFloyd/IF-II-L-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa , text_encoder=_A , tokenizer=_A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('''cuda''' )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Optional[int] = pipe_a.encode_prompt('''anime turtle''' , device='''cuda''' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
__SCREAMING_SNAKE_CASE : List[Any] = None
__SCREAMING_SNAKE_CASE : str = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(_A , _A , _A , _A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
__SCREAMING_SNAKE_CASE : Dict = IFImgaImgPipeline(**pipe_a.components )
__SCREAMING_SNAKE_CASE : int = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(_A , _A , _A , _A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
__SCREAMING_SNAKE_CASE : int = IFInpaintingPipeline(**pipe_a.components )
__SCREAMING_SNAKE_CASE : Dict = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(_A , _A , _A , _A )
def UpperCAmelCase__ ( self : Optional[Any] , _A : str , _A : Optional[Any] , _A : Tuple , _A : List[str] ):
"""simple docstring"""
_start_torch_memory_measurement()
__SCREAMING_SNAKE_CASE : List[Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , num_inference_steps=2 , generator=_A , output_type='''np''' , )
__SCREAMING_SNAKE_CASE : int = output.images[0]
assert image.shape == (64, 64, 3)
__SCREAMING_SNAKE_CASE : Dict = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
__SCREAMING_SNAKE_CASE : List[str] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''' )
assert_mean_pixel_difference(_A , _A )
# pipeline 2
_start_torch_memory_measurement()
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__SCREAMING_SNAKE_CASE : int = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , generator=_A , num_inference_steps=2 , output_type='''np''' , )
__SCREAMING_SNAKE_CASE : int = output.images[0]
assert image.shape == (256, 256, 3)
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
__SCREAMING_SNAKE_CASE : Optional[int] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(_A , _A )
def UpperCAmelCase__ ( self : Dict , _A : Optional[Any] , _A : List[Any] , _A : Optional[int] , _A : Union[str, Any] ):
"""simple docstring"""
_start_torch_memory_measurement()
__SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE : List[Any] = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , num_inference_steps=2 , generator=_A , output_type='''np''' , )
__SCREAMING_SNAKE_CASE : Any = output.images[0]
assert image.shape == (64, 64, 3)
__SCREAMING_SNAKE_CASE : List[str] = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
__SCREAMING_SNAKE_CASE : Any = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''' )
assert_mean_pixel_difference(_A , _A )
# pipeline 2
_start_torch_memory_measurement()
__SCREAMING_SNAKE_CASE : Tuple = torch.Generator(device='''cpu''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , original_image=_A , generator=_A , num_inference_steps=2 , output_type='''np''' , )
__SCREAMING_SNAKE_CASE : List[Any] = output.images[0]
assert image.shape == (256, 256, 3)
__SCREAMING_SNAKE_CASE : Dict = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
__SCREAMING_SNAKE_CASE : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(_A , _A )
def UpperCAmelCase__ ( self : Optional[int] , _A : List[str] , _A : List[str] , _A : Any , _A : Dict ):
"""simple docstring"""
_start_torch_memory_measurement()
__SCREAMING_SNAKE_CASE : Tuple = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(_A )
__SCREAMING_SNAKE_CASE : int = torch.Generator(device='''cpu''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE : Tuple = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , mask_image=_A , num_inference_steps=2 , generator=_A , output_type='''np''' , )
__SCREAMING_SNAKE_CASE : List[Any] = output.images[0]
assert image.shape == (64, 64, 3)
__SCREAMING_SNAKE_CASE : int = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
__SCREAMING_SNAKE_CASE : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''' )
assert_mean_pixel_difference(_A , _A )
# pipeline 2
_start_torch_memory_measurement()
__SCREAMING_SNAKE_CASE : Dict = torch.Generator(device='''cpu''' ).manual_seed(0 )
__SCREAMING_SNAKE_CASE : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_A )
__SCREAMING_SNAKE_CASE : Tuple = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = pipe_a(
prompt_embeds=_A , negative_prompt_embeds=_A , image=_A , mask_image=_A , original_image=_A , generator=_A , num_inference_steps=2 , output_type='''np''' , )
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
__SCREAMING_SNAKE_CASE : List[str] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
__SCREAMING_SNAKE_CASE : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(_A , _A )
def a__ ( ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 74 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class snake_case_ ( a ):
'''simple docstring'''
__UpperCamelCase = 'EncodecFeatureExtractor'
__UpperCamelCase = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self, A_, A_ ) -> Optional[int]:
super().__init__(A_, A_ )
UpperCAmelCase__ =self.feature_extractor
UpperCAmelCase__ =False
def __UpperCAmelCase ( self, A_=None, A_=None, A_=True ) -> Union[str, Any]:
return self.tokenizer.get_decoder_prompt_ids(task=A_, language=A_, no_timestamps=A_ )
def __call__( self, *A_, **A_ ) -> Any:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A_, **A_ )
UpperCAmelCase__ =kwargs.pop("audio", A_ )
UpperCAmelCase__ =kwargs.pop("sampling_rate", A_ )
UpperCAmelCase__ =kwargs.pop("text", A_ )
if len(A_ ) > 0:
UpperCAmelCase__ =args[0]
UpperCAmelCase__ =args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
UpperCAmelCase__ =self.tokenizer(A_, **A_ )
if audio is not None:
UpperCAmelCase__ =self.feature_extractor(A_, *A_, sampling_rate=A_, **A_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCAmelCase__ =audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
UpperCAmelCase__ =audio_inputs["padding_mask"]
return inputs
def __UpperCAmelCase ( self, *A_, **A_ ) -> Dict:
UpperCAmelCase__ =kwargs.pop("audio", A_ )
UpperCAmelCase__ =kwargs.pop("padding_mask", A_ )
if len(A_ ) > 0:
UpperCAmelCase__ =args[0]
UpperCAmelCase__ =args[1:]
if audio_values is not None:
return self._decode_audio(A_, padding_mask=A_ )
else:
return self.tokenizer.batch_decode(*A_, **A_ )
def __UpperCAmelCase ( self, *A_, **A_ ) -> int:
return self.tokenizer.decode(*A_, **A_ )
def __UpperCAmelCase ( self, A_, A_ = None ) -> List[np.ndarray]:
UpperCAmelCase__ =to_numpy(A_ )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ =audio_values.shape
if padding_mask is None:
return list(A_ )
UpperCAmelCase__ =to_numpy(A_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCAmelCase__ =seq_len - padding_mask.shape[-1]
UpperCAmelCase__ =1 - self.feature_extractor.padding_value
UpperCAmelCase__ =np.pad(A_, ((0, 0), (0, difference)), "constant", constant_values=A_ )
UpperCAmelCase__ =audio_values.tolist()
for i in range(A_ ):
UpperCAmelCase__ =np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCAmelCase__ =sliced_audio.reshape(A_, -1 )
return audio_values
| 625 | 0 |
from typing import List
import numpy as np
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = {key: len(snake_case_ ) for key, value in gen_kwargs.items() if isinstance(snake_case_ , snake_case_ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'''Sharding is ambiguous for this dataset: '''
+ '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
+ '''\n'''.join(F"""\t- key {key} has length {length}""" for key, length in lists_lengths.items() )
+ '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
+ '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
) )
lowercase__ = max(lists_lengths.values() , default=0 )
return max(1 , snake_case_ )
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = []
for group_idx in range(snake_case_ ):
lowercase__ = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
lowercase__ = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
lowercase__ = range(snake_case_ , start + num_shards_to_add )
shards_indices_per_group.append(snake_case_ )
return shards_indices_per_group
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = _number_of_shards_in_gen_kwargs(snake_case_ )
if num_shards == 1:
return [dict(snake_case_ )]
else:
lowercase__ = _distribute_shards(num_shards=snake_case_ , max_num_jobs=snake_case_ )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(snake_case_ , snake_case_ )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(snake_case_ ) )
]
def a ( lowerCamelCase_ ):
'''simple docstring'''
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , snake_case_ )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = {len(snake_case_ ) for value in gen_kwargs.values() if isinstance(snake_case_ , snake_case_ )}
lowercase__ = {}
for size in list_sizes:
lowercase__ = list(range(snake_case_ ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
lowercase__ = dict(snake_case_ )
for key, value in shuffled_kwargs.items():
if isinstance(snake_case_ , snake_case_ ):
lowercase__ = [value[i] for i in indices_per_size[len(snake_case_ )]]
return shuffled_kwargs
| 719 |
from functools import reduce
A__ : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def a ( lowerCamelCase_ = N ):
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda lowerCamelCase_ , lowerCamelCase_ : str(int(lowerCamelCase_ ) * int(lowerCamelCase_ ) ) , n[i : i + 13] ) )
for i in range(len(lowerCamelCase_ ) - 12 ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 671 | 0 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
snake_case = Lock()
def SCREAMING_SNAKE_CASE__ ( snake_case__ :Optional[int] , snake_case__ :Union[str, Any] , snake_case__ :Tuple , snake_case__ :Any , snake_case__ :Dict , snake_case__ :Optional[int] , snake_case__ :List[str] ) -> Optional[Any]:
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(snake_case__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
_lowercase = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
_lowercase = min(snake_case__ , snake_case__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(snake_case__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
_lowercase = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
_lowercase = max(snake_case__ , snake_case__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(snake_case__ )
def SCREAMING_SNAKE_CASE__ ( snake_case__ :str ) -> Dict:
_lowercase = []
_lowercase = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
_lowercase = Pipe()
_lowercase = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
_lowercase = temp_rs
_lowercase = temp_rr
for i in range(1 , len(snake_case__ ) - 1 ):
_lowercase = Pipe()
_lowercase = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
_lowercase = temp_rs
_lowercase = temp_rr
process_array_.append(
Process(
target=snake_case__ , args=(
len(snake_case__ ) - 1,
arr[len(snake_case__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(snake_case__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(snake_case__ ) ):
_lowercase = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
_lowercase = list(range(10 , 0 , -1 ) )
print('Initial List' )
print(*snake_case__ )
_lowercase = odd_even_transposition(snake_case__ )
print('Sorted List\n' )
print(*snake_case__ )
if __name__ == "__main__":
main() | 67 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def a_ ( _A , _A ) -> List[Any]:
"""simple docstring"""
snake_case__ = old_name
if "patch_embed" in old_name:
snake_case__ , snake_case__ , snake_case__ = old_name.split('.' )
if layer == "0":
snake_case__ = old_name.replace('0' , 'convolution1' )
elif layer == "1":
snake_case__ = old_name.replace('1' , 'batchnorm_before' )
elif layer == "3":
snake_case__ = old_name.replace('3' , 'convolution2' )
else:
snake_case__ = old_name.replace('4' , 'batchnorm_after' )
if "network" in old_name and re.search(R'\d\.\d' , _A ):
snake_case__ = R'\b\d{2}\b'
if bool(re.search(_A , _A ) ):
snake_case__ = re.search(R'\d\.\d\d.' , _A ).group()
else:
snake_case__ = re.search(R'\d\.\d.' , _A ).group()
if int(match[0] ) < 6:
snake_case__ = old_name.replace(_A , '' )
snake_case__ = trimmed_name.replace('network' , match[0] + '.meta4D_layers.blocks.' + match[2:-1] )
snake_case__ = 'intermediate_stages.' + trimmed_name
else:
snake_case__ = old_name.replace(_A , '' )
if int(match[2] ) < num_meta4D_last_stage:
snake_case__ = trimmed_name.replace('network' , 'meta4D_layers.blocks.' + match[2] )
else:
snake_case__ = str(int(match[2] ) - num_meta4D_last_stage )
snake_case__ = trimmed_name.replace('network' , 'meta3D_layers.blocks.' + layer_index )
if "norm1" in old_name:
snake_case__ = trimmed_name.replace('norm1' , 'layernorm1' )
elif "norm2" in old_name:
snake_case__ = trimmed_name.replace('norm2' , 'layernorm2' )
elif "fc1" in old_name:
snake_case__ = trimmed_name.replace('fc1' , 'linear_in' )
elif "fc2" in old_name:
snake_case__ = trimmed_name.replace('fc2' , 'linear_out' )
snake_case__ = 'last_stage.' + trimmed_name
elif "network" in old_name and re.search(R'.\d.' , _A ):
snake_case__ = old_name.replace('network' , 'intermediate_stages' )
if "fc" in new_name:
snake_case__ = new_name.replace('fc' , 'convolution' )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
snake_case__ = new_name.replace('norm1' , 'batchnorm_before' )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
snake_case__ = new_name.replace('norm2' , 'batchnorm_after' )
if "proj" in new_name:
snake_case__ = new_name.replace('proj' , 'projection' )
if "dist_head" in new_name:
snake_case__ = new_name.replace('dist_head' , 'distillation_classifier' )
elif "head" in new_name:
snake_case__ = new_name.replace('head' , 'classifier' )
elif "patch_embed" in new_name:
snake_case__ = 'efficientformer.' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
snake_case__ = new_name.replace('norm' , 'layernorm' )
snake_case__ = 'efficientformer.' + new_name
else:
snake_case__ = 'efficientformer.encoder.' + new_name
return new_name
def a_ ( _A , _A ) -> Optional[Any]:
"""simple docstring"""
for key in checkpoint.copy().keys():
snake_case__ = checkpoint.pop(_A )
snake_case__ = val
return checkpoint
def a_ ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case__ = Image.open(requests.get(_A , stream=_A ).raw )
return image
def a_ ( _A , _A , _A , _A ) -> Optional[Any]:
"""simple docstring"""
snake_case__ = torch.load(_A , map_location='cpu' )['model']
snake_case__ = EfficientFormerConfig.from_json_file(_A )
snake_case__ = EfficientFormerForImageClassificationWithTeacher(_A )
snake_case__ = '_'.join(checkpoint_path.split('/' )[-1].split('.' )[0].split('_' )[:-1] )
snake_case__ = config.depths[-1] - config.num_metaad_blocks + 1
snake_case__ = convert_torch_checkpoint(_A , _A )
model.load_state_dict(_A )
model.eval()
snake_case__ = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
# prepare image
snake_case__ = prepare_img()
snake_case__ = 256
snake_case__ = 224
snake_case__ = EfficientFormerImageProcessor(
size={'shortest_edge': image_size} , crop_size={'height': crop_size, 'width': crop_size} , resample=pillow_resamplings['bicubic'] , )
snake_case__ = processor(images=_A , return_tensors='pt' ).pixel_values
# original processing pipeline
snake_case__ = Compose(
[
Resize(_A , interpolation=pillow_resamplings['bicubic'] ),
CenterCrop(_A ),
ToTensor(),
Normalize(_A , _A ),
] )
snake_case__ = image_transforms(_A ).unsqueeze(0 )
assert torch.allclose(_A , _A )
snake_case__ = model(_A )
snake_case__ = outputs.logits
snake_case__ = (1, 1000)
if "l1" in model_name:
snake_case__ = torch.Tensor(
[-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
assert torch.allclose(logits[0, :10] , _A , atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
snake_case__ = torch.Tensor(
[-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
assert torch.allclose(logits[0, :10] , _A , atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
snake_case__ = torch.Tensor(
[-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
assert logits.shape == expected_shape
else:
raise ValueError(
f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7''' )
# Save Checkpoints
Path(_A ).mkdir(exist_ok=_A )
model.save_pretrained(_A )
print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
processor.save_pretrained(_A )
print(f'''Processor successfuly saved at {pytorch_dump_path}''' )
if push_to_hub:
print('Pushing model to the hub...' )
model.push_to_hub(
repo_id=f'''Bearnardd/{pytorch_dump_path}''' , commit_message='Add model' , use_temp_dir=_A , )
processor.push_to_hub(
repo_id=f'''Bearnardd/{pytorch_dump_path}''' , commit_message='Add image processor' , use_temp_dir=_A , )
if __name__ == "__main__":
__UpperCamelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--pytorch_model_path""",
default=None,
type=str,
required=True,
help="""Path to EfficientFormer pytorch checkpoint.""",
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The json file for EfficientFormer model config.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
parser.add_argument(
"""--no-push_to_hub""",
dest="""push_to_hub""",
action="""store_false""",
help="""Do not push model and image processor to the hub""",
)
parser.set_defaults(push_to_hub=True)
__UpperCamelCase : Dict = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 328 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self: Dict , __UpperCamelCase: Union[str, Any] ):
'''simple docstring'''
__magic_name__ = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__magic_name__ = len(__UpperCamelCase ) - 1
def _SCREAMING_SNAKE_CASE ( self: Optional[int] , __UpperCamelCase: int ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__magic_name__ = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , __UpperCamelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__UpperCamelCase ) , 5 ) == 1
return output_values
def _SCREAMING_SNAKE_CASE ( self: Optional[int] , __UpperCamelCase: Optional[int] ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__magic_name__ = self.basis_function(__UpperCamelCase )
__magic_name__ = 0.0
__magic_name__ = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def _SCREAMING_SNAKE_CASE ( self: Any , __UpperCamelCase: List[Any] = 0.01 ):
'''simple docstring'''
from matplotlib import pyplot as plt # type: ignore
__magic_name__ = [] # x coordinates of points to plot
__magic_name__ = [] # y coordinates of points to plot
__magic_name__ = 0.0
while t <= 1:
__magic_name__ = self.bezier_curve_function(__UpperCamelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__magic_name__ = [i[0] for i in self.list_of_points]
__magic_name__ = [i[1] for i in self.list_of_points]
plt.plot(
__UpperCamelCase , __UpperCamelCase , color='blue' , label='Curve of Degree ' + str(self.degree ) , )
plt.scatter(__UpperCamelCase , __UpperCamelCase , color='red' , label='Control Points' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 717 |
from __future__ import annotations
def _lowercase ( a_ : list[int] ) -> int:
'''simple docstring'''
if not nums:
return 0
__magic_name__ = nums[0]
__magic_name__ = 0
for num in nums[1:]:
__magic_name__, __magic_name__ = (
max_excluding + num,
max(a_ ,a_ ),
)
return max(a_ ,a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 184 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
A__ : Dict = "mgp-str"
def __init__( self : List[Any] , _snake_case : str=[32, 1_28] , _snake_case : int=4 , _snake_case : List[Any]=3 , _snake_case : int=27 , _snake_case : Optional[int]=38 , _snake_case : Optional[int]=5_02_57 , _snake_case : Union[str, Any]=3_05_22 , _snake_case : Optional[Any]=7_68 , _snake_case : Dict=12 , _snake_case : Union[str, Any]=12 , _snake_case : Any=4.0 , _snake_case : Dict=True , _snake_case : List[str]=False , _snake_case : List[str]=1E-5 , _snake_case : Tuple=0.0 , _snake_case : List[str]=0.0 , _snake_case : str=0.0 , _snake_case : List[Any]=False , _snake_case : str=0.02 , **_snake_case : Optional[int] , ):
"""simple docstring"""
super().__init__(**__snake_case )
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = max_token_length
A__ = num_character_labels
A__ = num_bpe_labels
A__ = num_wordpiece_labels
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = mlp_ratio
A__ = distilled
A__ = layer_norm_eps
A__ = drop_rate
A__ = qkv_bias
A__ = attn_drop_rate
A__ = drop_path_rate
A__ = output_aa_attentions
A__ = initializer_range
| 9 | def _lowerCamelCase ( a_ : str):
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def _lowerCamelCase ( a_ : str):
lowerCamelCase :Union[str, Any] = credit_card_number
lowerCamelCase :Tuple = 0
lowerCamelCase :Any = len(a_) - 2
for i in range(a_ , -1 , -2):
# double the value of every second digit
lowerCamelCase :Any = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
lowerCamelCase :Optional[int] = cc_number[:i] + str(a_) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(a_) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def _lowerCamelCase ( a_ : str):
lowerCamelCase :Union[str, Any] = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters.")
return False
if not 13 <= len(a_) <= 16:
print(F"{error_message} of its length.")
return False
if not validate_initial_digits(a_):
print(F"{error_message} of its first two digits.")
return False
if not luhn_validation(a_):
print(F"{error_message} it fails the Luhn check.")
return False
print(F"{credit_card_number} is a valid credit card number.")
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("""4111111111111111""")
validate_credit_card_number("""32323""")
| 166 | 0 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
_lowerCamelCase = get_tests_dir('''fixtures/dummy-config.json''')
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec('''transformers.models.auto''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained('''bert-base-uncased''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Dict = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = AutoConfig.for_model('''roberta''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(_lowerCamelCase , '''fake-roberta''' )
os.makedirs(_lowerCamelCase , exist_ok=_lowerCamelCase )
with open(os.path.join(_lowerCamelCase , '''config.json''' ) , '''w''' ) as f:
f.write(json.dumps({} ) )
__SCREAMING_SNAKE_CASE : int = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertEqual(type(_lowerCamelCase ) , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
# Wrong model type will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoConfig.register('''model''' , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoConfig.register('''bert''' , _lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
__SCREAMING_SNAKE_CASE : Any = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''bert-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained('''bert-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained('''hf-internal-testing/no-config-test-repo''' )
def SCREAMING_SNAKE_CASE_ ( self :str ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Tuple = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(config.__class__.__name__ , '''NewModelConfig''' )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_config.__class__.__name__ , '''NewModelConfig''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''new-model'''
try:
AutoConfig.register('''new-model''' , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' )
self.assertEqual(config.__class__.__name__ , '''NewModelConfigLocal''' )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(config.__class__.__name__ , '''NewModelConfigLocal''' )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(config.__class__.__name__ , '''NewModelConfig''' )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 401 |
"""simple docstring"""
_lowerCamelCase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_lowerCamelCase = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_lowerCamelCase = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 401 | 1 |
'''simple docstring'''
from math import pi, sqrt
def lowercase__ ( __UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__UpperCamelCase ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(__UpperCamelCase )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowercase__ ( ):
'''simple docstring'''
assert gamma(0.5 ) == sqrt(__UpperCamelCase )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
snake_case : List[Any] = 1.0
while num:
snake_case : List[str] = float(input('Gamma of: '))
print(F"""gamma({num}) = {gamma(num)}""")
print('\nEnter 0 to exit...')
| 566 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
snake_case : Any = logging.get_logger(__name__)
snake_case : Union[str, Any] = {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def lowercase__ ( __UpperCamelCase : List[Any] , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] ):
'''simple docstring'''
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.''' )
if tokenizer_name is None:
__lowercase = TOKENIZER_CLASSES
else:
__lowercase = {tokenizer_name: getattr(__UpperCamelCase , tokenizer_name + """Fast""" )}
logger.info(F'''Loading tokenizer classes: {tokenizer_names}''' )
for tokenizer_name in tokenizer_names:
__lowercase = TOKENIZER_CLASSES[tokenizer_name]
__lowercase = True
if checkpoint_name is None:
__lowercase = list(tokenizer_class.max_model_input_sizes.keys() )
else:
__lowercase = [checkpoint_name]
logger.info(F'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}''' )
for checkpoint in checkpoint_names:
logger.info(F'''Loading {tokenizer_class.__class__.__name__} {checkpoint}''' )
# Load tokenizer
__lowercase = tokenizer_class.from_pretrained(__UpperCamelCase , force_download=__UpperCamelCase )
# Save fast tokenizer
logger.info(F'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}''' )
# For organization names we create sub-directories
if "/" in checkpoint:
__lowercase , __lowercase = checkpoint.split("""/""" )
__lowercase = os.path.join(__UpperCamelCase , __UpperCamelCase )
elif add_prefix:
__lowercase = checkpoint
__lowercase = dump_path
else:
__lowercase = None
__lowercase = dump_path
logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}''' )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
__lowercase = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
__lowercase = file_path.split(__UpperCamelCase )[-1][0]
if next_char == "/":
__lowercase = os.path.join(__UpperCamelCase , __UpperCamelCase )
__lowercase = None
logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}''' )
__lowercase = tokenizer.save_pretrained(
__UpperCamelCase , legacy_format=__UpperCamelCase , filename_prefix=__UpperCamelCase )
logger.info(F'''=> File names {file_names}''' )
for file_name in file_names:
if not file_name.endswith("""tokenizer.json""" ):
os.remove(__UpperCamelCase )
logger.info(F'''=> removing {file_name}''' )
if __name__ == "__main__":
snake_case : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
)
parser.add_argument(
'--tokenizer_name',
default=None,
type=str,
help=(
F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
'download and convert all the checkpoints from AWS.'
),
)
parser.add_argument(
'--checkpoint_name',
default=None,
type=str,
help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
)
parser.add_argument(
'--force_download',
action='store_true',
help='Re-download checkpoints.',
)
snake_case : Tuple = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 566 | 1 |
a = tuple[float, float, float]
a = tuple[float, float, float]
def UpperCamelCase_( __magic_name__ : Pointad , __magic_name__ : Pointad ):
"""simple docstring"""
_lowerCAmelCase :int = end_pointa[0] - end_pointa[0]
_lowerCAmelCase :Any = end_pointa[1] - end_pointa[1]
_lowerCAmelCase :List[Any] = end_pointa[2] - end_pointa[2]
return (x, y, z)
def UpperCamelCase_( __magic_name__ : Vectorad , __magic_name__ : Vectorad ):
"""simple docstring"""
_lowerCAmelCase :Dict = ab[1] * ac[2] - ab[2] * ac[1] # *i
_lowerCAmelCase :List[Any] = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
_lowerCAmelCase :List[Any] = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def UpperCamelCase_( __magic_name__ : Vectorad , __magic_name__ : int ):
"""simple docstring"""
return tuple(round(__magic_name__ , __magic_name__ ) for x in vector ) == (0, 0, 0)
def UpperCamelCase_( __magic_name__ : Pointad , __magic_name__ : Pointad , __magic_name__ : Pointad , __magic_name__ : int = 10 ):
"""simple docstring"""
_lowerCAmelCase :Optional[int] = create_vector(__magic_name__ , __magic_name__ )
_lowerCAmelCase :Dict = create_vector(__magic_name__ , __magic_name__ )
return is_zero_vector(get_ad_vectors_cross(__magic_name__ , __magic_name__ ) , __magic_name__ ) | 702 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
a = """base_with_context"""
def UpperCamelCase_( __magic_name__ : Tuple , __magic_name__ : List[str] ):
"""simple docstring"""
_lowerCAmelCase :Dict = nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
_lowerCAmelCase :Union[str, Any] = nn.Parameter(
torch.FloatTensor(weights['Embed_0']['embedding'] ) , requires_grad=__magic_name__ )
for lyr_num, lyr in enumerate(model.encoders ):
_lowerCAmelCase :Optional[int] = weights[f"""layers_{lyr_num}"""]
_lowerCAmelCase :Union[str, Any] = nn.Parameter(
torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )
_lowerCAmelCase :Any = ly_weight['attention']
_lowerCAmelCase :Any = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
_lowerCAmelCase :Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
_lowerCAmelCase :Any = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
_lowerCAmelCase :Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
_lowerCAmelCase :List[Any] = nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
_lowerCAmelCase :Any = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
_lowerCAmelCase :List[str] = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
_lowerCAmelCase :List[str] = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
_lowerCAmelCase :Optional[int] = nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
return model
def UpperCamelCase_( __magic_name__ : Dict , __magic_name__ : Tuple ):
"""simple docstring"""
_lowerCAmelCase :int = nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )
_lowerCAmelCase :Union[str, Any] = nn.Parameter(
torch.FloatTensor(weights['Embed_0']['embedding'] ) , requires_grad=__magic_name__ )
for lyr_num, lyr in enumerate(model.encoders ):
_lowerCAmelCase :Any = weights[f"""layers_{lyr_num}"""]
_lowerCAmelCase :str = ly_weight['attention']
_lowerCAmelCase :str = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
_lowerCAmelCase :Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
_lowerCAmelCase :Tuple = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
_lowerCAmelCase :str = nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
_lowerCAmelCase :Union[str, Any] = nn.Parameter(
torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )
_lowerCAmelCase :Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
_lowerCAmelCase :int = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
_lowerCAmelCase :Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
_lowerCAmelCase :List[str] = nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
_lowerCAmelCase :str = nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
return model
def UpperCamelCase_( __magic_name__ : Optional[Any] , __magic_name__ : Optional[int] ):
"""simple docstring"""
_lowerCAmelCase :List[str] = nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
_lowerCAmelCase :Any = nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )
_lowerCAmelCase :Dict = nn.Parameter(
torch.FloatTensor(weights['Embed_0']['embedding'] ) , requires_grad=__magic_name__ )
_lowerCAmelCase :List[Any] = nn.Parameter(
torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
_lowerCAmelCase :int = weights[f"""layers_{lyr_num}"""]
_lowerCAmelCase :Tuple = nn.Parameter(
torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale'] ) )
_lowerCAmelCase :Tuple = nn.Parameter(
torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T ) )
_lowerCAmelCase :Tuple = ly_weight['self_attention']
_lowerCAmelCase :Dict = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
_lowerCAmelCase :Tuple = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
_lowerCAmelCase :Tuple = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
_lowerCAmelCase :List[Any] = nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
_lowerCAmelCase :List[Any] = ly_weight['MultiHeadDotProductAttention_0']
_lowerCAmelCase :Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
_lowerCAmelCase :Any = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
_lowerCAmelCase :str = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
_lowerCAmelCase :str = nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
_lowerCAmelCase :Union[str, Any] = nn.Parameter(
torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale'] ) )
_lowerCAmelCase :List[str] = nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
_lowerCAmelCase :int = nn.Parameter(
torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T ) )
_lowerCAmelCase :Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
_lowerCAmelCase :List[Any] = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
_lowerCAmelCase :List[str] = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
_lowerCAmelCase :List[str] = nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale'] ) )
_lowerCAmelCase :Optional[Any] = nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T ) )
return model
def UpperCamelCase_( __magic_name__ : Optional[Any] ):
"""simple docstring"""
_lowerCAmelCase :Dict = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_lowerCAmelCase :Tuple = jnp.tree_util.tree_map(onp.array , __magic_name__ )
_lowerCAmelCase :List[str] = [
'from __gin__ import dynamic_registration',
'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
]
_lowerCAmelCase :Any = os.path.join(args.checkpoint_path , '..' , 'config.gin' )
_lowerCAmelCase :Tuple = inference.parse_training_gin_file(__magic_name__ , __magic_name__ )
_lowerCAmelCase :List[Any] = inference.InferenceModel(args.checkpoint_path , __magic_name__ )
_lowerCAmelCase :Dict = DDPMScheduler(beta_schedule='squaredcos_cap_v2' , variance_type='fixed_large' )
_lowerCAmelCase :Dict = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length['inputs'] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='gated-gelu' , )
_lowerCAmelCase :Any = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length['targets_context'] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='gated-gelu' , )
_lowerCAmelCase :Any = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length['targets_context'] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
_lowerCAmelCase :str = load_notes_encoder(ta_checkpoint['target']['token_encoder'] , __magic_name__ )
_lowerCAmelCase :Optional[int] = load_continuous_encoder(ta_checkpoint['target']['continuous_encoder'] , __magic_name__ )
_lowerCAmelCase :List[str] = load_decoder(ta_checkpoint['target']['decoder'] , __magic_name__ )
_lowerCAmelCase :Tuple = OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder' )
_lowerCAmelCase :Union[str, Any] = SpectrogramDiffusionPipeline(
notes_encoder=__magic_name__ , continuous_encoder=__magic_name__ , decoder=__magic_name__ , scheduler=__magic_name__ , melgan=__magic_name__ , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument("""--output_path""", default=None, type=str, required=True, help="""Path to the converted model.""")
parser.add_argument(
"""--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
)
parser.add_argument(
"""--checkpoint_path""",
default=F'''{MODEL}/checkpoint_500000''',
type=str,
required=False,
help="""Path to the original jax model checkpoint.""",
)
a = parser.parse_args()
main(args) | 382 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , a_ : Optional[int] , a_ : Optional[int]=12 , a_ : Dict=7 , a_ : str=True , a_ : List[Any]=True , a_ : Any=True , a_ : List[str]=99 , a_ : int=32 , a_ : Any=32 , a_ : Union[str, Any]=2 , a_ : Optional[int]=4 , a_ : Any=37 , a_ : List[str]=0.1 , a_ : int=0.1 , a_ : str=512 , a_ : Optional[Any]=0.02 , a_ : List[Any]=0 , a_ : Any=None , ):
"""simple docstring"""
__snake_case = parent
__snake_case = batch_size
__snake_case = seq_length
__snake_case = is_training
__snake_case = use_input_mask
__snake_case = use_labels
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = projection_dim
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = dropout
__snake_case = attention_dropout
__snake_case = max_position_embeddings
__snake_case = initializer_range
__snake_case = scope
__snake_case = bos_token_id
def A ( self : Tuple ):
"""simple docstring"""
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case = None
if self.use_input_mask:
__snake_case = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
__snake_case = input_mask.numpy()
__snake_case , __snake_case = input_mask.shape
__snake_case = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(a_ ):
__snake_case = 1
__snake_case = 0
__snake_case = self.get_config()
return config, input_ids, tf.convert_to_tensor(a_ )
def A ( self : List[str] ):
"""simple docstring"""
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def A ( self : Optional[int] , a_ : Union[str, Any] , a_ : List[str] , a_ : str ):
"""simple docstring"""
__snake_case = TFBlipTextModel(config=a_ )
__snake_case = model(a_ , attention_mask=a_ , training=a_ )
__snake_case = model(a_ , training=a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : str ):
"""simple docstring"""
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = (TFBlipTextModel,) if is_tf_available() else ()
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = BlipTextModelTester(self )
__snake_case = ConfigTester(self , config_class=a_ , hidden_size=37 )
def A ( self : Dict ):
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Tuple ):
"""simple docstring"""
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def A ( self : str ):
"""simple docstring"""
pass
def A ( self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def A ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def A ( self : Optional[Any] ):
"""simple docstring"""
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def A ( self : int ):
"""simple docstring"""
pass
@slow
def A ( self : Optional[int] ):
"""simple docstring"""
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case = TFBlipTextModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def A ( self : Optional[int] , a_ : int=True ):
"""simple docstring"""
super().test_pt_tf_model_equivalence(allow_missing_keys=a_ )
| 69 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = """SpeechT5FeatureExtractor"""
__SCREAMING_SNAKE_CASE = """SpeechT5Tokenizer"""
def __init__( self : List[Any] , a_ : str , a_ : str ):
"""simple docstring"""
super().__init__(a_ , a_ )
def __call__( self : Dict , *a_ : Tuple , **a_ : List[str] ):
"""simple docstring"""
__snake_case = kwargs.pop("audio" , a_ )
__snake_case = kwargs.pop("text" , a_ )
__snake_case = kwargs.pop("text_target" , a_ )
__snake_case = kwargs.pop("audio_target" , a_ )
__snake_case = kwargs.pop("sampling_rate" , a_ )
if audio is not None and text is not None:
raise ValueError(
"Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?" )
if audio_target is not None and text_target is not None:
raise ValueError(
"Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process." )
if audio is not None:
__snake_case = self.feature_extractor(a_ , *a_ , sampling_rate=a_ , **a_ )
elif text is not None:
__snake_case = self.tokenizer(a_ , **a_ )
else:
__snake_case = None
if audio_target is not None:
__snake_case = self.feature_extractor(audio_target=a_ , *a_ , sampling_rate=a_ , **a_ )
__snake_case = targets["input_values"]
elif text_target is not None:
__snake_case = self.tokenizer(a_ , **a_ )
__snake_case = targets["input_ids"]
else:
__snake_case = None
if inputs is None:
return targets
if targets is not None:
__snake_case = labels
__snake_case = targets.get("attention_mask" )
if decoder_attention_mask is not None:
__snake_case = decoder_attention_mask
return inputs
def A ( self : List[str] , *a_ : str , **a_ : Dict ):
"""simple docstring"""
__snake_case = kwargs.pop("input_values" , a_ )
__snake_case = kwargs.pop("input_ids" , a_ )
__snake_case = kwargs.pop("labels" , a_ )
if input_values is not None and input_ids is not None:
raise ValueError("Cannot process both `input_values` and `input_ids` inputs." )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded." )
if input_values is not None:
__snake_case = self.feature_extractor.pad(a_ , *a_ , **a_ )
elif input_ids is not None:
__snake_case = self.tokenizer.pad(a_ , **a_ )
else:
__snake_case = None
if labels is not None:
if "input_ids" in labels or (isinstance(a_ , a_ ) and "input_ids" in labels[0]):
__snake_case = self.tokenizer.pad(a_ , **a_ )
__snake_case = targets["input_ids"]
else:
__snake_case = self.feature_extractor.feature_size
__snake_case = self.feature_extractor.num_mel_bins
__snake_case = self.feature_extractor.pad(a_ , *a_ , **a_ )
__snake_case = feature_size_hack
__snake_case = targets["input_values"]
else:
__snake_case = None
if inputs is None:
return targets
if targets is not None:
__snake_case = labels
__snake_case = targets.get("attention_mask" )
if decoder_attention_mask is not None:
__snake_case = decoder_attention_mask
return inputs
def A ( self : List[str] , *a_ : Any , **a_ : List[str] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def A ( self : Optional[int] , *a_ : Union[str, Any] , **a_ : str ):
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
| 69 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _a ( UpperCamelCase__ ):
_lowercase : Union[str, Any] = ''''''
_lowercase : List[Any] = '''hf-legacy''' # "hf://"" is reserved for hffs
def __init__( self: Union[str, Any] , UpperCamelCase_: Optional[DatasetInfo] = None , UpperCamelCase_: Optional[str] = None , **UpperCamelCase_: int , ) -> Any:
"""simple docstring"""
super().__init__(self , **UpperCamelCase_ )
lowercase__ = repo_info
lowercase__ = token
lowercase__ = None
def lowerCamelCase_ ( self: Optional[Any] ) -> Any:
"""simple docstring"""
if self.dir_cache is None:
lowercase__ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
lowercase__ = {
'''name''': hf_file.rfilename,
'''size''': None,
'''type''': '''file''',
}
self.dir_cache.update(
{
str(UpperCamelCase_ ): {'''name''': str(UpperCamelCase_ ), '''size''': None, '''type''': '''directory'''}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: str , UpperCamelCase_: str = "rb" , **UpperCamelCase_: Tuple , ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(self.repo_info , UpperCamelCase_ ):
raise NotImplementedError(f'Open is only implemented for dataset repositories, but got {self.repo_info}' )
lowercase__ = hf_hub_url(self.repo_info.id , UpperCamelCase_ , revision=self.repo_info.sha )
return fsspec.open(
UpperCamelCase_ , mode=UpperCamelCase_ , headers=get_authentication_headers_for_url(UpperCamelCase_ , use_auth_token=self.token ) , client_kwargs={'''trust_env''': True} , ).open()
def lowerCamelCase_ ( self: Dict , UpperCamelCase_: str , **UpperCamelCase_: Any ) -> List[Any]:
"""simple docstring"""
self._get_dirs()
lowercase__ = self._strip_protocol(UpperCamelCase_ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(UpperCamelCase_ )
def lowerCamelCase_ ( self: Dict , UpperCamelCase_: List[Any] , UpperCamelCase_: Optional[Any]=False , **UpperCamelCase_: List[str] ) -> Tuple:
"""simple docstring"""
self._get_dirs()
lowercase__ = PurePosixPath(path.strip('''/''' ) )
lowercase__ = {}
for p, f in self.dir_cache.items():
lowercase__ = PurePosixPath(p.strip('''/''' ) )
lowercase__ = p.parent
if root == path:
lowercase__ = f
lowercase__ = list(paths.values() )
if detail:
return out
else:
return sorted(f['''name'''] for f in out )
| 429 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _a :
def __init__( self: str , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Optional[Any]=3 , UpperCamelCase_: int=32 , UpperCamelCase_: Union[str, Any]=3 , UpperCamelCase_: str=10 , UpperCamelCase_: Tuple=[10, 20, 30, 40] , UpperCamelCase_: str=[1, 1, 2, 1] , UpperCamelCase_: Optional[Any]=True , UpperCamelCase_: Optional[int]=True , UpperCamelCase_: str="relu" , UpperCamelCase_: Optional[Any]=3 , UpperCamelCase_: Union[str, Any]=None , ) -> Dict:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = embeddings_size
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = hidden_act
lowercase__ = num_labels
lowercase__ = scope
lowercase__ = len(UpperCamelCase_ )
def lowerCamelCase_ ( self: List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.num_labels )
lowercase__ = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self: int ) -> Dict:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: Dict , UpperCamelCase_: List[str] , UpperCamelCase_: Any ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = TFRegNetModel(config=UpperCamelCase_ )
lowercase__ = model(UpperCamelCase_ , training=UpperCamelCase_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self: str , UpperCamelCase_: Dict , UpperCamelCase_: Any , UpperCamelCase_: Any ) -> Tuple:
"""simple docstring"""
lowercase__ = self.num_labels
lowercase__ = TFRegNetForImageClassification(UpperCamelCase_ )
lowercase__ = model(UpperCamelCase_ , labels=UpperCamelCase_ , training=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self: Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _a ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
_lowercase : Optional[int] = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
_lowercase : str = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
_lowercase : Union[str, Any] = False
_lowercase : Optional[int] = False
_lowercase : List[Any] = False
_lowercase : Dict = False
_lowercase : List[str] = False
def lowerCamelCase_ ( self: Dict ) -> Dict:
"""simple docstring"""
lowercase__ = TFRegNetModelTester(self )
lowercase__ = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_ )
def lowerCamelCase_ ( self: str ) -> Optional[int]:
"""simple docstring"""
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def lowerCamelCase_ ( self: Optional[int] ) -> Any:
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def lowerCamelCase_ ( self: Tuple ) -> Dict:
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def lowerCamelCase_ ( self: Dict ) -> Optional[Any]:
"""simple docstring"""
pass
def lowerCamelCase_ ( self: Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(UpperCamelCase_ )
lowercase__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCamelCase_ )
def lowerCamelCase_ ( self: int ) -> str:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def lowerCamelCase_ ( self: List[str] ) -> Optional[Any]:
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase_: List[str] , UpperCamelCase_: Any , UpperCamelCase_: Dict ):
lowercase__ = model_class(UpperCamelCase_ )
lowercase__ = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) , training=UpperCamelCase_ )
lowercase__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase__ = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase_ ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase__ = layer_type
lowercase__ = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def lowerCamelCase_ ( self: List[str] ) -> Dict:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(UpperCamelCase_: int , UpperCamelCase_: List[Any] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Optional[Any]={} ):
lowercase__ = model(UpperCamelCase_ , return_dict=UpperCamelCase_ , **UpperCamelCase_ )
lowercase__ = model(UpperCamelCase_ , return_dict=UpperCamelCase_ , **UpperCamelCase_ ).to_tuple()
def recursive_check(UpperCamelCase_: Optional[int] , UpperCamelCase_: Any ):
if isinstance(UpperCamelCase_ , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase_ , UpperCamelCase_ ):
recursive_check(UpperCamelCase_ , UpperCamelCase_ )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(UpperCamelCase_ , UpperCamelCase_ ) ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'
) , )
recursive_check(UpperCamelCase_ , UpperCamelCase_ )
for model_class in self.all_model_classes:
lowercase__ = model_class(UpperCamelCase_ )
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ )
check_equivalence(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
check_equivalence(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ )
check_equivalence(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , {'''output_hidden_states''': True} )
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
check_equivalence(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , {'''output_hidden_states''': True} )
def lowerCamelCase_ ( self: List[Any] ) -> str:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: List[str] ) -> Dict:
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = TFRegNetModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
def _a ( ):
"""simple docstring"""
lowercase__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _a ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self: Any ) -> Optional[int]:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase_ ( self: Dict ) -> Dict:
"""simple docstring"""
lowercase__ = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=UpperCamelCase_ , return_tensors='''tf''' )
# forward pass
lowercase__ = model(**UpperCamelCase_ , training=UpperCamelCase_ )
# verify the logits
lowercase__ = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase_ )
lowercase__ = tf.constant([-0.4180, -1.5051, -3.4836] )
tf.debugging.assert_near(outputs.logits[0, :3] , UpperCamelCase_ , atol=1E-4 )
| 429 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase_ : Tuple = 'docs/source/en/_toctree.yml'
def UpperCAmelCase ( A : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Dict = defaultdict(A )
for doc in model_doc:
counts[doc["local"]] += 1
SCREAMING_SNAKE_CASE : Tuple = [key for key, value in counts.items() if value > 1]
SCREAMING_SNAKE_CASE : Tuple = []
for duplicate_key in duplicates:
SCREAMING_SNAKE_CASE : Optional[int] = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(A ) > 1:
raise ValueError(
F"""{duplicate_key} is present several times in the documentation table of content at """
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(A , key=lambda A : s["title"].lower() )
def UpperCAmelCase ( A : Any=False ):
with open(A , encoding='''utf-8''' ) as f:
SCREAMING_SNAKE_CASE : str = yaml.safe_load(f.read() )
# Get to the API doc
SCREAMING_SNAKE_CASE : Any = 0
while content[api_idx]["title"] != "API":
api_idx += 1
SCREAMING_SNAKE_CASE : Dict = content[api_idx]['''sections''']
# Then to the model doc
SCREAMING_SNAKE_CASE : List[str] = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
SCREAMING_SNAKE_CASE : Dict = api_doc[model_idx]['''sections''']
SCREAMING_SNAKE_CASE : List[Any] = [(idx, section) for idx, section in enumerate(A ) if '''sections''' in section]
SCREAMING_SNAKE_CASE : int = False
for idx, modality_doc in modalities_docs:
SCREAMING_SNAKE_CASE : List[str] = modality_doc['''sections''']
SCREAMING_SNAKE_CASE : List[str] = clean_model_doc_toc(A )
if old_modality_doc != new_modality_doc:
SCREAMING_SNAKE_CASE : Dict = True
if overwrite:
SCREAMING_SNAKE_CASE : int = new_modality_doc
if diff:
if overwrite:
SCREAMING_SNAKE_CASE : List[Any] = model_doc
SCREAMING_SNAKE_CASE : Union[str, Any] = api_doc
with open(A , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(A , allow_unicode=A ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
lowerCAmelCase_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
lowerCAmelCase_ : Any = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 527 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCamelCase_ ( snake_case_ , unittest.TestCase ):
_lowerCAmelCase : str = KandinskyInpaintPipeline
_lowerCAmelCase : List[Any] = ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
_lowerCAmelCase : int = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
_lowerCAmelCase : List[str] = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
_lowerCAmelCase : Union[str, Any] = False
@property
def __lowercase ( self : str ):
"""simple docstring"""
return 32
@property
def __lowercase ( self : str ):
"""simple docstring"""
return 32
@property
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
return self.time_input_dim
@property
def __lowercase ( self : int ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def __lowercase ( self : int ):
"""simple docstring"""
return 1_00
@property
def __lowercase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __lowercase ( self : List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
SCREAMING_SNAKE_CASE : Any = MultilingualCLIP(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = text_encoder.eval()
return text_encoder
@property
def __lowercase ( self : List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE : Union[str, Any] = UNetaDConditionModel(**lowerCAmelCase__ )
return model
@property
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self : Union[str, Any] ):
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.dummy_text_encoder
SCREAMING_SNAKE_CASE : Tuple = self.dummy_tokenizer
SCREAMING_SNAKE_CASE : int = self.dummy_unet
SCREAMING_SNAKE_CASE : str = self.dummy_movq
SCREAMING_SNAKE_CASE : Optional[int] = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=lowerCAmelCase__ , set_alpha_to_one=lowerCAmelCase__ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE : Optional[int] = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowercase ( self : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[Any]=0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(lowerCAmelCase__ )
# create init_image
SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = Image.fromarray(np.uinta(lowerCAmelCase__ ) ).convert('''RGB''' ).resize((2_56, 2_56) )
# create mask
SCREAMING_SNAKE_CASE : Union[str, Any] = np.ones((64, 64) , dtype=np.floataa )
SCREAMING_SNAKE_CASE : str = 0
if str(lowerCAmelCase__ ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __lowercase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = '''cpu'''
SCREAMING_SNAKE_CASE : Dict = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Optional[int] = self.pipeline_class(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = pipe(**self.get_dummy_inputs(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE : int = output.images
SCREAMING_SNAKE_CASE : int = pipe(
**self.get_dummy_inputs(lowerCAmelCase__ ) , return_dict=lowerCAmelCase__ , )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Dict = image_from_tuple[0, -3:, -3:, -1]
print(F"""image.shape {image.shape}""" )
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : List[str] = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __lowercase ( self : str ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
def __lowercase ( self : List[str] ):
"""simple docstring"""
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.ones((7_68, 7_68) , dtype=np.floataa )
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : Dict = '''a hat'''
SCREAMING_SNAKE_CASE : str = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE : Dict = pipeline.to(lowerCAmelCase__ )
pipeline.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Any = pipe_prior(
lowerCAmelCase__ , generator=lowerCAmelCase__ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE : Tuple = pipeline(
lowerCAmelCase__ , image=lowerCAmelCase__ , mask_image=lowerCAmelCase__ , image_embeds=lowerCAmelCase__ , negative_image_embeds=lowerCAmelCase__ , generator=lowerCAmelCase__ , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='''np''' , )
SCREAMING_SNAKE_CASE : Tuple = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(lowerCAmelCase__ , lowerCAmelCase__ )
| 527 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Union[str, Any] = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class A__ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Optional[int]=None , *_SCREAMING_SNAKE_CASE: Dict , **_SCREAMING_SNAKE_CASE: str) -> Tuple:
"""simple docstring"""
super().__init__(*UpperCamelCase__ , **UpperCamelCase__)
if config is None:
assert isinstance(self.model , UpperCamelCase__), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
__lowerCAmelCase : List[Any] = self.model.config
else:
__lowerCAmelCase : Union[str, Any] = config
__lowerCAmelCase : List[Any] = data_args
__lowerCAmelCase : str = self.config.tgt_vocab_size if isinstance(self.config , UpperCamelCase__) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding..")
if self.args.label_smoothing == 0:
__lowerCAmelCase : int = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
__lowerCAmelCase : List[str] = label_smoothed_nll_loss
def _SCREAMING_SNAKE_CASE ( self: Any , _SCREAMING_SNAKE_CASE: int) -> Dict:
"""simple docstring"""
if self.optimizer is None:
__lowerCAmelCase : str = ['''bias''', '''LayerNorm.weight''']
__lowerCAmelCase : Optional[int] = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
'''weight_decay''': 0.0,
},
]
__lowerCAmelCase : int = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
__lowerCAmelCase : str = Adafactor
__lowerCAmelCase : Optional[Any] = {'''scale_parameter''': False, '''relative_step''': False}
else:
__lowerCAmelCase : Optional[int] = AdamW
__lowerCAmelCase : Any = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
__lowerCAmelCase : Dict = self.args.learning_rate
if self.sharded_ddp:
__lowerCAmelCase : Any = OSS(
params=UpperCamelCase__ , optim=UpperCamelCase__ , **UpperCamelCase__ , )
else:
__lowerCAmelCase : Any = optimizer_cls(UpperCamelCase__ , **UpperCamelCase__)
if self.lr_scheduler is None:
__lowerCAmelCase : List[Any] = self._get_lr_scheduler(UpperCamelCase__)
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.")
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[Any]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
__lowerCAmelCase : List[str] = schedule_func(self.optimizer)
elif self.args.lr_scheduler == "constant_w_warmup":
__lowerCAmelCase : Optional[Any] = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps)
else:
__lowerCAmelCase : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=UpperCamelCase__)
return scheduler
def _SCREAMING_SNAKE_CASE ( self: Any) -> List[Any]:
"""simple docstring"""
if isinstance(self.train_dataset , torch.utils.data.IterableDataset):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset)
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset)
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset)
)
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[str]) -> Any:
"""simple docstring"""
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
__lowerCAmelCase : List[str] = model(**UpperCamelCase__ , use_cache=UpperCamelCase__)[0]
__lowerCAmelCase : List[str] = self.loss_fn(logits.view(-1 , logits.shape[-1]) , labels.view(-1))
else:
# compute usual loss via models
__lowerCAmelCase : Union[str, Any] = model(**UpperCamelCase__ , labels=UpperCamelCase__ , use_cache=UpperCamelCase__)[:2]
else:
# compute label smoothed loss
__lowerCAmelCase : List[str] = model(**UpperCamelCase__ , use_cache=UpperCamelCase__)[0]
__lowerCAmelCase : int = torch.nn.functional.log_softmax(UpperCamelCase__ , dim=-1)
__lowerCAmelCase : int = self.loss_fn(UpperCamelCase__ , UpperCamelCase__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id)
return loss, logits
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: Union[str, Any]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = inputs.pop("labels")
__lowerCAmelCase : List[Any] = self._compute_loss(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
return loss
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: nn.Module , _SCREAMING_SNAKE_CASE: Dict[str, Union[torch.Tensor, Any]] , _SCREAMING_SNAKE_CASE: bool , _SCREAMING_SNAKE_CASE: Optional[List[str]] = None , ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self._prepare_inputs(UpperCamelCase__)
__lowerCAmelCase : Dict = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
__lowerCAmelCase : Optional[Any] = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **UpperCamelCase__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
__lowerCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(UpperCamelCase__ , gen_kwargs["max_length"])
__lowerCAmelCase : Tuple = inputs.pop("labels")
with torch.no_grad():
# compute loss on predict data
__lowerCAmelCase : int = self._compute_loss(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
__lowerCAmelCase : Optional[Any] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
__lowerCAmelCase : int = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
__lowerCAmelCase : Dict = self._pad_tensors_to_max_len(UpperCamelCase__ , gen_kwargs["max_length"])
return (loss, logits, labels)
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[int]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
F""" padded to `max_length`={max_length}""")
__lowerCAmelCase : List[Any] = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device)
__lowerCAmelCase : Tuple = tensor
return padded_tensor | 712 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
__snake_case : Any = True
except (ImportError, ModuleNotFoundError):
__snake_case : Optional[int] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _lowercase ( __snake_case ) -> str:
re.sub("<n>" ,"" ,__snake_case ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__snake_case ) ) | 615 | 0 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
lowercase_ = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
print("Cuda version:", torch.version.cuda)
print("CuDNN version:", torch.backends.cudnn.version())
print("Number of GPUs available:", torch.cuda.device_count())
print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
print("Torch version:", None)
try:
import deepspeed
print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
print("DeepSpeed version:", None)
try:
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
print("TensorFlow version:", None)
| 695 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any=False ) -> Any:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__a = len(set_a.intersection(lowerCAmelCase__ ) )
if alternative_union:
__a = len(lowerCAmelCase__ ) + len(lowerCAmelCase__ )
else:
__a = len(set_a.union(lowerCAmelCase__ ) )
return intersection / union
if isinstance(lowerCAmelCase__ , (list, tuple) ) and isinstance(lowerCAmelCase__ , (list, tuple) ):
__a = [element for element in set_a if element in set_b]
if alternative_union:
__a = len(lowerCAmelCase__ ) + len(lowerCAmelCase__ )
return len(lowerCAmelCase__ ) / union
else:
__a = set_a + [element for element in set_b if element not in set_a]
return len(lowerCAmelCase__ ) / len(lowerCAmelCase__ )
return len(lowerCAmelCase__ ) / len(lowerCAmelCase__ )
return None
if __name__ == "__main__":
lowercase_ = {"a", "b", "c", "d", "e"}
lowercase_ = {"c", "d", "e", "f", "h", "i"}
print(jaccard_similarity(set_a, set_b))
| 695 | 1 |
import argparse
import os
import re
A__ : Dict = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
A__ : List[Any] = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
A__ : Union[str, Any] = re.compile(r'\s*\(\s*"(\S[^"]+)"')
def a ( lowerCamelCase_ , lowerCamelCase_ = False ):
'''simple docstring'''
with open(lowerCamelCase_ , '''r''' , encoding='''utf-8''' ) as f:
lowercase__ = f.read()
lowercase__ = content.split('''\n''' )
lowercase__ = []
lowercase__ = 0
while line_idx < len(lowerCamelCase_ ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowercase__ = len(re.search(r'''^(\s*)\S''' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowercase__ = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowercase__ = line_idx
while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
line_idx += 1
blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowercase__ = sorted(lowerCamelCase_ , key=lambda lowerCamelCase_ : _re_identifier.search(lowerCamelCase_ ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(lowerCamelCase_ , '''w''' , encoding='''utf-8''' ) as f:
f.write('''\n'''.join(lowerCamelCase_ ) )
elif "\n".join(lowerCamelCase_ ) != content:
return True
def a ( lowerCamelCase_ = False ):
'''simple docstring'''
lowercase__ = [os.path.join(lowerCamelCase_ , lowerCamelCase_ ) for f in os.listdir(lowerCamelCase_ ) if f.endswith('''.py''' )]
lowercase__ = [sort_auto_mapping(lowerCamelCase_ , overwrite=lowerCamelCase_ ) for fname in fnames]
if not overwrite and any(lowerCamelCase_ ):
lowercase__ = [f for f, d in zip(lowerCamelCase_ , lowerCamelCase_ ) if d]
raise ValueError(
F"""The following files have auto mappings that need sorting: {', '.join(lowerCamelCase_ )}. Run `make style` to fix"""
''' this.''' )
if __name__ == "__main__":
A__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
A__ : Dict = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 700 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A__ : Optional[int] = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
A__ : List[str] = {
'bert-base-uncased': 5_12,
'bert-large-uncased': 5_12,
'bert-base-cased': 5_12,
'bert-large-cased': 5_12,
'bert-base-multilingual-uncased': 5_12,
'bert-base-multilingual-cased': 5_12,
'bert-base-chinese': 5_12,
'bert-base-german-cased': 5_12,
'bert-large-uncased-whole-word-masking': 5_12,
'bert-large-cased-whole-word-masking': 5_12,
'bert-large-uncased-whole-word-masking-finetuned-squad': 5_12,
'bert-large-cased-whole-word-masking-finetuned-squad': 5_12,
'bert-base-cased-finetuned-mrpc': 5_12,
'bert-base-german-dbmdz-cased': 5_12,
'bert-base-german-dbmdz-uncased': 5_12,
'TurkuNLP/bert-base-finnish-cased-v1': 5_12,
'TurkuNLP/bert-base-finnish-uncased-v1': 5_12,
'wietsedv/bert-base-dutch-cased': 5_12,
}
A__ : Optional[int] = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_INIT_CONFIGURATION
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = BertTokenizer
def __init__( self : Any, lowerCamelCase : Optional[Any]=None, lowerCamelCase : Any=None, lowerCamelCase : Tuple=True, lowerCamelCase : Dict="[UNK]", lowerCamelCase : Any="[SEP]", lowerCamelCase : List[Any]="[PAD]", lowerCamelCase : Optional[Any]="[CLS]", lowerCamelCase : Dict="[MASK]", lowerCamelCase : List[Any]=True, lowerCamelCase : Tuple=None, **lowerCamelCase : Dict, ):
'''simple docstring'''
super().__init__(
lowerCamelCase, tokenizer_file=lowerCamelCase, do_lower_case=lowerCamelCase, unk_token=lowerCamelCase, sep_token=lowerCamelCase, pad_token=lowerCamelCase, cls_token=lowerCamelCase, mask_token=lowerCamelCase, tokenize_chinese_chars=lowerCamelCase, strip_accents=lowerCamelCase, **lowerCamelCase, )
lowercase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''', lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''', lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''', lowerCamelCase ) != tokenize_chinese_chars
):
lowercase__ = getattr(lowerCamelCase, normalizer_state.pop('''type''' ) )
lowercase__ = do_lower_case
lowercase__ = strip_accents
lowercase__ = tokenize_chinese_chars
lowercase__ = normalizer_class(**lowerCamelCase )
lowercase__ = do_lower_case
def lowercase__ ( self : Any, lowerCamelCase : List[Any], lowerCamelCase : Dict=None ):
'''simple docstring'''
lowercase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowercase__ ( self : List[Any], lowerCamelCase : List[int], lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = [self.sep_token_id]
lowercase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase__ ( self : Any, lowerCamelCase : str, lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
lowercase__ = self._tokenizer.model.save(lowerCamelCase, name=lowerCamelCase )
return tuple(lowerCamelCase )
| 671 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class A__ ( nn.Module ):
_UpperCAmelCase :int
_UpperCAmelCase :jnp.dtype = jnp.floataa
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , A_ ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Tuple = hidden_states.shape
UpperCamelCase : Union[str, Any] = jax.image.resize(
A_ , shape=(batch, height * 2, width * 2, channels) , method="nearest" , )
UpperCamelCase : Optional[Any] = self.conv(A_ )
return hidden_states
class A__ ( nn.Module ):
_UpperCAmelCase :int
_UpperCAmelCase :jnp.dtype = jnp.floataa
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Dict = self.conv(A_ )
return hidden_states
class A__ ( nn.Module ):
_UpperCAmelCase :int
_UpperCAmelCase :int = None
_UpperCAmelCase :float = 0.0
_UpperCAmelCase :bool = None
_UpperCAmelCase :jnp.dtype = jnp.floataa
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = self.in_channels if self.out_channels is None else self.out_channels
UpperCamelCase : List[str] = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
UpperCamelCase : int = nn.Conv(
A_ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
UpperCamelCase : int = nn.Dense(A_ , dtype=self.dtype )
UpperCamelCase : Tuple = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
UpperCamelCase : Optional[Any] = nn.Dropout(self.dropout_prob )
UpperCamelCase : Tuple = nn.Conv(
A_ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
UpperCamelCase : str = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
UpperCamelCase : List[Any] = None
if use_nin_shortcut:
UpperCamelCase : Union[str, Any] = nn.Conv(
A_ , kernel_size=(1, 1) , strides=(1, 1) , padding="VALID" , dtype=self.dtype , )
def __call__( self , A_ , A_ , A_=True ):
'''simple docstring'''
UpperCamelCase : Optional[int] = hidden_states
UpperCamelCase : Any = self.norma(A_ )
UpperCamelCase : Tuple = nn.swish(A_ )
UpperCamelCase : List[str] = self.conva(A_ )
UpperCamelCase : List[str] = self.time_emb_proj(nn.swish(A_ ) )
UpperCamelCase : Union[str, Any] = jnp.expand_dims(jnp.expand_dims(A_ , 1 ) , 1 )
UpperCamelCase : Dict = hidden_states + temb
UpperCamelCase : Optional[int] = self.norma(A_ )
UpperCamelCase : Tuple = nn.swish(A_ )
UpperCamelCase : Optional[Any] = self.dropout(A_ , A_ )
UpperCamelCase : Optional[Any] = self.conva(A_ )
if self.conv_shortcut is not None:
UpperCamelCase : Union[str, Any] = self.conv_shortcut(A_ )
return hidden_states + residual
| 629 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class A__ :
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=True , A_=True , A_=99 , A_=64 , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ):
'''simple docstring'''
UpperCamelCase : Dict = parent
UpperCamelCase : int = batch_size
UpperCamelCase : Dict = seq_length
UpperCamelCase : Union[str, Any] = is_training
UpperCamelCase : int = use_input_mask
UpperCamelCase : Optional[Any] = use_token_type_ids
UpperCamelCase : Optional[Any] = use_labels
UpperCamelCase : str = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Any = embedding_size
UpperCamelCase : List[Any] = num_hidden_layers
UpperCamelCase : Optional[int] = num_attention_heads
UpperCamelCase : Optional[Any] = intermediate_size
UpperCamelCase : Dict = hidden_act
UpperCamelCase : Union[str, Any] = hidden_dropout_prob
UpperCamelCase : Optional[Any] = attention_probs_dropout_prob
UpperCamelCase : List[str] = max_position_embeddings
UpperCamelCase : List[Any] = type_vocab_size
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : Optional[int] = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : List[str] = num_choices
UpperCamelCase : Tuple = scope
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : List[str] = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase : Dict = None
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase( self ):
'''simple docstring'''
return MegatronBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A_ , initializer_range=self.initializer_range , )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Optional[int] = MegatronBertModel(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Optional[Any] = model(A_ , attention_mask=A_ , token_type_ids=A_ )
UpperCamelCase : Dict = model(A_ , token_type_ids=A_ )
UpperCamelCase : Union[str, Any] = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : List[str] = MegatronBertForMaskedLM(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Optional[int] = model(A_ , attention_mask=A_ , token_type_ids=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : int = MegatronBertForCausalLM(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Dict = model(A_ , attention_mask=A_ , token_type_ids=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : int = MegatronBertForNextSentencePrediction(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Dict = model(
A_ , attention_mask=A_ , token_type_ids=A_ , labels=A_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Optional[int] = MegatronBertForPreTraining(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : str = model(
A_ , attention_mask=A_ , token_type_ids=A_ , labels=A_ , next_sentence_label=A_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : str = MegatronBertForQuestionAnswering(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(
A_ , attention_mask=A_ , token_type_ids=A_ , start_positions=A_ , end_positions=A_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : List[str] = self.num_labels
UpperCamelCase : Optional[int] = MegatronBertForSequenceClassification(A_ )
model.to(A_ )
model.eval()
UpperCamelCase : List[Any] = model(A_ , attention_mask=A_ , token_type_ids=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = self.num_labels
UpperCamelCase : List[str] = MegatronBertForTokenClassification(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : List[str] = model(A_ , attention_mask=A_ , token_type_ids=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : int = self.num_choices
UpperCamelCase : int = MegatronBertForMultipleChoice(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
A_ , attention_mask=A_ , token_type_ids=A_ , labels=A_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Any = config_and_inputs
UpperCamelCase : Optional[int] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A__ ( __snake_case , __snake_case , unittest.TestCase ):
_UpperCAmelCase :Tuple = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase :Optional[Any] = (
{
'feature-extraction': MegatronBertModel,
'fill-mask': MegatronBertForMaskedLM,
'question-answering': MegatronBertForQuestionAnswering,
'text-classification': MegatronBertForSequenceClassification,
'text-generation': MegatronBertForCausalLM,
'token-classification': MegatronBertForTokenClassification,
'zero-shot': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase :Optional[Any] = True
# test_resize_embeddings = False
_UpperCAmelCase :Optional[Any] = False
def __UpperCamelCase( self , A_ , A_ , A_=False ):
'''simple docstring'''
UpperCamelCase : Any = super()._prepare_for_class(A_ , A_ , return_labels=A_ )
if return_labels:
if model_class in get_values(A_ ):
UpperCamelCase : str = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=A_ )
UpperCamelCase : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A_ )
return inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = MegatronBertModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=A_ , hidden_size=37 )
def __UpperCamelCase( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*A_ )
def A_ ( _lowerCAmelCase ) -> int:
return torch.tensor(
_lowerCAmelCase , dtype=torch.long , device=_lowerCAmelCase , )
__lowerCamelCase : Optional[int] = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class A__ ( unittest.TestCase ):
@slow
@unittest.skip("Model is not available." )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = "nvidia/megatron-bert-uncased-345m"
if "MYDIR" in os.environ:
UpperCamelCase : Union[str, Any] = os.path.join(os.environ["MYDIR"] , A_ )
UpperCamelCase : List[Any] = MegatronBertModel.from_pretrained(A_ )
model.to(A_ )
model.half()
UpperCamelCase : Optional[Any] = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
UpperCamelCase : Tuple = model(A_ )[0]
UpperCamelCase : Dict = torch.Size((1, 9, 1024) )
self.assertEqual(output.shape , A_ )
UpperCamelCase : Union[str, Any] = [-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
for ii in range(3 ):
for jj in range(3 ):
UpperCamelCase : List[str] = output[0, ii, jj]
UpperCamelCase : List[Any] = expected[3 * ii + jj]
UpperCamelCase : Optional[Any] = "ii={} jj={} a={} b={}".format(A_ , A_ , A_ , A_ )
self.assertTrue(math.isclose(A_ , A_ , rel_tol=A_ , abs_tol=A_ ) , msg=A_ )
| 629 | 1 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = None
if token is not None:
_SCREAMING_SNAKE_CASE = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
_SCREAMING_SNAKE_CASE = F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
_SCREAMING_SNAKE_CASE = requests.get(SCREAMING_SNAKE_CASE_ , headers=SCREAMING_SNAKE_CASE_ ).json()
_SCREAMING_SNAKE_CASE = {}
try:
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
_SCREAMING_SNAKE_CASE = math.ceil((result["""total_count"""] - 1_00) / 1_00 )
for i in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = requests.get(url + F"&page={i + 2}" , headers=SCREAMING_SNAKE_CASE_ ).json()
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return job_links
except Exception:
print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )
return {}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = None
if token is not None:
_SCREAMING_SNAKE_CASE = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
_SCREAMING_SNAKE_CASE = F"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
_SCREAMING_SNAKE_CASE = requests.get(SCREAMING_SNAKE_CASE_ , headers=SCREAMING_SNAKE_CASE_ ).json()
_SCREAMING_SNAKE_CASE = {}
try:
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
_SCREAMING_SNAKE_CASE = math.ceil((result["""total_count"""] - 1_00) / 1_00 )
for i in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = requests.get(url + F"&page={i + 2}" , headers=SCREAMING_SNAKE_CASE_ ).json()
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
return artifacts
except Exception:
print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )
return {}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = None
if token is not None:
_SCREAMING_SNAKE_CASE = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
_SCREAMING_SNAKE_CASE = requests.get(SCREAMING_SNAKE_CASE_ , headers=SCREAMING_SNAKE_CASE_ , allow_redirects=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = result.headers["""Location"""]
_SCREAMING_SNAKE_CASE = requests.get(SCREAMING_SNAKE_CASE_ , allow_redirects=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = os.path.join(SCREAMING_SNAKE_CASE_ , F"{artifact_name}.zip" )
with open(SCREAMING_SNAKE_CASE_ , """wb""" ) as fp:
fp.write(response.content )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = None
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ ) as z:
for filename in z.namelist():
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(SCREAMING_SNAKE_CASE_ ) as f:
for line in f:
_SCREAMING_SNAKE_CASE = line.decode("""UTF-8""" ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
_SCREAMING_SNAKE_CASE = line[: line.index(""": """ )]
_SCREAMING_SNAKE_CASE = line[line.index(""": """ ) + len(""": """ ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith("""FAILED """ ):
# `test` is the test method that failed
_SCREAMING_SNAKE_CASE = line[len("""FAILED """ ) :]
failed_tests.append(SCREAMING_SNAKE_CASE_ )
elif filename == "job_name.txt":
_SCREAMING_SNAKE_CASE = line
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
F"`errors` and `failed_tests` should have the same number of elements. Got {len(SCREAMING_SNAKE_CASE_ )} for `errors` "
F"and {len(SCREAMING_SNAKE_CASE_ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"
""" problem.""" )
_SCREAMING_SNAKE_CASE = None
if job_name and job_links:
_SCREAMING_SNAKE_CASE = job_links.get(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# A list with elements of the form (line of error, error, failed test)
_SCREAMING_SNAKE_CASE = [x + [y] + [job_link] for x, y in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )]
return result
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = [os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for p in os.listdir(SCREAMING_SNAKE_CASE_ ) if p.endswith(""".zip""" )]
for p in paths:
errors.extend(get_errors_from_single_artifact(SCREAMING_SNAKE_CASE_ , job_links=SCREAMING_SNAKE_CASE_ ) )
return errors
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Counter()
counter.update([x[1] for x in logs] )
_SCREAMING_SNAKE_CASE = counter.most_common()
_SCREAMING_SNAKE_CASE = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
_SCREAMING_SNAKE_CASE = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}
_SCREAMING_SNAKE_CASE = dict(sorted(r.items() , key=lambda SCREAMING_SNAKE_CASE_ : item[1]["count"] , reverse=SCREAMING_SNAKE_CASE_ ) )
return r
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = test.split("""::""" )[0]
if test.startswith("""tests/models/""" ):
_SCREAMING_SNAKE_CASE = test.split("""/""" )[2]
else:
_SCREAMING_SNAKE_CASE = None
return test
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [(x[0], x[1], get_model(x[2] )) for x in logs]
_SCREAMING_SNAKE_CASE = [x for x in logs if x[2] is not None]
_SCREAMING_SNAKE_CASE = {x[2] for x in logs}
_SCREAMING_SNAKE_CASE = {}
for test in tests:
_SCREAMING_SNAKE_CASE = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
_SCREAMING_SNAKE_CASE = counter.most_common()
_SCREAMING_SNAKE_CASE = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
_SCREAMING_SNAKE_CASE = sum(error_counts.values() )
if n_errors > 0:
_SCREAMING_SNAKE_CASE = {"""count""": n_errors, """errors""": error_counts}
_SCREAMING_SNAKE_CASE = dict(sorted(r.items() , key=lambda SCREAMING_SNAKE_CASE_ : item[1]["count"] , reverse=SCREAMING_SNAKE_CASE_ ) )
return r
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """| no. | error | status |"""
_SCREAMING_SNAKE_CASE = """|-:|:-|:-|"""
_SCREAMING_SNAKE_CASE = [header, sep]
for error in reduced_by_error:
_SCREAMING_SNAKE_CASE = reduced_by_error[error]["""count"""]
_SCREAMING_SNAKE_CASE = F"| {count} | {error[:1_00]} | |"
lines.append(SCREAMING_SNAKE_CASE_ )
return "\n".join(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """| model | no. of errors | major error | count |"""
_SCREAMING_SNAKE_CASE = """|-:|-:|-:|-:|"""
_SCREAMING_SNAKE_CASE = [header, sep]
for model in reduced_by_model:
_SCREAMING_SNAKE_CASE = reduced_by_model[model]["""count"""]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = list(reduced_by_model[model]["""errors"""].items() )[0]
_SCREAMING_SNAKE_CASE = F"| {model} | {count} | {error[:60]} | {_count} |"
lines.append(SCREAMING_SNAKE_CASE_ )
return "\n".join(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
UpperCamelCase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Where to store the downloaded artifacts and other result files.",
)
parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
UpperCamelCase__ : str = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
UpperCamelCase__ : List[Any] = get_job_links(args.workflow_run_id, token=args.token)
UpperCamelCase__ : List[str] = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
UpperCamelCase__ : Dict = k.find(" / ")
UpperCamelCase__ : Optional[Any] = k[index + len(" / ") :]
UpperCamelCase__ : List[str] = v
with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
UpperCamelCase__ : List[str] = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
UpperCamelCase__ : List[str] = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
UpperCamelCase__ : str = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
UpperCamelCase__ : List[str] = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
UpperCamelCase__ : Optional[Any] = reduce_by_error(errors)
UpperCamelCase__ : Optional[int] = reduce_by_model(errors)
UpperCamelCase__ : Optional[int] = make_github_table(reduced_by_error)
UpperCamelCase__ : Optional[Any] = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
| 715 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("""https://huggingface.co""" )
| 0 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __lowercase ( unittest.TestCase ):
def __init__( self , a__ , a__=7 , a__=3 , a__=1_8 , a__=3_0 , a__=4_0_0 , a__=True , a__=None , a__=True , ) -> Optional[int]:
'''simple docstring'''
A_ = size if size is not None else {'''height''': 1_8, '''width''': 1_8}
A_ = parent
A_ = batch_size
A_ = num_channels
A_ = image_size
A_ = min_resolution
A_ = max_resolution
A_ = do_resize
A_ = size
A_ = apply_ocr
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class __lowercase ( A , unittest.TestCase ):
__magic_name__ : List[Any] = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def lowerCAmelCase_ ( self ) -> str:
'''simple docstring'''
A_ = LayoutLMvaImageProcessingTester(self )
@property
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase_ ( self ) -> List[str]:
'''simple docstring'''
A_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a__ , '''do_resize''' ) )
self.assertTrue(hasattr(a__ , '''size''' ) )
self.assertTrue(hasattr(a__ , '''apply_ocr''' ) )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 1_8, '''width''': 1_8} )
A_ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 )
self.assertEqual(image_processor.size , {'''height''': 4_2, '''width''': 4_2} )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
# Initialize image_processing
A_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=a__ )
for image in image_inputs:
self.assertIsInstance(a__ , Image.Image )
# Test not batched input
A_ = image_processing(image_inputs[0] , return_tensors='''pt''' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
self.assertIsInstance(encoding.words , a__ )
self.assertIsInstance(encoding.boxes , a__ )
# Test batched
A_ = image_processing(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# Initialize image_processing
A_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=a__ , numpify=a__ )
for image in image_inputs:
self.assertIsInstance(a__ , np.ndarray )
# Test not batched input
A_ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
A_ = image_processing(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
# Initialize image_processing
A_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=a__ , torchify=a__ )
for image in image_inputs:
self.assertIsInstance(a__ , torch.Tensor )
# Test not batched input
A_ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
A_ = image_processing(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# with apply_OCR = True
A_ = LayoutLMvaImageProcessor()
from datasets import load_dataset
A_ = load_dataset('''hf-internal-testing/fixtures_docvqa''' , split='''test''' )
A_ = Image.open(ds[0]['''file'''] ).convert('''RGB''' )
A_ = image_processing(a__ , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
A_ = [['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']] # noqa: E231
A_ = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , a__ )
self.assertListEqual(encoding.boxes , a__ )
# with apply_OCR = False
A_ = LayoutLMvaImageProcessor(apply_ocr=a__ )
A_ = image_processing(a__ , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) ) | 141 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class __lowercase ( A , A , unittest.TestCase ):
__magic_name__ : List[str] = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__magic_name__ : Optional[Any] = (
{
'''feature-extraction''': TFMobileBertModel,
'''fill-mask''': TFMobileBertForMaskedLM,
'''question-answering''': TFMobileBertForQuestionAnswering,
'''text-classification''': TFMobileBertForSequenceClassification,
'''token-classification''': TFMobileBertForTokenClassification,
'''zero-shot''': TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__magic_name__ : Optional[int] = False
__magic_name__ : List[Any] = False
def lowerCAmelCase_ ( self , a__ , a__ , a__=False ) -> Union[str, Any]:
'''simple docstring'''
A_ = super()._prepare_for_class(a__ , a__ , return_labels=a__ )
if return_labels:
if model_class in get_values(a__ ):
A_ = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class __lowercase ( A ):
def __init__( self , a__ , a__=1_3 , a__=7 , a__=True , a__=True , a__=True , a__=True , a__=9_9 , a__=3_2 , a__=3_2 , a__=2 , a__=4 , a__=3_7 , a__="gelu" , a__=0.1 , a__=0.1 , a__=5_1_2 , a__=1_6 , a__=2 , a__=0.02 , a__=3 , a__=4 , a__=None , ) -> List[str]:
'''simple docstring'''
A_ = parent
A_ = batch_size
A_ = seq_length
A_ = is_training
A_ = use_input_mask
A_ = use_token_type_ids
A_ = use_labels
A_ = vocab_size
A_ = hidden_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = max_position_embeddings
A_ = type_vocab_size
A_ = type_sequence_label_size
A_ = initializer_range
A_ = num_labels
A_ = num_choices
A_ = scope
A_ = embedding_size
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = None
if self.use_input_mask:
A_ = random_attention_mask([self.batch_size, self.seq_length] )
A_ = None
if self.use_token_type_ids:
A_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ = None
A_ = None
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ = ids_tensor([self.batch_size] , self.num_choices )
A_ = MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Dict:
'''simple docstring'''
A_ = TFMobileBertModel(config=a__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(a__ )
A_ = [input_ids, input_mask]
A_ = model(a__ )
A_ = model(a__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCAmelCase_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Optional[int]:
'''simple docstring'''
A_ = TFMobileBertForMaskedLM(config=a__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> int:
'''simple docstring'''
A_ = TFMobileBertForNextSentencePrediction(config=a__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCAmelCase_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> List[str]:
'''simple docstring'''
A_ = TFMobileBertForPreTraining(config=a__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(a__ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCAmelCase_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> List[Any]:
'''simple docstring'''
A_ = self.num_labels
A_ = TFMobileBertForSequenceClassification(config=a__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Any:
'''simple docstring'''
A_ = self.num_choices
A_ = TFMobileBertForMultipleChoice(config=a__ )
A_ = tf.tile(tf.expand_dims(a__ , 1 ) , (1, self.num_choices, 1) )
A_ = tf.tile(tf.expand_dims(a__ , 1 ) , (1, self.num_choices, 1) )
A_ = tf.tile(tf.expand_dims(a__ , 1 ) , (1, self.num_choices, 1) )
A_ = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
A_ = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Optional[Any]:
'''simple docstring'''
A_ = self.num_labels
A_ = TFMobileBertForTokenClassification(config=a__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Any:
'''simple docstring'''
A_ = TFMobileBertForQuestionAnswering(config=a__ )
A_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
A_ = model(a__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
A_ = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) = config_and_inputs
A_ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
def lowerCAmelCase_ ( self ) -> str:
'''simple docstring'''
A_ = TFMobileBertModelTest.TFMobileBertModelTester(self )
A_ = ConfigTester(self , config_class=a__ , hidden_size=3_7 )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self ) -> int:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*a__ )
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*a__ )
def lowerCAmelCase_ ( self ) -> List[str]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*a__ )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*a__ )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*a__ )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*a__ )
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*a__ )
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*a__ )
@slow
def lowerCAmelCase_ ( self ) -> int:
'''simple docstring'''
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
A_ = TFMobileBertModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
@require_tf
class __lowercase ( unittest.TestCase ):
@slow
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
A_ = TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased''' )
A_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
A_ = model(a__ )[0]
A_ = [1, 6, 3_0_5_2_2]
self.assertEqual(output.shape , a__ )
A_ = tf.constant(
[
[
[-4.5_91_95_47, -9.24_82_95, -9.64_52_56],
[-6.7_30_61_75, -6.44_02_84, -6.6_05_28_37],
[-7.2_74_35_06, -6.7_84_79_15, -6.02_46_73],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , a__ , atol=1E-4 ) | 141 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
# Initialise PyTorch model
lowerCamelCase : int =TaConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
print(F"Building PyTorch model from configuration: {config}" )
lowerCamelCase : List[str] =TaForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
snake_case_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 262 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
snake_case_ = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
snake_case_ = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
snake_case_ = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def A__ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
def remove_articles(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Tuple =re.compile(R'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(SCREAMING_SNAKE_CASE_ , ''' ''' , SCREAMING_SNAKE_CASE_ )
def white_space_fix(SCREAMING_SNAKE_CASE_ ):
return " ".join(text.split() )
def remove_punc(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : int =set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(SCREAMING_SNAKE_CASE_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_ ) ) ) )
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
return int(normalize_answer(SCREAMING_SNAKE_CASE_ ) == normalize_answer(SCREAMING_SNAKE_CASE_ ) )
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
lowerCamelCase : Union[str, Any] =[any(compute_exact(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for ref in refs ) for pred, refs in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )]
return (sum(SCREAMING_SNAKE_CASE_ ) / len(SCREAMING_SNAKE_CASE_ )) * 1_0_0
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
lowerCamelCase : Any =[rgram for rgrams in rgramslist for rgram in rgrams]
lowerCamelCase : int =Counter(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Dict =Counter(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Any =Counter()
for sgram, scount in sgramcounter.items():
lowerCamelCase : Tuple =scount * numref
lowerCamelCase : Optional[int] =Counter(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Tuple =Counter()
for cgram, ccount in cgramcounter.items():
lowerCamelCase : Tuple =ccount * numref
# KEEP
lowerCamelCase : str =sgramcounter_rep & cgramcounter_rep
lowerCamelCase : Union[str, Any] =keepgramcounter_rep & rgramcounter
lowerCamelCase : Optional[Any] =sgramcounter_rep & rgramcounter
lowerCamelCase : Optional[Any] =0
lowerCamelCase : List[Any] =0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
lowerCamelCase : Tuple =1
lowerCamelCase : int =1
if len(SCREAMING_SNAKE_CASE_ ) > 0:
lowerCamelCase : Tuple =keeptmpscorea / len(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
lowerCamelCase : Any =keeptmpscorea / sum(keepgramcounterall_rep.values() )
lowerCamelCase : Optional[Any] =0
if keepscore_precision > 0 or keepscore_recall > 0:
lowerCamelCase : Optional[int] =2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
lowerCamelCase : int =sgramcounter_rep - cgramcounter_rep
lowerCamelCase : Dict =delgramcounter_rep - rgramcounter
lowerCamelCase : Dict =sgramcounter_rep - rgramcounter
lowerCamelCase : Optional[int] =0
lowerCamelCase : List[Any] =0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
lowerCamelCase : str =1
if len(SCREAMING_SNAKE_CASE_ ) > 0:
lowerCamelCase : Optional[int] =deltmpscorea / len(SCREAMING_SNAKE_CASE_ )
# ADDITION
lowerCamelCase : List[Any] =set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : int =set(SCREAMING_SNAKE_CASE_ ) & set(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[Any] =set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : int =0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
lowerCamelCase : int =1
lowerCamelCase : List[Any] =1
if len(SCREAMING_SNAKE_CASE_ ) > 0:
lowerCamelCase : str =addtmpscore / len(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
lowerCamelCase : List[str] =addtmpscore / len(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[Any] =0
if addscore_precision > 0 or addscore_recall > 0:
lowerCamelCase : Optional[Any] =2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
lowerCamelCase : Optional[int] =len(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Dict =ssent.split(''' ''' )
lowerCamelCase : Any =csent.split(''' ''' )
lowerCamelCase : str =[]
lowerCamelCase : Optional[Any] =[]
lowerCamelCase : List[Any] =[]
lowerCamelCase : List[str] =[]
lowerCamelCase : Tuple =[]
lowerCamelCase : Optional[Any] =[]
lowerCamelCase : int =[]
lowerCamelCase : List[str] =[]
lowerCamelCase : Dict =[]
lowerCamelCase : Any =[]
for rsent in rsents:
lowerCamelCase : Any =rsent.split(''' ''' )
lowerCamelCase : int =[]
lowerCamelCase : Optional[Any] =[]
lowerCamelCase : List[Any] =[]
ragramslist.append(SCREAMING_SNAKE_CASE_ )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 ):
if i < len(SCREAMING_SNAKE_CASE_ ) - 1:
lowerCamelCase : Optional[int] =ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(SCREAMING_SNAKE_CASE_ )
if i < len(SCREAMING_SNAKE_CASE_ ) - 2:
lowerCamelCase : str =ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(SCREAMING_SNAKE_CASE_ )
if i < len(SCREAMING_SNAKE_CASE_ ) - 3:
lowerCamelCase : int =ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(SCREAMING_SNAKE_CASE_ )
ragramslist.append(SCREAMING_SNAKE_CASE_ )
ragramslist.append(SCREAMING_SNAKE_CASE_ )
ragramslist.append(SCREAMING_SNAKE_CASE_ )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 ):
if i < len(SCREAMING_SNAKE_CASE_ ) - 1:
lowerCamelCase : Optional[int] =sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(SCREAMING_SNAKE_CASE_ )
if i < len(SCREAMING_SNAKE_CASE_ ) - 2:
lowerCamelCase : List[Any] =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(SCREAMING_SNAKE_CASE_ )
if i < len(SCREAMING_SNAKE_CASE_ ) - 3:
lowerCamelCase : Optional[int] =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(SCREAMING_SNAKE_CASE_ )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 ):
if i < len(SCREAMING_SNAKE_CASE_ ) - 1:
lowerCamelCase : Optional[int] =cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(SCREAMING_SNAKE_CASE_ )
if i < len(SCREAMING_SNAKE_CASE_ ) - 2:
lowerCamelCase : List[str] =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(SCREAMING_SNAKE_CASE_ )
if i < len(SCREAMING_SNAKE_CASE_ ) - 3:
lowerCamelCase : str =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(SCREAMING_SNAKE_CASE_ )
((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) : Any =SARIngram(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) : Optional[Any] =SARIngram(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) : List[Any] =SARIngram(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) : List[str] =SARIngram(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[Any] =sum([keepascore, keepascore, keepascore, keepascore] ) / 4
lowerCamelCase : List[str] =sum([delascore, delascore, delascore, delascore] ) / 4
lowerCamelCase : int =sum([addascore, addascore, addascore, addascore] ) / 4
lowerCamelCase : Any =(avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = "13a" , SCREAMING_SNAKE_CASE_ = True ) -> Any:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
lowerCamelCase : Union[str, Any] =sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
lowerCamelCase : List[Any] =sacrebleu.metrics.bleu._get_tokenizer(SCREAMING_SNAKE_CASE_ )()(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase : Any =sacrebleu.TOKENIZERS[tokenizer]()(SCREAMING_SNAKE_CASE_ )
elif tokenizer == "moses":
lowerCamelCase : int =sacremoses.MosesTokenizer().tokenize(SCREAMING_SNAKE_CASE_ , return_str=SCREAMING_SNAKE_CASE_ , escape=SCREAMING_SNAKE_CASE_ )
elif tokenizer == "penn":
lowerCamelCase : Any =sacremoses.MosesTokenizer().penn_tokenize(SCREAMING_SNAKE_CASE_ , return_str=SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase : Optional[int] =sentence
if not return_str:
lowerCamelCase : Union[str, Any] =normalized_sent.split()
return normalized_sent
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
if not (len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
lowerCamelCase : Dict =0
for src, pred, refs in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
sari_score += SARIsent(normalize(SCREAMING_SNAKE_CASE_ ) , normalize(SCREAMING_SNAKE_CASE_ ) , [normalize(SCREAMING_SNAKE_CASE_ ) for sent in refs] )
lowerCamelCase : str =sari_score / len(SCREAMING_SNAKE_CASE_ )
return 1_0_0 * sari_score
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="exp" , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , ) -> Dict:
lowerCamelCase : Optional[int] =len(references[0] )
if any(len(SCREAMING_SNAKE_CASE_ ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
lowerCamelCase : Optional[int] =[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_ )]
lowerCamelCase : Union[str, Any] =sacrebleu.corpus_bleu(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , smooth_method=SCREAMING_SNAKE_CASE_ , smooth_value=SCREAMING_SNAKE_CASE_ , force=SCREAMING_SNAKE_CASE_ , lowercase=SCREAMING_SNAKE_CASE_ , use_effective_order=SCREAMING_SNAKE_CASE_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class snake_case_ ( datasets.Metric):
def __lowercase ( self ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def __lowercase ( self , __lowercase , __lowercase , __lowercase ) -> Tuple:
lowerCamelCase : str ={}
result.update({'''sari''': compute_sari(sources=__lowercase , predictions=__lowercase , references=__lowercase )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=__lowercase , references=__lowercase )} )
result.update({'''exact''': compute_em(predictions=__lowercase , references=__lowercase )} )
return result
| 262 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = [[1, 2, 4], [1, 2, 3, 4]]
__SCREAMING_SNAKE_CASE : Tuple = DisjunctiveConstraint(_A )
self.assertTrue(isinstance(dc.token_ids , _A ) )
with self.assertRaises(_A ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(_A ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(_A ):
DisjunctiveConstraint(_A ) # fails here
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = [[1, 2, 3], [1, 2, 4]]
__SCREAMING_SNAKE_CASE : Optional[Any] = DisjunctiveConstraint(_A )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : List[Any] = dc.update(1 )
__SCREAMING_SNAKE_CASE : int = stepped is True and completed is False and reset is False
self.assertTrue(_A )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Dict = dc.update(2 )
__SCREAMING_SNAKE_CASE : Optional[Any] = stepped is True and completed is False and reset is False
self.assertTrue(_A )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : List[str] = dc.update(3 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = stepped is True and completed is True and reset is False
self.assertTrue(_A )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def UpperCAmelCase__ ( self : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
__SCREAMING_SNAKE_CASE : str = DisjunctiveConstraint(_A )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Dict = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : List[Any] = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : str = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Tuple = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Optional[int] = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 74 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2''' , revision='''bf16''' , dtype=jnp.bfloataa , )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''A painting of a squirrel eating a burger'''
__SCREAMING_SNAKE_CASE : int = jax.device_count()
__SCREAMING_SNAKE_CASE : Tuple = num_samples * [prompt]
__SCREAMING_SNAKE_CASE : Optional[Any] = sd_pipe.prepare_inputs(_A )
__SCREAMING_SNAKE_CASE : Tuple = replicate(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = shard(_A )
__SCREAMING_SNAKE_CASE : Dict = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE : Optional[int] = jax.random.split(_A , jax.device_count() )
__SCREAMING_SNAKE_CASE : str = sd_pipe(_A , _A , _A , num_inference_steps=25 , jit=_A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
__SCREAMING_SNAKE_CASE : List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 253:256, 253:256, -1]
__SCREAMING_SNAKE_CASE : Dict = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__SCREAMING_SNAKE_CASE : Tuple = jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = '''stabilityai/stable-diffusion-2'''
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Dict = FlaxDPMSolverMultistepScheduler.from_pretrained(_A , subfolder='''scheduler''' )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = FlaxStableDiffusionPipeline.from_pretrained(
_A , scheduler=_A , revision='''bf16''' , dtype=jnp.bfloataa , )
__SCREAMING_SNAKE_CASE : List[str] = scheduler_params
__SCREAMING_SNAKE_CASE : Tuple = '''A painting of a squirrel eating a burger'''
__SCREAMING_SNAKE_CASE : List[Any] = jax.device_count()
__SCREAMING_SNAKE_CASE : Tuple = num_samples * [prompt]
__SCREAMING_SNAKE_CASE : Any = sd_pipe.prepare_inputs(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = replicate(_A )
__SCREAMING_SNAKE_CASE : List[str] = shard(_A )
__SCREAMING_SNAKE_CASE : int = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = jax.random.split(_A , jax.device_count() )
__SCREAMING_SNAKE_CASE : List[Any] = sd_pipe(_A , _A , _A , num_inference_steps=25 , jit=_A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
__SCREAMING_SNAKE_CASE : Tuple = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__SCREAMING_SNAKE_CASE : Dict = images[0, 253:256, 253:256, -1]
__SCREAMING_SNAKE_CASE : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 74 | 1 |
def UpperCamelCase ( lowerCAmelCase_ ) -> str:
'''simple docstring'''
_A= 0
# if input_string is "aba" than new_input_string become "a|b|a"
_A= ''
_A= ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(lowerCAmelCase_ ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
_A, _A= 0, 0
# length[i] shows the length of palindromic substring with center i
_A= [1 for i in range(len(lowerCAmelCase_ ) )]
# for each character in new_string find corresponding palindromic string
_A= 0
for j in range(len(lowerCAmelCase_ ) ):
_A= 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(lowerCAmelCase_ )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
_A= 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
_A= j - k + 1 # noqa: E741
_A= j + k - 1
# update max_length and start position
if max_length < length[j]:
_A= length[j]
_A= j
# create that string
_A= new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod() | 476 | UpperCAmelCase_ = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',
'''dataclasses''': '''dataclasses''',
'''datasets''': '''datasets!=2.5.0''',
'''decord''': '''decord==0.6.0''',
'''deepspeed''': '''deepspeed>=0.9.3''',
'''diffusers''': '''diffusers''',
'''dill''': '''dill<0.3.5''',
'''evaluate''': '''evaluate>=0.2.0''',
'''fairscale''': '''fairscale>0.3''',
'''faiss-cpu''': '''faiss-cpu''',
'''fastapi''': '''fastapi''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1,<=0.7.0''',
'''ftfy''': '''ftfy''',
'''fugashi''': '''fugashi>=1.0''',
'''GitPython''': '''GitPython<3.1.19''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''ipadic''': '''ipadic>=1.0.0,<2.0''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
'''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
'''jieba''': '''jieba''',
'''kenlm''': '''kenlm''',
'''keras-nlp''': '''keras-nlp>=0.3.1''',
'''librosa''': '''librosa''',
'''nltk''': '''nltk''',
'''natten''': '''natten>=0.14.6''',
'''numpy''': '''numpy>=1.17''',
'''onnxconverter-common''': '''onnxconverter-common''',
'''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
'''onnxruntime''': '''onnxruntime>=1.4.0''',
'''opencv-python''': '''opencv-python''',
'''optuna''': '''optuna''',
'''optax''': '''optax>=0.0.8,<=0.1.4''',
'''packaging''': '''packaging>=20.0''',
'''parameterized''': '''parameterized''',
'''phonemizer''': '''phonemizer''',
'''protobuf''': '''protobuf''',
'''psutil''': '''psutil''',
'''pyyaml''': '''pyyaml>=5.1''',
'''pydantic''': '''pydantic<2''',
'''pytest''': '''pytest>=7.2.0''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''python''': '''python>=3.8.0''',
'''ray[tune]''': '''ray[tune]''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
'''rjieba''': '''rjieba''',
'''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
'''ruff''': '''ruff>=0.0.241,<=0.0.259''',
'''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
'''sacremoses''': '''sacremoses''',
'''safetensors''': '''safetensors>=0.3.1''',
'''sagemaker''': '''sagemaker>=2.31.0''',
'''scikit-learn''': '''scikit-learn''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''sigopt''': '''sigopt''',
'''starlette''': '''starlette''',
'''sudachipy''': '''sudachipy>=0.6.6''',
'''sudachidict_core''': '''sudachidict_core>=20220729''',
'''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
'''tensorflow''': '''tensorflow>=2.6,<2.14''',
'''tensorflow-text''': '''tensorflow-text<2.14''',
'''tf2onnx''': '''tf2onnx''',
'''timeout-decorator''': '''timeout-decorator''',
'''timm''': '''timm''',
'''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
'''torch''': '''torch>=1.9,!=1.12.0''',
'''torchaudio''': '''torchaudio''',
'''torchvision''': '''torchvision''',
'''pyctcdecode''': '''pyctcdecode>=0.4.0''',
'''tqdm''': '''tqdm>=4.27''',
'''unidic''': '''unidic>=1.0.2''',
'''unidic_lite''': '''unidic_lite>=1.0.7''',
'''urllib3''': '''urllib3<2.0.0''',
'''uvicorn''': '''uvicorn''',
} | 476 | 1 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
assert isinstance(__UpperCamelCase , __UpperCamelCase ), F'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number < 1:
snake_case_ : Union[str, Any] = F'The input value of [n={number}] has to be > 0'
raise ValueError(__UpperCamelCase )
else:
snake_case_ : Tuple = sylvester(number - 1 )
snake_case_ : Any = num - 1
snake_case_ : Optional[Any] = num
return lower * upper + 1
if __name__ == "__main__":
print(F'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')
| 58 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : list[list[int]] , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : list[int] ):
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def __lowerCAmelCase ( __UpperCamelCase : list[list[int]] , __UpperCamelCase : list[int] , __UpperCamelCase : int ):
'''simple docstring'''
if curr_ind == len(__UpperCamelCase ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(__UpperCamelCase ) ):
if valid_connection(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# Insert current vertex into path as next transition
snake_case_ : List[str] = next_ver
# Validate created path
if util_hamilton_cycle(__UpperCamelCase , __UpperCamelCase , curr_ind + 1 ):
return True
# Backtrack
snake_case_ : Tuple = -1
return False
def __lowerCAmelCase ( __UpperCamelCase : list[list[int]] , __UpperCamelCase : int = 0 ):
'''simple docstring'''
snake_case_ : Tuple = [-1] * (len(__UpperCamelCase ) + 1)
# initialize start and end of path with starting index
snake_case_ : Optional[int] = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(__UpperCamelCase , __UpperCamelCase , 1 ) else []
| 58 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class UpperCamelCase (__snake_case , __snake_case , __snake_case , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Any = StableUnCLIPImgaImgPipeline
_SCREAMING_SNAKE_CASE : Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
_SCREAMING_SNAKE_CASE : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_SCREAMING_SNAKE_CASE : int = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_SCREAMING_SNAKE_CASE : Tuple = frozenset([] )
def __snake_case ( self :Union[str, Any] ) ->Tuple:
lowercase : Dict = 32
lowercase : Tuple = embedder_hidden_size
# image encoding components
lowercase : Tuple = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
lowercase : Union[str, Any] = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=__magic_name__ , projection_dim=__magic_name__ , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
lowercase : Tuple = StableUnCLIPImageNormalizer(embedding_dim=__magic_name__ )
lowercase : Optional[int] = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" )
torch.manual_seed(0 )
lowercase : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
lowercase : Tuple = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__magic_name__ , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
lowercase : Dict = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="""projection""" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__magic_name__ , layers_per_block=1 , upcast_attention=__magic_name__ , use_linear_projection=__magic_name__ , )
torch.manual_seed(0 )
lowercase : Dict = DDIMScheduler(
beta_schedule="""scaled_linear""" , beta_start=0.0_0085 , beta_end=0.012 , prediction_type="""v_prediction""" , set_alpha_to_one=__magic_name__ , steps_offset=1 , )
torch.manual_seed(0 )
lowercase : Tuple = AutoencoderKL()
lowercase : Union[str, Any] = {
# image encoding components
"""feature_extractor""": feature_extractor,
"""image_encoder""": image_encoder.eval(),
# image noising components
"""image_normalizer""": image_normalizer.eval(),
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder.eval(),
"""unet""": unet.eval(),
"""scheduler""": scheduler,
"""vae""": vae.eval(),
}
return components
def __snake_case ( self :Optional[int] , __magic_name__ :Optional[Any] , __magic_name__ :List[Any]=0 , __magic_name__ :List[Any]=True ) ->List[str]:
if str(__magic_name__ ).startswith("""mps""" ):
lowercase : Dict = torch.manual_seed(__magic_name__ )
else:
lowercase : List[str] = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
lowercase : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
if pil_image:
lowercase : str = input_image * 0.5 + 0.5
lowercase : Optional[Any] = input_image.clamp(0 , 1 )
lowercase : Dict = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowercase : List[Any] = DiffusionPipeline.numpy_to_pil(__magic_name__ )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def __snake_case ( self :Any ) ->int:
lowercase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase : Union[str, Any] = self.get_dummy_components()
lowercase : Union[str, Any] = StableUnCLIPImgaImgPipeline(**__magic_name__ )
lowercase : int = sd_pipe.to(__magic_name__ )
sd_pipe.set_progress_bar_config(disable=__magic_name__ )
lowercase : List[Any] = self.get_dummy_inputs(__magic_name__ )
inputs.update({"""image_embeds""": None} )
lowercase : List[str] = sd_pipe(**__magic_name__ ).images
lowercase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase : int = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __snake_case ( self :str ) ->str:
lowercase : str = torch_device in ["""cpu""", """mps"""]
self._test_attention_slicing_forward_pass(test_max_difference=__magic_name__ )
def __snake_case ( self :List[Any] ) ->Tuple:
lowercase : int = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=__magic_name__ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __snake_case ( self :List[Any] ) ->Dict:
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__magic_name__ )
@slow
@require_torch_gpu
class UpperCamelCase (unittest.TestCase ):
def __snake_case ( self :Any ) ->str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case ( self :Union[str, Any] ) ->str:
lowercase : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
lowercase : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy""" )
lowercase : Union[str, Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-l-img2img""" , torch_dtype=torch.floataa )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowercase : Optional[int] = pipe(__magic_name__ , """anime turle""" , generator=__magic_name__ , output_type="""np""" )
lowercase : int = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__magic_name__ , __magic_name__ )
def __snake_case ( self :Any ) ->List[str]:
lowercase : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
lowercase : Optional[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy""" )
lowercase : Optional[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase : List[str] = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowercase : Dict = pipe(__magic_name__ , """anime turle""" , generator=__magic_name__ , output_type="""np""" )
lowercase : int = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__magic_name__ , __magic_name__ )
def __snake_case ( self :Union[str, Any] ) ->Tuple:
lowercase : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase : Any = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa )
lowercase : Dict = pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase : Dict = pipe(
__magic_name__ , """anime turtle""" , num_inference_steps=2 , output_type="""np""" , )
lowercase : Optional[int] = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 702 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def UpperCamelCase ( _A ) -> List[List[ImageInput]]:
if isinstance(_A , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(_A , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(_A ):
return [[videos]]
raise ValueError(F"""Could not make batched video from {videos}""" )
class UpperCamelCase (__snake_case ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = ["""pixel_values"""]
def __init__( self :Union[str, Any] , __magic_name__ :bool = True , __magic_name__ :Dict[str, int] = None , __magic_name__ :PILImageResampling = PILImageResampling.BILINEAR , __magic_name__ :bool = True , __magic_name__ :Dict[str, int] = None , __magic_name__ :bool = True , __magic_name__ :Union[int, float] = 1 / 255 , __magic_name__ :bool = True , __magic_name__ :bool = True , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[Union[float, List[float]]] = None , **__magic_name__ :List[str] , ) ->None:
super().__init__(**__magic_name__ )
lowercase : str = size if size is not None else {"""shortest_edge""": 256}
lowercase : Union[str, Any] = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
lowercase : str = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowercase : Union[str, Any] = get_size_dict(__magic_name__ , param_name="""crop_size""" )
lowercase : Union[str, Any] = do_resize
lowercase : Any = size
lowercase : int = do_center_crop
lowercase : Any = crop_size
lowercase : Tuple = resample
lowercase : str = do_rescale
lowercase : Tuple = rescale_factor
lowercase : Optional[Any] = offset
lowercase : Any = do_normalize
lowercase : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __snake_case ( self :Optional[int] , __magic_name__ :np.ndarray , __magic_name__ :Dict[str, int] , __magic_name__ :PILImageResampling = PILImageResampling.BILINEAR , __magic_name__ :Optional[Union[str, ChannelDimension]] = None , **__magic_name__ :Any , ) ->np.ndarray:
lowercase : Any = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
if "shortest_edge" in size:
lowercase : Union[str, Any] = get_resize_output_image_size(__magic_name__ , size["""shortest_edge"""] , default_to_square=__magic_name__ )
elif "height" in size and "width" in size:
lowercase : List[str] = (size["""height"""], size["""width"""])
else:
raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(__magic_name__ , size=__magic_name__ , resample=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __snake_case ( self :int , __magic_name__ :np.ndarray , __magic_name__ :Dict[str, int] , __magic_name__ :Optional[Union[str, ChannelDimension]] = None , **__magic_name__ :Dict , ) ->np.ndarray:
lowercase : Any = get_size_dict(__magic_name__ )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(__magic_name__ , size=(size["""height"""], size["""width"""]) , data_format=__magic_name__ , **__magic_name__ )
def __snake_case ( self :Dict , __magic_name__ :np.ndarray , __magic_name__ :Union[int, float] , __magic_name__ :bool = True , __magic_name__ :Optional[Union[str, ChannelDimension]] = None , **__magic_name__ :Union[str, Any] , ) ->Union[str, Any]:
lowercase : Dict = image.astype(np.floataa )
if offset:
lowercase : List[str] = image - (scale / 2)
return rescale(__magic_name__ , scale=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __snake_case ( self :Dict , __magic_name__ :np.ndarray , __magic_name__ :Union[float, List[float]] , __magic_name__ :Union[float, List[float]] , __magic_name__ :Optional[Union[str, ChannelDimension]] = None , **__magic_name__ :Union[str, Any] , ) ->np.ndarray:
return normalize(__magic_name__ , mean=__magic_name__ , std=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __snake_case ( self :Tuple , __magic_name__ :ImageInput , __magic_name__ :bool = None , __magic_name__ :Dict[str, int] = None , __magic_name__ :PILImageResampling = None , __magic_name__ :bool = None , __magic_name__ :Dict[str, int] = None , __magic_name__ :bool = None , __magic_name__ :float = None , __magic_name__ :bool = None , __magic_name__ :bool = None , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[ChannelDimension] = ChannelDimension.FIRST , ) ->np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
lowercase : Union[str, Any] = to_numpy_array(__magic_name__ )
if do_resize:
lowercase : int = self.resize(image=__magic_name__ , size=__magic_name__ , resample=__magic_name__ )
if do_center_crop:
lowercase : Union[str, Any] = self.center_crop(__magic_name__ , size=__magic_name__ )
if do_rescale:
lowercase : Dict = self.rescale(image=__magic_name__ , scale=__magic_name__ , offset=__magic_name__ )
if do_normalize:
lowercase : Tuple = self.normalize(image=__magic_name__ , mean=__magic_name__ , std=__magic_name__ )
lowercase : List[Any] = to_channel_dimension_format(__magic_name__ , __magic_name__ )
return image
def __snake_case ( self :Optional[int] , __magic_name__ :ImageInput , __magic_name__ :bool = None , __magic_name__ :Dict[str, int] = None , __magic_name__ :PILImageResampling = None , __magic_name__ :bool = None , __magic_name__ :Dict[str, int] = None , __magic_name__ :bool = None , __magic_name__ :float = None , __magic_name__ :bool = None , __magic_name__ :bool = None , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[Union[str, TensorType]] = None , __magic_name__ :ChannelDimension = ChannelDimension.FIRST , **__magic_name__ :Any , ) ->PIL.Image.Image:
lowercase : List[str] = do_resize if do_resize is not None else self.do_resize
lowercase : Optional[int] = resample if resample is not None else self.resample
lowercase : int = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
lowercase : int = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : Optional[Any] = offset if offset is not None else self.offset
lowercase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
lowercase : Any = image_mean if image_mean is not None else self.image_mean
lowercase : Optional[int] = image_std if image_std is not None else self.image_std
lowercase : List[Any] = size if size is not None else self.size
lowercase : str = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
lowercase : str = crop_size if crop_size is not None else self.crop_size
lowercase : List[Any] = get_size_dict(__magic_name__ , param_name="""crop_size""" )
if not valid_images(__magic_name__ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowercase : int = make_batched(__magic_name__ )
lowercase : Dict = [
[
self._preprocess_image(
image=__magic_name__ , do_resize=__magic_name__ , size=__magic_name__ , resample=__magic_name__ , do_center_crop=__magic_name__ , crop_size=__magic_name__ , do_rescale=__magic_name__ , rescale_factor=__magic_name__ , offset=__magic_name__ , do_normalize=__magic_name__ , image_mean=__magic_name__ , image_std=__magic_name__ , data_format=__magic_name__ , )
for img in video
]
for video in videos
]
lowercase : List[str] = {"""pixel_values""": videos}
return BatchFeature(data=__magic_name__ , tensor_type=__magic_name__ )
| 348 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCAmelCase__ =logging.getLogger(__name__)
def _a ( ) -> Tuple:
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser(
description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).''' )
parser.add_argument('''--file_path''' , type=lowerCamelCase__ , default='''data/dump.txt''' , help='''The path to the data.''' )
parser.add_argument('''--tokenizer_type''' , type=lowerCamelCase__ , default='''bert''' , choices=['''bert''', '''roberta''', '''gpt2'''] )
parser.add_argument('''--tokenizer_name''' , type=lowerCamelCase__ , default='''bert-base-uncased''' , help='''The tokenizer to use.''' )
parser.add_argument('''--dump_file''' , type=lowerCamelCase__ , default='''data/dump''' , help='''The dump file prefix.''' )
__SCREAMING_SNAKE_CASE = parser.parse_args()
logger.info(f"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
__SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained(args.tokenizer_name )
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map['''cls_token'''] # `[CLS]`
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map['''sep_token'''] # `[SEP]`
elif args.tokenizer_type == "roberta":
__SCREAMING_SNAKE_CASE = RobertaTokenizer.from_pretrained(args.tokenizer_name )
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map['''cls_token'''] # `<s>`
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map['''sep_token'''] # `</s>`
elif args.tokenizer_type == "gpt2":
__SCREAMING_SNAKE_CASE = GPTaTokenizer.from_pretrained(args.tokenizer_name )
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map['''bos_token'''] # `<|endoftext|>`
__SCREAMING_SNAKE_CASE = tokenizer.special_tokens_map['''eos_token'''] # `<|endoftext|>`
logger.info(f"""Loading text from {args.file_path}""" )
with open(args.file_path , '''r''' , encoding='''utf8''' ) as fp:
__SCREAMING_SNAKE_CASE = fp.readlines()
logger.info('''Start encoding''' )
logger.info(f"""{len(lowerCamelCase__ )} examples to process.""" )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1_00_00
__SCREAMING_SNAKE_CASE = time.time()
for text in data:
__SCREAMING_SNAKE_CASE = f"""{bos} {text.strip()} {sep}"""
__SCREAMING_SNAKE_CASE = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
rslt.append(lowerCamelCase__ )
iter += 1
if iter % interval == 0:
__SCREAMING_SNAKE_CASE = time.time()
logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
__SCREAMING_SNAKE_CASE = time.time()
logger.info('''Finished binarization''' )
logger.info(f"""{len(lowerCamelCase__ )} examples processed.""" )
__SCREAMING_SNAKE_CASE = f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
__SCREAMING_SNAKE_CASE = tokenizer.vocab_size
if vocab_size < (1 << 16):
__SCREAMING_SNAKE_CASE = [np.uintaa(lowerCamelCase__ ) for d in rslt]
else:
__SCREAMING_SNAKE_CASE = [np.intaa(lowerCamelCase__ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(f"""Dump to {dp_file}""" )
with open(lowerCamelCase__ , '''wb''' ) as handle:
pickle.dump(rslt_ , lowerCamelCase__ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 482 | """simple docstring"""
from itertools import permutations
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowerCAmelCase__ = [7, 11, 13, 17]
for i, test in enumerate(lowerCamelCase__ ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def _UpperCAmelCase ( lowerCamelCase__ = 10 ):
"""simple docstring"""
return sum(
int("""""".join(map(lowerCamelCase__ , lowerCamelCase__ ) ) )
for num in permutations(range(lowerCamelCase__ ) )
if is_substring_divisible(lowerCamelCase__ ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 644 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase : List[Any] = """src/diffusers"""
lowercase : Dict = """."""
# This is to make sure the diffusers module imported is the one in the repo.
lowercase : Dict = importlib.util.spec_from_file_location(
"""diffusers""",
os.path.join(DIFFUSERS_PATH, """__init__.py"""),
submodule_search_locations=[DIFFUSERS_PATH],
)
lowercase : Optional[Any] = spec.loader.load_module()
def A_ ( A__ , A__ ) -> List[Any]:
return line.startswith(A__ ) or len(A__ ) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$' , A__ ) is not None
def A_ ( A__ ) -> Tuple:
a__ : Union[str, Any] = object_name.split('.' )
a__ : Optional[Any] = 0
# First let's find the module where our object lives.
a__ : Union[str, Any] = parts[i]
while i < len(A__ ) and not os.path.isfile(os.path.join(A__ , F'{module}.py' ) ):
i += 1
if i < len(A__ ):
a__ : Optional[Any] = os.path.join(A__ , parts[i] )
if i >= len(A__ ):
raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.' )
with open(os.path.join(A__ , F'{module}.py' ) , 'r' , encoding='utf-8' , newline='\n' ) as f:
a__ : Optional[int] = f.readlines()
# Now let's find the class / func in the code!
a__ : List[Any] = ''
a__ : Tuple = 0
for name in parts[i + 1 :]:
while (
line_index < len(A__ ) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)' , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(A__ ):
raise ValueError(F' {object_name} does not match any function or class in {module}.' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
a__ : str = line_index
while line_index < len(A__ ) and _should_continue(lines[line_index] , A__ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
a__ : str = lines[start_index:line_index]
return "".join(A__ )
lowercase : Dict = re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
lowercase : Optional[Any] = re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
lowercase : Union[str, Any] = re.compile(r"""<FILL\s+[^>]*>""")
def A_ ( A__ ) -> int:
a__ : Any = code.split('\n' )
a__ : Any = 0
while idx < len(A__ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(A__ ):
return re.search(R'^(\s*)\S' , lines[idx] ).groups()[0]
return ""
def A_ ( A__ ) -> Tuple:
a__ : List[Any] = len(get_indent(A__ ) ) > 0
if has_indent:
a__ : Tuple = F'class Bla:\n{code}'
a__ : int = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=A__ )
a__ : Dict = black.format_str(A__ , mode=A__ )
a__ , a__ : Dict = style_docstrings_in_code(A__ )
return result[len('class Bla:\n' ) :] if has_indent else result
def A_ ( A__ , A__=False ) -> Dict:
with open(A__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
a__ : Dict = f.readlines()
a__ : List[Any] = []
a__ : List[str] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(A__ ):
a__ : Dict = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
a__ , a__ , a__ : Any = search.groups()
a__ : Tuple = find_code_in_diffusers(A__ )
a__ : List[str] = get_indent(A__ )
a__ : Optional[Any] = line_index + 1 if indent == theoretical_indent else line_index + 2
a__ : Union[str, Any] = theoretical_indent
a__ : List[Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
a__ : Dict = True
while line_index < len(A__ ) and should_continue:
line_index += 1
if line_index >= len(A__ ):
break
a__ : Dict = lines[line_index]
a__ : Optional[int] = _should_continue(A__ , A__ ) and re.search(F'^{indent}# End copy' , A__ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
a__ : int = lines[start_index:line_index]
a__ : Optional[Any] = ''.join(A__ )
# Remove any nested `Copied from` comments to avoid circular copies
a__ : Tuple = [line for line in theoretical_code.split('\n' ) if _re_copy_warning.search(A__ ) is None]
a__ : Optional[Any] = '\n'.join(A__ )
# Before comparing, use the `replace_pattern` on the original code.
if len(A__ ) > 0:
a__ : Optional[Any] = replace_pattern.replace('with' , '' ).split(',' )
a__ : Optional[int] = [_re_replace_pattern.search(A__ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
a__ , a__ , a__ : List[str] = pattern.groups()
a__ : int = re.sub(A__ , A__ , A__ )
if option.strip() == "all-casing":
a__ : Any = re.sub(obja.lower() , obja.lower() , A__ )
a__ : str = re.sub(obja.upper() , obja.upper() , A__ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
a__ : List[str] = blackify(lines[start_index - 1] + theoretical_code )
a__ : Optional[Any] = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
a__ : Dict = lines[:start_index] + [theoretical_code] + lines[line_index:]
a__ : Optional[Any] = start_index + 1
if overwrite and len(A__ ) > 0:
# Warn the user a file has been modified.
print(F'Detected changes, rewriting {filename}.' )
with open(A__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(A__ )
return diffs
def A_ ( A__ = False ) -> str:
a__ : Tuple = glob.glob(os.path.join(A__ , '**/*.py' ) , recursive=A__ )
a__ : Any = []
for filename in all_files:
a__ : Union[str, Any] = is_copy_consistent(A__ , A__ )
diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
if not overwrite and len(A__ ) > 0:
a__ : Optional[Any] = '\n'.join(A__ )
raise Exception(
'Found the following copy inconsistencies:\n'
+ diff
+ '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.' )
if __name__ == "__main__":
lowercase : str = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
lowercase : Optional[int] = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 392 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : List[Any] = '''vit_msn'''
def __init__( self , lowercase=768 , lowercase=12 , lowercase=12 , lowercase=3072 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1e-06 , lowercase=224 , lowercase=16 , lowercase=3 , lowercase=True , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(**lowercase)
a__ : Tuple = hidden_size
a__ : Optional[Any] = num_hidden_layers
a__ : str = num_attention_heads
a__ : Optional[Any] = intermediate_size
a__ : Optional[Any] = hidden_act
a__ : int = hidden_dropout_prob
a__ : Optional[int] = attention_probs_dropout_prob
a__ : List[Any] = initializer_range
a__ : Optional[int] = layer_norm_eps
a__ : List[str] = image_size
a__ : Optional[int] = patch_size
a__ : List[str] = num_channels
a__ : Dict = qkv_bias
| 392 | 1 |
import os
import pytest
from attr import dataclass
_lowercase = """us-east-1""" # defaults region
@dataclass
class lowercase_ :
__lowerCamelCase = 42
__lowerCamelCase = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
__lowerCamelCase = {
"task_name": "mnli",
"per_device_train_batch_size": 1_6,
"per_device_eval_batch_size": 1_6,
"do_train": True,
"do_eval": True,
"do_predict": True,
"output_dir": "/opt/ml/model",
"overwrite_output_dir": True,
"max_steps": 5_0_0,
"save_steps": 5_5_0_0,
}
__lowerCamelCase = {**hyperparameters, "max_steps": 1_0_0_0}
@property
def _snake_case ( self ) -> str:
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def _snake_case ( self ) -> str:
return F'{self.framework}-transfromers-test'
@property
def _snake_case ( self ) -> str:
return F'./tests/sagemaker/scripts/{self.framework}'
@property
def _snake_case ( self ) -> str:
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope='''class''' )
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =SageMakerTestEnvironment(framework=request.cls.framework )
| 443 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : list[int | float] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int | float:
if len(UpperCAmelCase_ ) == 0:
raise ValueError('''find_max() arg is an empty sequence''' )
if (
left >= len(UpperCAmelCase_ )
or left < -len(UpperCAmelCase_ )
or right >= len(UpperCAmelCase_ )
or right < -len(UpperCAmelCase_ )
):
raise IndexError('''list index out of range''' )
if left == right:
return nums[left]
SCREAMING_SNAKE_CASE_ : Optional[int] =(left + right) >> 1 # the middle
SCREAMING_SNAKE_CASE_ : Dict =find_max(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ) # find max in range[left, mid]
SCREAMING_SNAKE_CASE_ : Dict =find_max(UpperCAmelCase_ , mid + 1 , UpperCAmelCase_ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 443 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
_A = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCamelCase_ ( datasets.BuilderConfig ):
_lowerCamelCase : int = 10000
_lowerCamelCase : Optional[List[str]] = None
_lowerCamelCase : Optional[datasets.Features] = None
class lowerCamelCase_ ( datasets.ArrowBasedBuilder ):
_lowerCamelCase : Optional[Any] = ParquetConfig
def __magic_name__ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE ):
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
a_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_SCREAMING_SNAKE_CASE , (str, list, tuple) ):
a_ = data_files
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
a_ = [dl_manager.iter_files(_SCREAMING_SNAKE_CASE ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
a_ = []
for split_name, files in data_files.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
a_ = [dl_manager.iter_files(_SCREAMING_SNAKE_CASE ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ):
with open(_SCREAMING_SNAKE_CASE , """rb""" ) as f:
a_ = datasets.Features.from_arrow_schema(pq.read_schema(_SCREAMING_SNAKE_CASE ) )
break
splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE , gen_kwargs={"""files""": files} ) )
return splits
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE ):
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
a_ = table_cast(_SCREAMING_SNAKE_CASE , self.info.features.arrow_schema )
return pa_table
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE ):
a_ = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ) ):
with open(_SCREAMING_SNAKE_CASE , """rb""" ) as f:
a_ = pq.ParquetFile(_SCREAMING_SNAKE_CASE )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
a_ = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f"""{file_idx}_{batch_idx}""", self._cast_table(_SCREAMING_SNAKE_CASE )
except ValueError as e:
logger.error(f"""Failed to read file '{file}' with error {type(_SCREAMING_SNAKE_CASE )}: {e}""" )
raise | 403 |
import argparse
import json
import subprocess
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : List[str] , UpperCamelCase : Tuple ) -> Dict:
"""simple docstring"""
a_ = []
a_ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
""" https://api.github.com/repos/huggingface/transformers/actions/runners"""
)
a_ = subprocess.run(UpperCamelCase , shell=UpperCamelCase , stdout=subprocess.PIPE )
a_ = output.stdout.decode("""utf-8""" )
a_ = json.loads(UpperCamelCase )
a_ = status["""runners"""]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(UpperCamelCase )
# save the result so we can report them on Slack
with open("""offline_runners.txt""" , """w""" ) as fp:
fp.write(json.dumps(UpperCamelCase ) )
if len(UpperCamelCase ) > 0:
a_ = """\n""".join([x["""name"""] for x in offline_runners] )
raise ValueError(F"""The following runners are offline:\n{failed}""" )
if __name__ == "__main__":
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : List[str] ) -> str:
"""simple docstring"""
return values.split(""",""" )
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--target_runners',
default=None,
type=list_str,
required=True,
help='Comma-separated list of runners to check status.',
)
parser.add_argument(
'--token', default=None, type=str, required=True, help='A token that has actions:read permission.'
)
_A = parser.parse_args()
get_runner_status(args.target_runners, args.token) | 403 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
"""shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class a__ ( __magic_name__ , __magic_name__ ):
lowercase_ = "dinat"
lowercase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Optional[int] , UpperCamelCase_ : int=4 , UpperCamelCase_ : str=3 , UpperCamelCase_ : Optional[Any]=64 , UpperCamelCase_ : Union[str, Any]=[3, 4, 6, 5] , UpperCamelCase_ : Union[str, Any]=[2, 4, 8, 16] , UpperCamelCase_ : List[Any]=7 , UpperCamelCase_ : List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] , UpperCamelCase_ : Tuple=3.0 , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Dict=0.0 , UpperCamelCase_ : Union[str, Any]=0.0 , UpperCamelCase_ : int=0.1 , UpperCamelCase_ : int="gelu" , UpperCamelCase_ : Dict=0.02 , UpperCamelCase_ : List[Any]=1e-5 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : int=None , UpperCamelCase_ : Optional[Any]=None , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
super().__init__(**UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = patch_size
__UpperCAmelCase : int = num_channels
__UpperCAmelCase : List[str] = embed_dim
__UpperCAmelCase : List[Any] = depths
__UpperCAmelCase : List[str] = len(UpperCamelCase_)
__UpperCAmelCase : Tuple = num_heads
__UpperCAmelCase : Union[str, Any] = kernel_size
__UpperCAmelCase : Dict = dilations
__UpperCAmelCase : Optional[int] = mlp_ratio
__UpperCAmelCase : Tuple = qkv_bias
__UpperCAmelCase : Dict = hidden_dropout_prob
__UpperCAmelCase : Tuple = attention_probs_dropout_prob
__UpperCAmelCase : str = drop_path_rate
__UpperCAmelCase : int = hidden_act
__UpperCAmelCase : Dict = layer_norm_eps
__UpperCAmelCase : List[Any] = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__UpperCAmelCase : Dict = int(embed_dim * 2 ** (len(UpperCamelCase_) - 1))
__UpperCAmelCase : Dict = layer_scale_init_value
__UpperCAmelCase : int = ["stem"] + [F"stage{idx}" for idx in range(1 , len(UpperCamelCase_) + 1)]
__UpperCAmelCase , __UpperCAmelCase : str = get_aligned_output_features_output_indices(
out_features=UpperCamelCase_ , out_indices=UpperCamelCase_ , stage_names=self.stage_names)
| 77 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = jnp.ones((batch_size, length) ) / length
return scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 20
lowerCamelCase__ = self._get_uniform_logits(batch_size=2 ,length=_lowerCAmelCase )
# tweak scores to not be uniform anymore
lowerCamelCase__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase__ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase__ = jax.nn.softmax(_lowerCAmelCase ,axis=-1 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create ramp distribution
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy()
lowerCamelCase__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, length) ).copy()
lowerCamelCase__ = top_k_warp_safety_check(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase__ = np.exp(top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase__ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase__ = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
# check that min length is applied at length 5
lowerCamelCase__ = ids_tensor((batch_size, 20) ,vocab_size=20 )
lowerCamelCase__ = 5
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = 15
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase__ = ids_tensor((batch_size, 1) ,vocab_size=20 )
lowerCamelCase__ = 1
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase__ = ids_tensor((batch_size, 4) ,vocab_size=20 )
lowerCamelCase__ = 4
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# with processor list
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
def run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
# with processor list
def run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jitted_run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = jitted_run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 50 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class _a ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self : Tuple , a : int , a : int ) ->Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.ones((batch_size, length) ) / length
return scores
def A_ ( self : Tuple ) ->List[str]:
SCREAMING_SNAKE_CASE__ : List[Any] = None
SCREAMING_SNAKE_CASE__ : Tuple = 20
SCREAMING_SNAKE_CASE__ : Optional[int] = self._get_uniform_logits(batch_size=2 , length=lowerCAmelCase_ )
# tweak scores to not be uniform anymore
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
SCREAMING_SNAKE_CASE__ : Optional[int] = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
SCREAMING_SNAKE_CASE__ : Optional[Any] = jax.nn.softmax(lowerCAmelCase_ , axis=-1 )
SCREAMING_SNAKE_CASE__ : List[Any] = FlaxTemperatureLogitsWarper(temperature=0.5 )
SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxTemperatureLogitsWarper(temperature=1.3 )
SCREAMING_SNAKE_CASE__ : str = jax.nn.softmax(temp_dist_warper_sharper(lowerCAmelCase_ , scores.copy() , cur_len=lowerCAmelCase_ ) , axis=-1 )
SCREAMING_SNAKE_CASE__ : Any = jax.nn.softmax(temp_dist_warper_smoother(lowerCAmelCase_ , scores.copy() , cur_len=lowerCAmelCase_ ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def A_ ( self : List[Any] ) ->Optional[Any]:
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = 10
SCREAMING_SNAKE_CASE__ : Optional[Any] = 2
# create ramp distribution
SCREAMING_SNAKE_CASE__ : List[str] = np.broadcast_to(np.arange(lowerCAmelCase_ )[None, :] , (batch_size, vocab_size) ).copy()
SCREAMING_SNAKE_CASE__ : Optional[Any] = ramp_logits[1:, : vocab_size // 2] + vocab_size
SCREAMING_SNAKE_CASE__ : str = FlaxTopKLogitsWarper(3 )
SCREAMING_SNAKE_CASE__ : str = top_k_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : int = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
SCREAMING_SNAKE_CASE__ : List[Any] = np.broadcast_to(np.arange(lowerCAmelCase_ )[None, :] , (batch_size, length) ).copy()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = top_k_warp_safety_check(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def A_ ( self : int ) ->Dict:
SCREAMING_SNAKE_CASE__ : List[Any] = None
SCREAMING_SNAKE_CASE__ : Tuple = 10
SCREAMING_SNAKE_CASE__ : str = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
SCREAMING_SNAKE_CASE__ : Dict = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
SCREAMING_SNAKE_CASE__ : int = FlaxTopPLogitsWarper(0.8 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.exp(top_p_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
SCREAMING_SNAKE_CASE__ : str = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1E-3 ) )
# check edge cases with negative and extreme logits
SCREAMING_SNAKE_CASE__ : Dict = np.broadcast_to(np.arange(lowerCAmelCase_ )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
SCREAMING_SNAKE_CASE__ : Tuple = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
SCREAMING_SNAKE_CASE__ : Dict = top_p_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def A_ ( self : Dict ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[str] = 20
SCREAMING_SNAKE_CASE__ : str = 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Any = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=lowerCAmelCase_ )
# check that min length is applied at length 5
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor((batch_size, 20) , vocab_size=20 )
SCREAMING_SNAKE_CASE__ : List[Any] = 5
SCREAMING_SNAKE_CASE__ : List[str] = self._get_uniform_logits(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = min_dist_processor(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float("inf" )] )
# check that min length is not applied anymore at length 15
SCREAMING_SNAKE_CASE__ : List[str] = self._get_uniform_logits(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = 15
SCREAMING_SNAKE_CASE__ : int = min_dist_processor(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
self.assertFalse(jnp.isinf(lowerCAmelCase_ ).any() )
def A_ ( self : str ) ->List[Any]:
SCREAMING_SNAKE_CASE__ : Tuple = 20
SCREAMING_SNAKE_CASE__ : Tuple = 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase_ )
# check that all scores are -inf except the bos_token_id score
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor((batch_size, 1) , vocab_size=20 )
SCREAMING_SNAKE_CASE__ : str = 1
SCREAMING_SNAKE_CASE__ : Tuple = self._get_uniform_logits(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Tuple = logits_processor(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
SCREAMING_SNAKE_CASE__ : Tuple = 3
SCREAMING_SNAKE_CASE__ : Dict = self._get_uniform_logits(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Dict = logits_processor(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
self.assertFalse(jnp.isinf(lowerCAmelCase_ ).any() )
def A_ ( self : Optional[Any] ) ->List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 20
SCREAMING_SNAKE_CASE__ : Dict = 4
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : Dict = 5
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ )
# check that all scores are -inf except the eos_token_id when max_length is reached
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((batch_size, 4) , vocab_size=20 )
SCREAMING_SNAKE_CASE__ : List[Any] = 4
SCREAMING_SNAKE_CASE__ : Dict = self._get_uniform_logits(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = logits_processor(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
SCREAMING_SNAKE_CASE__ : str = 3
SCREAMING_SNAKE_CASE__ : List[str] = self._get_uniform_logits(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = logits_processor(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
self.assertFalse(jnp.isinf(lowerCAmelCase_ ).any() )
def A_ ( self : List[Any] ) ->Optional[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = 10
SCREAMING_SNAKE_CASE__ : str = 15
SCREAMING_SNAKE_CASE__ : int = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1
SCREAMING_SNAKE_CASE__ : Any = 15
# dummy input_ids and scores
SCREAMING_SNAKE_CASE__ : str = ids_tensor((batch_size, sequence_length) , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.copy()
SCREAMING_SNAKE_CASE__ : Tuple = self._get_uniform_logits(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = scores.copy()
# instantiate all dist processors
SCREAMING_SNAKE_CASE__ : Tuple = FlaxTemperatureLogitsWarper(temperature=0.5 )
SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxTopKLogitsWarper(3 )
SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
SCREAMING_SNAKE_CASE__ : int = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : str = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = 10
# no processor list
SCREAMING_SNAKE_CASE__ : List[Any] = temp_dist_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Dict = top_k_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Tuple = top_p_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = min_dist_proc(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Tuple = bos_dist_proc(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = eos_dist_proc(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
# with processor list
SCREAMING_SNAKE_CASE__ : int = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
# scores should be equal
self.assertTrue(jnp.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def A_ ( self : int ) ->List[Any]:
SCREAMING_SNAKE_CASE__ : str = 4
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 10
SCREAMING_SNAKE_CASE__ : List[Any] = 15
SCREAMING_SNAKE_CASE__ : Optional[Any] = 2
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 15
# dummy input_ids and scores
SCREAMING_SNAKE_CASE__ : Any = ids_tensor((batch_size, sequence_length) , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.copy()
SCREAMING_SNAKE_CASE__ : int = self._get_uniform_logits(lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[str] = scores.copy()
# instantiate all dist processors
SCREAMING_SNAKE_CASE__ : Any = FlaxTemperatureLogitsWarper(temperature=0.5 )
SCREAMING_SNAKE_CASE__ : Any = FlaxTopKLogitsWarper(3 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
SCREAMING_SNAKE_CASE__ : int = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : str = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Dict = 10
# no processor list
def run_no_processor_list(a : int , a : Dict , a : List[Any] ):
SCREAMING_SNAKE_CASE__ : Any = temp_dist_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : List[Any] = top_k_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Tuple = top_p_warp(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : str = min_dist_proc(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = bos_dist_proc(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Tuple = eos_dist_proc(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
return scores
# with processor list
def run_processor_list(a : Union[str, Any] , a : int , a : Optional[Any] ):
SCREAMING_SNAKE_CASE__ : Tuple = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
SCREAMING_SNAKE_CASE__ : Optional[int] = processor(lowerCAmelCase_ , lowerCAmelCase_ , cur_len=lowerCAmelCase_ )
return scores
SCREAMING_SNAKE_CASE__ : List[Any] = jax.jit(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jax.jit(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : str = jitted_run_no_processor_list(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jitted_run_processor_list(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# scores should be equal
self.assertTrue(jnp.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() ) | 711 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(_lowerCamelCase )
or left < -len(_lowerCamelCase )
or right >= len(_lowerCamelCase )
or right < -len(_lowerCamelCase )
):
raise IndexError("list index out of range" )
if left == right:
return nums[left]
SCREAMING_SNAKE_CASE__ : Optional[int] = (left + right) >> 1 # the middle
SCREAMING_SNAKE_CASE__ : List[Any] = find_max(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) # find max in range[left, mid]
SCREAMING_SNAKE_CASE__ : Optional[int] = find_max(_lowerCamelCase , mid + 1 , _lowerCamelCase ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 26 | 0 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
UpperCAmelCase : Dict = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n Schwartz, Rich and\n Micciulla, Linnea and\n Makhoul, John",\n booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n month = aug # " 8-12",\n year = "2006",\n address = "Cambridge, Massachusetts, USA",\n publisher = "Association for Machine Translation in the Americas",\n url = "https://aclanthology.org/2006.amta-papers.25",\n pages = "223--231",\n}\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
UpperCAmelCase : List[Any] = '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'
UpperCAmelCase : int = '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n predictions (list of str): The system stream (a sequence of segments).\n references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n Only applies if `normalized = True`. Defaults to `False`.\n case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n \'num_edits\' (int): The cumulative number of edits\n \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n Example 1:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n Example 2:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n Example 3:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... normalized=True,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n Example 4:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n Example 5:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase__ ( self : str ):
'''simple docstring'''
if version.parse(scb.__version__ ) < version.parse("""1.4.12""" ):
raise ImportWarning(
"""To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
"""You can install it with `pip install \"sacrebleu>=1.4.12\"`.""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""http://www.cs.umd.edu/~snover/tercom/""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""" ) , id="""references""" ),
} ) , codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""] , reference_urls=[
"""https://github.com/jhclark/tercom""",
] , )
def lowerCamelCase__ ( self : List[Any] , UpperCamelCase : int , UpperCamelCase : Dict , UpperCamelCase : bool = False , UpperCamelCase : bool = False , UpperCamelCase : bool = False , UpperCamelCase : bool = False , ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = len(references[0] )
if any(len(UpperCamelCase ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
__UpperCAmelCase : Any = [[refs[i] for refs in references] for i in range(UpperCamelCase )]
__UpperCAmelCase : List[str] = TER(
normalized=UpperCamelCase , no_punct=UpperCamelCase , asian_support=UpperCamelCase , case_sensitive=UpperCamelCase , )
__UpperCAmelCase : Tuple = sb_ter.corpus_score(UpperCamelCase , UpperCamelCase )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 139 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
UpperCAmelCase : Dict = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n Schwartz, Rich and\n Micciulla, Linnea and\n Makhoul, John",\n booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n month = aug # " 8-12",\n year = "2006",\n address = "Cambridge, Massachusetts, USA",\n publisher = "Association for Machine Translation in the Americas",\n url = "https://aclanthology.org/2006.amta-papers.25",\n pages = "223--231",\n}\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
UpperCAmelCase : List[Any] = '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'
UpperCAmelCase : int = '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n predictions (list of str): The system stream (a sequence of segments).\n references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n Only applies if `normalized = True`. Defaults to `False`.\n case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n \'num_edits\' (int): The cumulative number of edits\n \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n Example 1:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n Example 2:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n Example 3:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... normalized=True,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n Example 4:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n Example 5:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase__ ( self : str ):
'''simple docstring'''
if version.parse(scb.__version__ ) < version.parse("""1.4.12""" ):
raise ImportWarning(
"""To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
"""You can install it with `pip install \"sacrebleu>=1.4.12\"`.""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""http://www.cs.umd.edu/~snover/tercom/""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""" ) , id="""references""" ),
} ) , codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""] , reference_urls=[
"""https://github.com/jhclark/tercom""",
] , )
def lowerCamelCase__ ( self : List[Any] , UpperCamelCase : int , UpperCamelCase : Dict , UpperCamelCase : bool = False , UpperCamelCase : bool = False , UpperCamelCase : bool = False , UpperCamelCase : bool = False , ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = len(references[0] )
if any(len(UpperCamelCase ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
__UpperCAmelCase : Any = [[refs[i] for refs in references] for i in range(UpperCamelCase )]
__UpperCAmelCase : List[str] = TER(
normalized=UpperCamelCase , no_punct=UpperCamelCase , asian_support=UpperCamelCase , case_sensitive=UpperCamelCase , )
__UpperCAmelCase : Tuple = sb_ter.corpus_score(UpperCamelCase , UpperCamelCase )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 139 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: Dict =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Any ={
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
),
}
class __A ( UpperCamelCase__ ):
a__ : List[str] = """pix2struct_text_model"""
a__ : Optional[Any] = ["""past_key_values"""]
a__ : Tuple = {
"""hidden_size""": """hidden_size""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__(self : List[Any] , __a : Optional[Any]=50244 , __a : List[str]=768 , __a : Union[str, Any]=64 , __a : Tuple=2048 , __a : Union[str, Any]=12 , __a : Dict=12 , __a : Dict=32 , __a : Tuple=128 , __a : Optional[Any]=0.1 , __a : int=1E-6 , __a : Any=1.0 , __a : Any="gelu_new" , __a : List[Any]=0 , __a : Any=False , __a : Union[str, Any]=0 , __a : Tuple=1 , __a : List[Any]=False , __a : Union[str, Any]=True , **__a : Union[str, Any] , ):
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = d_kv
UpperCAmelCase_ = d_ff
UpperCAmelCase_ = num_layers
UpperCAmelCase_ = num_heads
UpperCAmelCase_ = relative_attention_num_buckets
UpperCAmelCase_ = relative_attention_max_distance
UpperCAmelCase_ = dropout_rate
UpperCAmelCase_ = layer_norm_epsilon
UpperCAmelCase_ = initializer_factor
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = eos_token_id
UpperCAmelCase_ = decoder_start_token_id
# for backwards compatibility
UpperCAmelCase_ = dense_act_fn
super().__init__(
pad_token_id=__a , eos_token_id=__a , decoder_start_token_id=__a , tie_word_embeddings=__a , is_decoder=__a , **__a , )
@classmethod
def _lowercase (cls : List[str] , __a : Union[str, os.PathLike] , **__a : List[str] ):
cls._set_token_in_kwargs(__a )
UpperCAmelCase_ , UpperCAmelCase_ = cls.get_config_dict(__a , **__a )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
UpperCAmelCase_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__a , **__a )
class __A ( UpperCamelCase__ ):
a__ : Any = """pix2struct_vision_model"""
def __init__(self : List[Any] , __a : Dict=768 , __a : Tuple=768 , __a : str=2048 , __a : List[Any]=64 , __a : Optional[int]=12 , __a : Optional[Any]=12 , __a : Union[str, Any]="gelu_new" , __a : Union[str, Any]=1E-6 , __a : List[str]=0.0 , __a : Union[str, Any]=0.0 , __a : Dict=1E-10 , __a : List[str]=1.0 , __a : Union[str, Any]=4096 , __a : Tuple=32 , __a : Any=128 , **__a : int , ):
super().__init__(**__a )
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = patch_embed_hidden_size
UpperCAmelCase_ = d_ff
UpperCAmelCase_ = dropout_rate
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = initializer_factor
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = dense_act_fn
UpperCAmelCase_ = seq_len
UpperCAmelCase_ = relative_attention_num_buckets
UpperCAmelCase_ = relative_attention_max_distance
UpperCAmelCase_ = d_kv
@classmethod
def _lowercase (cls : List[Any] , __a : Union[str, os.PathLike] , **__a : Optional[int] ):
cls._set_token_in_kwargs(__a )
UpperCAmelCase_ , UpperCAmelCase_ = cls.get_config_dict(__a , **__a )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
UpperCAmelCase_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__a , **__a )
class __A ( UpperCamelCase__ ):
a__ : Union[str, Any] = """pix2struct"""
a__ : Optional[int] = True
def __init__(self : Dict , __a : Optional[int]=None , __a : Dict=None , __a : Optional[int]=1.0 , __a : int=0.02 , __a : Any=False , __a : Optional[Any]=False , __a : int=True , **__a : List[str] , ):
super().__init__(tie_word_embeddings=__a , is_encoder_decoder=__a , **__a )
if text_config is None:
UpperCAmelCase_ = {}
logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values." )
if vision_config is None:
UpperCAmelCase_ = {}
logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values." )
UpperCAmelCase_ = PixaStructTextConfig(**__a )
UpperCAmelCase_ = PixaStructVisionConfig(**__a )
UpperCAmelCase_ = self.text_config.decoder_start_token_id
UpperCAmelCase_ = self.text_config.pad_token_id
UpperCAmelCase_ = self.text_config.eos_token_id
UpperCAmelCase_ = initializer_factor
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = self.initializer_range
UpperCAmelCase_ = self.initializer_range
UpperCAmelCase_ = is_vqa
@classmethod
def _lowercase (cls : Dict , __a : PixaStructTextConfig , __a : PixaStructVisionConfig , **__a : int ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__a )
def _lowercase (self : Tuple ):
UpperCAmelCase_ = copy.deepcopy(self.__dict__ )
UpperCAmelCase_ = self.text_config.to_dict()
UpperCAmelCase_ = self.vision_config.to_dict()
UpperCAmelCase_ = self.__class__.model_type
return output
| 415 | '''simple docstring'''
import sys
SCREAMING_SNAKE_CASE_: Optional[int] =(
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCAmelCase_ ( snake_case_ : str = N ) -> int:
'''simple docstring'''
UpperCAmelCase_ = -sys.maxsize - 1
for i in range(len(snake_case_ ) - 12 ):
UpperCAmelCase_ = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
UpperCAmelCase_ = product
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| 415 | 1 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
A = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def __A ( a_ :Optional[Any]) -> Optional[Any]:
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def __A ( a_ :Optional[int] , a_ :Optional[int] , a_ :Optional[Any]) -> int:
return max(metric_fn(a_ , a_) for gt in ground_truths)
def __A ( a_ :List[str] , a_ :List[str] , a_ :Optional[Any]) -> Dict:
__a : List[Any] = [line.strip() for line in open(a_ , '''r''').readlines()]
__a : Dict = []
if args.gold_data_mode == "qa":
__a : Dict = pd.read_csv(a_ , sep='''\t''' , header=a_)
for answer_list in data[1]:
__a : List[str] = ast.literal_eval(a_)
answers.append(a_)
else:
__a : Union[str, Any] = [line.strip() for line in open(a_ , '''r''').readlines()]
__a : Union[str, Any] = [[reference] for reference in references]
__a : Dict = 0
for prediction, ground_truths in zip(a_ , a_):
total += 1
em += metric_max_over_ground_truths(a_ , a_ , a_)
fa += metric_max_over_ground_truths(a_ , a_ , a_)
__a : List[str] = 1_0_0.0 * em / total
__a : List[str] = 1_0_0.0 * fa / total
logger.info(F"""F1: {fa:.2f}""")
logger.info(F"""EM: {em:.2f}""")
def __A ( a_ :Union[str, Any] , a_ :List[Any] , a_ :Optional[Any]) -> Optional[int]:
__a : Optional[int] = args.k
__a : Optional[int] = [line.strip() for line in open(a_ , '''r''').readlines()]
__a : Optional[Any] = [line.strip() for line in open(a_ , '''r''').readlines()]
__a : Optional[int] = 0
for hypo, reference in zip(a_ , a_):
__a : List[str] = set(hypo.split('''\t''')[:k])
__a : Dict = set(reference.split('''\t'''))
total += 1
em += len(hypo_provenance & ref_provenance) / k
__a : Tuple = 1_0_0.0 * em / total
logger.info(F"""Precision@{k}: {em: .2f}""")
def __A ( a_ :Dict , a_ :Any , a_ :int) -> int:
def strip_title(a_ :List[str]):
if title.startswith('''"'''):
__a : str = title[1:]
if title.endswith('''"'''):
__a : Dict = title[:-1]
return title
__a : Tuple = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
a_ , return_tensors='''pt''' , padding=a_ , truncation=a_ , )['''input_ids'''].to(args.device)
__a : Optional[Any] = rag_model.rag.question_encoder(a_)
__a : Dict = question_enc_outputs[0]
__a : List[str] = rag_model.retriever(
a_ , question_enc_pool_output.cpu().detach().to(torch.floataa).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors='''pt''' , )
__a : Optional[Any] = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
__a : Dict = []
for docs in all_docs:
__a : Any = [strip_title(a_) for title in docs['''title''']]
provenance_strings.append('''\t'''.join(a_))
return provenance_strings
def __A ( a_ :Optional[Any] , a_ :str , a_ :str) -> List[str]:
with torch.no_grad():
__a : Any = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
a_ , return_tensors='''pt''' , padding=a_ , truncation=a_)
__a : Optional[int] = inputs_dict.input_ids.to(args.device)
__a : List[Any] = inputs_dict.attention_mask.to(args.device)
__a : List[Any] = rag_model.generate( # rag_model overwrites generate
a_ , attention_mask=a_ , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=a_ , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
__a : Optional[Any] = rag_model.retriever.generator_tokenizer.batch_decode(a_ , skip_special_tokens=a_)
if args.print_predictions:
for q, a in zip(a_ , a_):
logger.info('''Q: {} - A: {}'''.format(a_ , a_))
return answers
def __A ( ) -> Union[str, Any]:
__a : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''' , choices=['''rag_sequence''', '''rag_token''', '''bart'''] , type=a_ , help=(
'''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
''' model_name_or_path'''
) , )
parser.add_argument(
'''--index_name''' , default=a_ , choices=['''exact''', '''compressed''', '''legacy'''] , type=a_ , help='''RAG model retriever type''' , )
parser.add_argument(
'''--index_path''' , default=a_ , type=a_ , help='''Path to the retrieval index''' , )
parser.add_argument('''--n_docs''' , default=5 , type=a_ , help='''Number of retrieved docs''')
parser.add_argument(
'''--model_name_or_path''' , default=a_ , type=a_ , required=a_ , help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--eval_mode''' , choices=['''e2e''', '''retrieval'''] , default='''e2e''' , type=a_ , help=(
'''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
''' precision@k.'''
) , )
parser.add_argument('''--k''' , default=1 , type=a_ , help='''k for the precision@k calculation''')
parser.add_argument(
'''--evaluation_set''' , default=a_ , type=a_ , required=a_ , help='''Path to a file containing evaluation samples''' , )
parser.add_argument(
'''--gold_data_path''' , default=a_ , type=a_ , required=a_ , help='''Path to a tab-separated file with gold samples''' , )
parser.add_argument(
'''--gold_data_mode''' , default='''qa''' , type=a_ , choices=['''qa''', '''ans'''] , help=(
'''Format of the gold data file'''
'''qa - a single line in the following format: question [tab] answer_list'''
'''ans - a single line of the gold file contains the expected answer string'''
) , )
parser.add_argument(
'''--predictions_path''' , type=a_ , default='''predictions.txt''' , help='''Name of the predictions file, to be stored in the checkpoints directory''' , )
parser.add_argument(
'''--eval_all_checkpoints''' , action='''store_true''' , help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''' , )
parser.add_argument(
'''--eval_batch_size''' , default=8 , type=a_ , help='''Batch size per GPU/CPU for evaluation.''' , )
parser.add_argument(
'''--recalculate''' , help='''Recalculate predictions even if the prediction file exists''' , action='''store_true''' , )
parser.add_argument(
'''--num_beams''' , default=4 , type=a_ , help='''Number of beams to be used when generating answers''' , )
parser.add_argument('''--min_length''' , default=1 , type=a_ , help='''Min length of the generated answers''')
parser.add_argument('''--max_length''' , default=50 , type=a_ , help='''Max length of the generated answers''')
parser.add_argument(
'''--print_predictions''' , action='''store_true''' , help='''If True, prints predictions while evaluating.''' , )
parser.add_argument(
'''--print_docs''' , action='''store_true''' , help='''If True, prints docs retried while generating.''' , )
__a : Optional[Any] = parser.parse_args()
__a : Any = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')
return args
def __A ( a_ :Optional[int]) -> str:
__a : Any = {}
if args.model_type is None:
__a : List[Any] = infer_model_type(args.model_name_or_path)
assert args.model_type is not None
if args.model_type.startswith('''rag'''):
__a : List[str] = RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
__a : Optional[int] = args.n_docs
if args.index_name is not None:
__a : Union[str, Any] = args.index_name
if args.index_path is not None:
__a : Tuple = args.index_path
else:
__a : Tuple = BartForConditionalGeneration
__a : str = (
[f.path for f in os.scandir(args.model_name_or_path) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info('''Evaluate the following checkpoints: %s''' , a_)
__a : Optional[int] = get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
__a : str = evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path) and (not args.recalculate):
logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path))
score_fn(a_ , args.predictions_path , args.gold_data_path)
continue
logger.info('''***** Running evaluation for {} *****'''.format(a_))
logger.info(''' Batch size = %d''' , args.eval_batch_size)
logger.info(''' Predictions will be stored under {}'''.format(args.predictions_path))
if args.model_type.startswith('''rag'''):
__a : Optional[int] = RagRetriever.from_pretrained(a_ , **a_)
__a : Tuple = model_class.from_pretrained(a_ , retriever=a_ , **a_)
model.retriever.init_retrieval()
else:
__a : Dict = model_class.from_pretrained(a_ , **a_)
model.to(args.device)
with open(args.evaluation_set , '''r''') as eval_file, open(args.predictions_path , '''w''') as preds_file:
__a : Any = []
for line in tqdm(a_):
questions.append(line.strip())
if len(a_) == args.eval_batch_size:
__a : Tuple = evaluate_batch_fn(a_ , a_ , a_)
preds_file.write('''\n'''.join(a_) + '''\n''')
preds_file.flush()
__a : Dict = []
if len(a_) > 0:
__a : Optional[Any] = evaluate_batch_fn(a_ , a_ , a_)
preds_file.write('''\n'''.join(a_))
preds_file.flush()
score_fn(a_ , args.predictions_path , args.gold_data_path)
if __name__ == "__main__":
A = get_args()
main(args) | 52 |
"""simple docstring"""
from __future__ import annotations
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
__a , __a : List[Any] = text, pattern
__a , __a : Tuple = len(_UpperCAmelCase ), len(_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def _lowerCamelCase ( self , _UpperCAmelCase ):
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def _lowerCamelCase ( self ):
# searches pattern in text and returns index positions
__a : Dict = []
for i in range(self.textLen - self.patLen + 1 ):
__a : List[str] = self.mismatch_in_text(_UpperCAmelCase )
if mismatch_index == -1:
positions.append(_UpperCAmelCase )
else:
__a : Tuple = self.match_in_pattern(self.text[mismatch_index] )
__a : Optional[int] = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
A = '''ABAABA'''
A = '''AB'''
A = BoyerMooreSearch(text, pattern)
A = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions) | 52 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ ={
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ =[
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
lowercase__ =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 511 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowercase__ =logging.getLogger(__name__)
@dataclass
class a_ :
lowerCamelCase__ : str
lowerCamelCase__ : List[str]
lowerCamelCase__ : Optional[List[str]]
@dataclass
class a_ :
lowerCamelCase__ : List[int]
lowerCamelCase__ : List[int]
lowerCamelCase__ : Optional[List[int]] = None
lowerCamelCase__ : Optional[List[int]] = None
class a_ ( UpperCamelCase__ ):
lowerCamelCase__ : Any = 'train'
lowerCamelCase__ : Optional[int] = 'dev'
lowerCamelCase__ : int = 'test'
class a_ :
@staticmethod
def lowerCAmelCase__ ( UpperCAmelCase , UpperCAmelCase ):
raise NotImplementedError
@staticmethod
def lowerCAmelCase__ ( UpperCAmelCase ):
raise NotImplementedError
@staticmethod
def lowerCAmelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=False , UpperCAmelCase="[CLS]" , UpperCAmelCase=1 , UpperCAmelCase="[SEP]" , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=0 , UpperCAmelCase=0 , UpperCAmelCase=-1_00 , UpperCAmelCase=0 , UpperCAmelCase=True , ):
a_ = {label: i for i, label in enumerate(UpperCAmelCase )}
a_ = []
for ex_index, example in enumerate(UpperCAmelCase ):
if ex_index % 1_00_00 == 0:
logger.info("""Writing example %d of %d""" , UpperCAmelCase , len(UpperCAmelCase ) )
a_ = []
a_ = []
for word, label in zip(example.words , example.labels ):
a_ = tokenizer.tokenize(UpperCAmelCase )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(UpperCAmelCase ) > 0:
tokens.extend(UpperCAmelCase )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCAmelCase ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
a_ = tokenizer.num_special_tokens_to_add()
if len(UpperCAmelCase ) > max_seq_length - special_tokens_count:
a_ = tokens[: (max_seq_length - special_tokens_count)]
a_ = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
a_ = [sequence_a_segment_id] * len(UpperCAmelCase )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
a_ = [cls_token] + tokens
a_ = [pad_token_label_id] + label_ids
a_ = [cls_token_segment_id] + segment_ids
a_ = tokenizer.convert_tokens_to_ids(UpperCAmelCase )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
a_ = [1 if mask_padding_with_zero else 0] * len(UpperCAmelCase )
# Zero-pad up to the sequence length.
a_ = max_seq_length - len(UpperCAmelCase )
if pad_on_left:
a_ = ([pad_token] * padding_length) + input_ids
a_ = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
a_ = ([pad_token_segment_id] * padding_length) + segment_ids
a_ = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(UpperCAmelCase ) == max_seq_length
assert len(UpperCAmelCase ) == max_seq_length
assert len(UpperCAmelCase ) == max_seq_length
assert len(UpperCAmelCase ) == max_seq_length
if ex_index < 5:
logger.info("""*** Example ***""" )
logger.info("""guid: %s""" , example.guid )
logger.info("""tokens: %s""" , """ """.join([str(UpperCAmelCase ) for x in tokens] ) )
logger.info("""input_ids: %s""" , """ """.join([str(UpperCAmelCase ) for x in input_ids] ) )
logger.info("""input_mask: %s""" , """ """.join([str(UpperCAmelCase ) for x in input_mask] ) )
logger.info("""segment_ids: %s""" , """ """.join([str(UpperCAmelCase ) for x in segment_ids] ) )
logger.info("""label_ids: %s""" , """ """.join([str(UpperCAmelCase ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
a_ = None
features.append(
InputFeatures(
input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , label_ids=UpperCAmelCase ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class a_ ( UpperCamelCase__ ):
lowerCamelCase__ : List[InputFeatures]
lowerCamelCase__ : int = nn.CrossEntropyLoss().ignore_index
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase=False , UpperCAmelCase = Split.train , ):
# Load data features from cache or dataset file
a_ = os.path.join(
UpperCAmelCase , """cached_{}_{}_{}""".format(mode.value , tokenizer.__class__.__name__ , str(UpperCAmelCase ) ) , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
a_ = cached_features_file + """.lock"""
with FileLock(UpperCAmelCase ):
if os.path.exists(UpperCAmelCase ) and not overwrite_cache:
logger.info(f'''Loading features from cached file {cached_features_file}''' )
a_ = torch.load(UpperCAmelCase )
else:
logger.info(f'''Creating features from dataset file at {data_dir}''' )
a_ = token_classification_task.read_examples_from_file(UpperCAmelCase , UpperCAmelCase )
# TODO clean up all this to leverage built-in features of tokenizers
a_ = token_classification_task.convert_examples_to_features(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=UpperCAmelCase , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info(f'''Saving features into cached file {cached_features_file}''' )
torch.save(self.features , UpperCAmelCase )
def __len__( self ):
return len(self.features )
def __getitem__( self , UpperCAmelCase ):
return self.features[i]
if is_tf_available():
import tensorflow as tf
class a_ :
lowerCamelCase__ : List[InputFeatures]
lowerCamelCase__ : int = -100
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase=False , UpperCAmelCase = Split.train , ):
a_ = token_classification_task.read_examples_from_file(UpperCAmelCase , UpperCAmelCase )
# TODO clean up all this to leverage built-in features of tokenizers
a_ = token_classification_task.convert_examples_to_features(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=UpperCAmelCase , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
a_ = tf.data.Dataset.from_generator(
UpperCAmelCase , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) , (
{"""input_ids""": tf.TensorShape([None] ), """attention_mask""": tf.TensorShape([None] )},
tf.TensorShape([None] ),
) , )
else:
a_ = tf.data.Dataset.from_generator(
UpperCAmelCase , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) , (
{
"""input_ids""": tf.TensorShape([None] ),
"""attention_mask""": tf.TensorShape([None] ),
"""token_type_ids""": tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) , )
def lowerCAmelCase__ ( self ):
a_ = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self ):
return len(self.features )
def __getitem__( self , UpperCAmelCase ):
return self.features[i]
| 511 | 1 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
_A = int(A__ )
_A , _A , _A = t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
return F'{h}:{m:02d}:{s:02d}' if h != 0 else F'{m:02d}:{s:02d}'
def _SCREAMING_SNAKE_CASE ( __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Dict , __snake_case : Tuple , __snake_case : Union[str, Any]=3_0_0 ):
# docstyle-ignore
return F'\n <div>\n {prefix}\n <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>\n {label}\n </div>\n '
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
_A = '<table border=\"1\" class=\"dataframe\">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += F' <th>{i}</th>\n'
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
_A = F'{elt:.6f}' if isinstance(A__ , A__ ) else str(A__ )
html_code += F' <td>{elt}</td>\n'
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class lowercase_ :
"""simple docstring"""
__lowerCAmelCase = 5
__lowerCAmelCase = 0.2
def __init__( self : Optional[int], UpperCamelCase__ : int, UpperCamelCase__ : Optional[str] = None, UpperCamelCase__ : bool = True, UpperCamelCase__ : Optional["NotebookTrainingTracker"] = None, UpperCamelCase__ : int = 3_00, ) -> List[str]:
_A = total
_A = '' if prefix is None else prefix
_A = leave
_A = parent
_A = width
_A = None
_A = None
_A = None
def __UpperCAmelCase ( self : Union[str, Any], UpperCamelCase__ : int, UpperCamelCase__ : bool = False, UpperCamelCase__ : str = None ) -> Any:
_A = value
if comment is not None:
_A = comment
if self.last_value is None:
_A = _A = time.time()
_A = _A = value
_A = _A = None
_A = self.warmup
_A = 1
self.update_bar(__lowerCamelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total ):
if self.first_calls > 0:
self.first_calls -= 1
_A = time.time()
_A = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
_A = self.elapsed_time / (value - self.start_value)
else:
_A = None
if value >= self.total:
_A = self.total
_A = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
_A = self.average_time_per_item * (self.total - value)
self.update_bar(__lowerCamelCase )
_A = value
_A = current_time
if self.average_time_per_item is None:
_A = 1
else:
_A = max(int(self.update_every / self.average_time_per_item ), 1 )
def __UpperCAmelCase ( self : str, UpperCamelCase__ : List[Any], UpperCamelCase__ : List[Any]=None ) -> Any:
_A = ' ' * (len(str(self.total ) ) - len(str(__lowerCamelCase ) )) + str(__lowerCamelCase )
if self.elapsed_time is None:
_A = f'[{spaced_value}/{self.total} : < :'
elif self.predicted_remaining is None:
_A = f'[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'
else:
_A = (
f'[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'
f' {format_time(self.predicted_remaining )}'
)
self.label += f', {1/self.average_time_per_item:.2f} it/s'
self.label += "]" if self.comment is None or len(self.comment ) == 0 else f', {self.comment}]'
self.display()
def __UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
_A = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
_A = disp.display(disp.HTML(self.html_code ), display_id=__lowerCamelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def __UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class lowercase_ ( __snake_case ):
"""simple docstring"""
def __init__( self : int, UpperCamelCase__ : Optional[int], UpperCamelCase__ : List[str]=None ) -> Optional[int]:
super().__init__(__lowerCamelCase )
_A = None if column_names is None else [column_names]
_A = None
def __UpperCAmelCase ( self : Any ) -> Any:
_A = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
_A = disp.display(disp.HTML(self.html_code ), display_id=__lowerCamelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def __UpperCAmelCase ( self : Optional[Any], UpperCamelCase__ : Optional[Any] ) -> str:
if self.inner_table is None:
_A = [list(values.keys() ), list(values.values() )]
else:
_A = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(__lowerCamelCase )
_A = columns
self.inner_table.append([values[c] for c in columns] )
def __UpperCAmelCase ( self : Dict, UpperCamelCase__ : Any, UpperCamelCase__ : List[str]=None, UpperCamelCase__ : str=3_00 ) -> Optional[Any]:
_A = NotebookProgressBar(__lowerCamelCase, prefix=__lowerCamelCase, parent=self, width=__lowerCamelCase )
return self.child_bar
def __UpperCAmelCase ( self : Tuple ) -> Any:
_A = None
self.display()
class lowercase_ ( __snake_case ):
"""simple docstring"""
def __init__( self : Dict ) -> List[Any]:
_A = None
_A = None
_A = False
def __UpperCAmelCase ( self : Optional[int], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : str, UpperCamelCase__ : str, **UpperCamelCase__ : Tuple ) -> str:
_A = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
_A = 0
_A = 0
_A = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
_A = NotebookTrainingTracker(state.max_steps, __lowerCamelCase )
def __UpperCAmelCase ( self : Dict, UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Dict, UpperCamelCase__ : Tuple, **UpperCamelCase__ : List[str] ) -> str:
_A = int(state.epoch ) if int(state.epoch ) == state.epoch else f'{state.epoch:.2f}'
self.training_tracker.update(
state.global_step + 1, comment=f'Epoch {epoch}/{state.num_train_epochs}', force_update=self._force_next_update, )
_A = False
def __UpperCAmelCase ( self : Dict, UpperCamelCase__ : int, UpperCamelCase__ : List[str], UpperCamelCase__ : int, UpperCamelCase__ : Any=None, **UpperCamelCase__ : int ) -> str:
if not has_length(__lowerCamelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
_A = self.training_tracker.add_child(len(__lowerCamelCase ) )
else:
_A = NotebookProgressBar(len(__lowerCamelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def __UpperCAmelCase ( self : Union[str, Any], UpperCamelCase__ : Optional[int], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : List[str], **UpperCamelCase__ : List[Any] ) -> Any:
if self.prediction_bar is not None:
self.prediction_bar.close()
_A = None
def __UpperCAmelCase ( self : Optional[Any], UpperCamelCase__ : str, UpperCamelCase__ : int, UpperCamelCase__ : Tuple, UpperCamelCase__ : Dict=None, **UpperCamelCase__ : Dict ) -> int:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
_A = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
_A = state.global_step
self.training_tracker.write_line(__lowerCamelCase )
def __UpperCAmelCase ( self : str, UpperCamelCase__ : str, UpperCamelCase__ : List[Any], UpperCamelCase__ : Any, UpperCamelCase__ : Optional[int]=None, **UpperCamelCase__ : int ) -> List[Any]:
if self.training_tracker is not None:
_A = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
_A = log['loss']
break
if self.first_column == "Epoch":
_A = int(state.epoch )
else:
_A = state.global_step
_A = 'eval'
for k in metrics:
if k.endswith('_loss' ):
_A = re.sub(r'\_loss$', '', __lowerCamelCase )
_A = metrics.pop('total_flos', __lowerCamelCase )
_A = metrics.pop('epoch', __lowerCamelCase )
_A = metrics.pop(f'{metric_key_prefix}_runtime', __lowerCamelCase )
_A = metrics.pop(f'{metric_key_prefix}_samples_per_second', __lowerCamelCase )
_A = metrics.pop(f'{metric_key_prefix}_steps_per_second', __lowerCamelCase )
_A = metrics.pop(f'{metric_key_prefix}_jit_compilation_time', __lowerCamelCase )
for k, v in metrics.items():
if k == f'{metric_key_prefix}_loss':
_A = v
else:
_A = k.split('_' )
_A = ' '.join([part.capitalize() for part in splits[1:]] )
_A = v
self.training_tracker.write_line(__lowerCamelCase )
self.training_tracker.remove_child()
_A = None
# Evaluation takes a long time so we should force the next update.
_A = True
def __UpperCAmelCase ( self : Union[str, Any], UpperCamelCase__ : Dict, UpperCamelCase__ : Dict, UpperCamelCase__ : Dict, **UpperCamelCase__ : Any ) -> Dict:
self.training_tracker.update(
state.global_step, comment=f'Epoch {int(state.epoch )}/{state.num_train_epochs}', force_update=__lowerCamelCase )
_A = None
| 107 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[Any] = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "gpt_neox"
def __init__( self : Optional[int] , __lowerCamelCase : List[str]=50432 , __lowerCamelCase : int=6144 , __lowerCamelCase : Optional[Any]=44 , __lowerCamelCase : Tuple=64 , __lowerCamelCase : Optional[int]=24576 , __lowerCamelCase : List[str]="gelu" , __lowerCamelCase : Any=0.25 , __lowerCamelCase : List[Any]=10000 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : int=0.0 , __lowerCamelCase : str=0.1 , __lowerCamelCase : List[Any]=2048 , __lowerCamelCase : Tuple=0.02 , __lowerCamelCase : Tuple=1e-5 , __lowerCamelCase : Dict=True , __lowerCamelCase : int=0 , __lowerCamelCase : Optional[Any]=2 , __lowerCamelCase : List[str]=False , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Optional[int]=None , **__lowerCamelCase : str , ):
super().__init__(bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = rotary_pct
SCREAMING_SNAKE_CASE = rotary_emb_base
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = hidden_dropout
SCREAMING_SNAKE_CASE = classifier_dropout
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = tie_word_embeddings
SCREAMING_SNAKE_CASE = use_parallel_residual
SCREAMING_SNAKE_CASE = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"The hidden size is not divisble by the number of attention heads! Make sure to update them!" )
def _snake_case ( self : Union[str, Any] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __lowerCamelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
f"got {self.rope_scaling}" )
SCREAMING_SNAKE_CASE = self.rope_scaling.get("type" , __lowerCamelCase )
SCREAMING_SNAKE_CASE = self.rope_scaling.get("factor" , __lowerCamelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(__lowerCamelCase , __lowerCamelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" ) | 16 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
lowercase : Optional[Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = ['''input_features''']
def __init__( self , __UpperCAmelCase=80 , __UpperCAmelCase=1_60_00 , __UpperCAmelCase=1_60 , __UpperCAmelCase=30 , __UpperCAmelCase=4_00 , __UpperCAmelCase=0.0 , __UpperCAmelCase=False , **__UpperCAmelCase , ) -> str:
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , **__lowerCAmelCase , )
A : Dict = n_fft
A : int = hop_length
A : Optional[int] = chunk_length
A : Any = chunk_length * sampling_rate
A : List[str] = self.n_samples // hop_length
A : Any = sampling_rate
A : Optional[Any] = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=80_00.0 , sampling_rate=__lowerCAmelCase , norm='''slaney''' , mel_scale='''slaney''' , )
def snake_case ( self , __UpperCAmelCase ) -> np.ndarray:
A : List[Any] = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel='''log10''' , )
A : Any = log_spec[:, :-1]
A : int = np.maximum(__lowerCAmelCase , log_spec.max() - 8.0 )
A : int = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def snake_case ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
A : Tuple = np.array(__lowerCAmelCase , np.intaa )
A : Union[str, Any] = []
for vector, length in zip(__lowerCAmelCase , attention_mask.sum(-1 ) ):
A : Dict = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
A : List[Any] = padding_value
normed_input_values.append(__lowerCAmelCase )
else:
A : List[Any] = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __call__( self , __UpperCAmelCase , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = "max_length" , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
A : List[str] = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
A : List[str] = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
A : List[Any] = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
A : int = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A : Any = [np.asarray([raw_speech] ).T]
A : str = BatchFeature({'''input_features''': raw_speech} )
# convert into correct format for padding
A : Dict = self.pad(
__lowerCAmelCase , padding=__lowerCAmelCase , max_length=max_length if max_length else self.n_samples , truncation=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
A : Any = self.zero_mean_unit_var_norm(
padded_inputs['''input_features'''] , attention_mask=padded_inputs['''attention_mask'''] , padding_value=self.padding_value , )
A : Optional[int] = np.stack(padded_inputs['''input_features'''] , axis=0 )
# make sure list is in array format
A : List[str] = padded_inputs.get('''input_features''' ).transpose(2 , 0 , 1 )
A : int = [self._np_extract_fbank_features(__lowerCAmelCase ) for waveform in input_features[0]]
if isinstance(input_features[0] , __lowerCAmelCase ):
A : Any = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in input_features]
else:
A : Optional[int] = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
A : List[str] = padded_inputs['''attention_mask'''][:, :: self.hop_length]
if return_tensors is not None:
A : Tuple = padded_inputs.convert_to_tensors(__lowerCAmelCase )
return padded_inputs
def snake_case ( self ) -> Dict[str, Any]:
A : Dict = copy.deepcopy(self.__dict__ )
A : Union[str, Any] = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 710 |
import heapq
import sys
import numpy as np
lowercase : Optional[int] = tuple[int, int]
class __lowercase :
"""simple docstring"""
def __init__( self ) -> List[str]:
A : List[str] = []
A : str = set()
def snake_case ( self ) -> str:
if not self.empty():
return self.elements[0][0]
else:
return float('''inf''' )
def snake_case ( self ) -> Dict:
return len(self.elements ) == 0
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(__UpperCAmelCase )
else:
# update
# print("update", item)
A : int = []
((A) , (A)) : Tuple = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((A) , (A)) : Any = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def snake_case ( self , __UpperCAmelCase ) -> Dict:
if item in self.set:
self.set.remove(__UpperCAmelCase )
A : str = []
((A) , (A)) : List[Any] = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((A) , (A)) : Optional[Any] = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def snake_case ( self ) -> List[str]:
return self.elements[0][1]
def snake_case ( self ) -> Optional[int]:
((A) , (A)) : int = heapq.heappop(self.elements )
self.set.remove(__UpperCAmelCase )
return (priority, item)
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
# euclidean distance
A : int = np.array(lowerCamelCase_ )
A : Tuple = np.array(lowerCamelCase_ )
return np.linalg.norm(a - b )
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
# integer division by time variable
return consistent_heuristic(lowerCamelCase_ , lowerCamelCase_ ) // t
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
# manhattan distance
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
A : List[Any] = g_function[start] + Wa * heuristics[i](lowerCamelCase_ , lowerCamelCase_ )
return ans
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
A : Union[str, Any] = np.chararray((n, n) )
for i in range(lowerCamelCase_ ):
for j in range(lowerCamelCase_ ):
A : List[str] = '''*'''
for i in range(lowerCamelCase_ ):
for j in range(lowerCamelCase_ ):
if (j, (n - 1) - i) in blocks:
A : List[Any] = '''#'''
A : Tuple = '''-'''
A : Optional[Any] = back_pointer[goal]
while x != start:
((A) , (A)) : Union[str, Any] = x
# print(x)
A : str = '''-'''
A : Union[str, Any] = back_pointer[x]
A : Union[str, Any] = '''-'''
for i in range(lowerCamelCase_ ):
for j in range(lowerCamelCase_ ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=''' ''' )
print('''<-- End position''' , end=''' ''' )
else:
print(grid[i][j] , end=''' ''' )
print()
print('''^''' )
print('''Start position''' )
print()
print('''# is an obstacle''' )
print('''- is the path taken by algorithm''' )
print('''PATH TAKEN BY THE ALGORITHM IS:-''' )
A : Any = back_pointer[goal]
while x != start:
print(lowerCamelCase_ , end=''' ''' )
A : List[Any] = back_pointer[x]
print(lowerCamelCase_ )
sys.exit()
def snake_case__ ( lowerCamelCase_ ):
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ):
for itera in range(lowerCamelCase_ ):
open_list[itera].remove_element(lowerCamelCase_ )
# print("s", s)
# print("j", j)
((A) , (A)) : Tuple = s
A : Any = (x - 1, y)
A : Dict = (x + 1, y)
A : Union[str, Any] = (x, y + 1)
A : Tuple = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(lowerCamelCase_ ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(lowerCamelCase_ )
A : Optional[int] = -1
A : Dict = float('''inf''' )
if valid(lowerCamelCase_ ) and g_function[neighbours] > g_function[s] + 1:
A : Optional[int] = g_function[s] + 1
A : Any = s
if neighbours not in close_list_anchor:
open_list[0].put(lowerCamelCase_ , key(lowerCamelCase_ , 0 , lowerCamelCase_ , lowerCamelCase_ ) )
if neighbours not in close_list_inad:
for var in range(1 , lowerCamelCase_ ):
if key(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) <= Wa * key(
lowerCamelCase_ , 0 , lowerCamelCase_ , lowerCamelCase_ ):
open_list[j].put(
lowerCamelCase_ , key(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) )
def snake_case__ ( ):
A : Dict = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
lowercase : Dict = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
lowercase : Dict = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
lowercase : int = make_common_ground()
lowercase : Optional[int] = blocks_blk
# hyper parameters
lowercase : Dict = 1
lowercase : int = 1
lowercase : str = 20
lowercase : Optional[int] = 3 # one consistent and two other inconsistent
# start and end destination
lowercase : List[Any] = (0, 0)
lowercase : Dict = (n - 1, n - 1)
lowercase : Any = 1
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
A : Any = {start: 0, goal: float('''inf''' )}
A : Optional[Any] = {start: -1, goal: -1}
A : List[Any] = []
A : str = set()
for i in range(lowerCamelCase_ ):
open_list.append(PriorityQueue() )
open_list[i].put(lowerCamelCase_ , key(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) )
A : list[int] = []
A : list[int] = []
while open_list[0].minkey() < float('''inf''' ):
for i in range(1 , lowerCamelCase_ ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float('''inf''' ):
do_something(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
else:
A , A : Tuple = open_list[i].top_show()
visited.add(lowerCamelCase_ )
expand_state(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , )
close_list_inad.append(lowerCamelCase_ )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float('''inf''' ):
do_something(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
else:
A : Any = open_list[0].top_show()
visited.add(lowerCamelCase_ )
expand_state(
lowerCamelCase_ , 0 , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , )
close_list_anchor.append(lowerCamelCase_ )
print('''No path found to goal''' )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(lowerCamelCase_ ):
if (j, i) in blocks:
print('''#''' , end=''' ''' )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print('''*''' , end=''' ''' )
else:
print('''-''' , end=''' ''' )
else:
print('''*''' , end=''' ''' )
if (j, i) == (n - 1, n - 1):
print('''<-- End position''' , end=''' ''' )
print()
print('''^''' )
print('''Start position''' )
print()
print('''# is an obstacle''' )
print('''- is the path taken by algorithm''' )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 423 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class lowercase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any]=13 , _UpperCAmelCase : int=7 , _UpperCAmelCase : Any=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Tuple=99 , _UpperCAmelCase : int=32 , _UpperCAmelCase : Optional[int]=5 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : List[str]=37 , _UpperCAmelCase : Any="gelu" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : Dict=512 , _UpperCAmelCase : Any=16 , _UpperCAmelCase : str=2 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : Any=3 , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Dict=None , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_input_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
def lowerCAmelCase_ ( self : Any ):
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase_ ( self : str ):
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , use_stable_embedding=_UpperCAmelCase , )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Any ):
_A = OpenLlamaModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )
_A = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , ):
_A = True
_A = OpenLlamaModel(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , )
_A = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , )
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] , ):
_A = OpenLlamaForCausalLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Any , ):
_A = True
_A = True
_A = OpenLlamaForCausalLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
# first forward pass
_A = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , use_cache=_UpperCAmelCase , )
_A = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_A = ids_tensor((self.batch_size, 3) , config.vocab_size )
_A = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_A = torch.cat([input_ids, next_tokens] , dim=-1 )
_A = torch.cat([input_mask, next_mask] , dim=-1 )
_A = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , )['hidden_states'][0]
_A = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , )['hidden_states'][0]
# select random slice
_A = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_A = output_from_no_past[:, -3:, random_slice_idx].detach()
_A = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
def lowerCAmelCase_ ( self : List[str] ):
_A = self.prepare_config_and_inputs()
(
(
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) ,
) = config_and_inputs
_A = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
UpperCAmelCase : List[str] = (OpenLlamaForCausalLM,) if is_torch_available() else ()
UpperCAmelCase : str = (
{
'''feature-extraction''': OpenLlamaModel,
'''text-classification''': OpenLlamaForSequenceClassification,
'''text-generation''': OpenLlamaForCausalLM,
'''zero-shot''': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase : Any = False
UpperCAmelCase : Any = False
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = OpenLlamaModelTester(self )
_A = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def lowerCAmelCase_ ( self : List[Any] ):
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Tuple ):
_A = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_A = type
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
_A = 3
_A = input_dict['input_ids']
_A = input_ids.ne(1 ).to(_UpperCAmelCase )
_A = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_A = OpenLlamaForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase_ ( self : Dict ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
_A = 3
_A = 'single_label_classification'
_A = input_dict['input_ids']
_A = input_ids.ne(1 ).to(_UpperCAmelCase )
_A = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_A = OpenLlamaForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
_A = 3
_A = 'multi_label_classification'
_A = input_dict['input_ids']
_A = input_ids.ne(1 ).to(_UpperCAmelCase )
_A = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_A = OpenLlamaForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test' )
def lowerCAmelCase_ ( self : List[str] ):
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
_A = ids_tensor([1, 10] , config.vocab_size )
_A = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_A = OpenLlamaModel(_UpperCAmelCase )
original_model.to(_UpperCAmelCase )
original_model.eval()
_A = original_model(_UpperCAmelCase ).last_hidden_state
_A = original_model(_UpperCAmelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_A = {'type': scaling_type, 'factor': 10.0}
_A = OpenLlamaModel(_UpperCAmelCase )
scaled_model.to(_UpperCAmelCase )
scaled_model.eval()
_A = scaled_model(_UpperCAmelCase ).last_hidden_state
_A = scaled_model(_UpperCAmelCase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-5 ) )
| 7 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class _UpperCAmelCase ( lowercase ):
def __init__( self : Optional[int] , UpperCAmelCase : Any=-1):
# in NER datasets, the last column is usually reserved for NER label
SCREAMING_SNAKE_CASE_ :Tuple = label_idx
def _snake_case ( self : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Union[Split, str]):
if isinstance(UpperCAmelCase , UpperCAmelCase):
SCREAMING_SNAKE_CASE_ :List[Any] = mode.value
SCREAMING_SNAKE_CASE_ :Optional[Any] = os.path.join(UpperCAmelCase , F"{mode}.txt")
SCREAMING_SNAKE_CASE_ :Tuple = 1
SCREAMING_SNAKE_CASE_ :str = []
with open(UpperCAmelCase , encoding="utf-8") as f:
SCREAMING_SNAKE_CASE_ :Tuple = []
SCREAMING_SNAKE_CASE_ :int = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=UpperCAmelCase , labels=UpperCAmelCase))
guid_index += 1
SCREAMING_SNAKE_CASE_ :Tuple = []
SCREAMING_SNAKE_CASE_ :Any = []
else:
SCREAMING_SNAKE_CASE_ :int = line.split(" ")
words.append(splits[0])
if len(UpperCAmelCase) > 1:
labels.append(splits[self.label_idx].replace("\n" , ""))
else:
# Examples could have no label for mode = "test"
labels.append("O")
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=UpperCAmelCase , labels=UpperCAmelCase))
return examples
def _snake_case ( self : List[Any] , UpperCAmelCase : TextIO , UpperCAmelCase : TextIO , UpperCAmelCase : List):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
writer.write(UpperCAmelCase)
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
writer.write(UpperCAmelCase)
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0])
def _snake_case ( self : List[str] , UpperCAmelCase : str):
if path:
with open(UpperCAmelCase , "r") as f:
SCREAMING_SNAKE_CASE_ :Any = f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class _UpperCAmelCase ( lowercase ):
def __init__( self : Dict):
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2)
def _snake_case ( self : Union[str, Any] , UpperCAmelCase : str):
if path:
with open(UpperCAmelCase , "r") as f:
SCREAMING_SNAKE_CASE_ :Optional[int] = f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE_ :Dict = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class _UpperCAmelCase ( lowercase ):
def _snake_case ( self : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[Split, str]):
if isinstance(UpperCAmelCase , UpperCAmelCase):
SCREAMING_SNAKE_CASE_ :List[str] = mode.value
SCREAMING_SNAKE_CASE_ :List[str] = os.path.join(UpperCAmelCase , F"{mode}.txt")
SCREAMING_SNAKE_CASE_ :Dict = 1
SCREAMING_SNAKE_CASE_ :List[str] = []
with open(UpperCAmelCase , encoding="utf-8") as f:
for sentence in parse_incr(UpperCAmelCase):
SCREAMING_SNAKE_CASE_ :List[str] = []
SCREAMING_SNAKE_CASE_ :int = []
for token in sentence:
words.append(token["form"])
labels.append(token["upos"])
assert len(UpperCAmelCase) == len(UpperCAmelCase)
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=UpperCAmelCase , labels=UpperCAmelCase))
guid_index += 1
return examples
def _snake_case ( self : List[Any] , UpperCAmelCase : TextIO , UpperCAmelCase : TextIO , UpperCAmelCase : List):
SCREAMING_SNAKE_CASE_ :List[str] = 0
for sentence in parse_incr(UpperCAmelCase):
SCREAMING_SNAKE_CASE_ :str = preds_list[example_id]
SCREAMING_SNAKE_CASE_ :List[Any] = ""
for token in sentence:
out += F"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
out += "\n"
writer.write(UpperCAmelCase)
example_id += 1
def _snake_case ( self : Tuple , UpperCAmelCase : str):
if path:
with open(UpperCAmelCase , "r") as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 631 | 0 |
'''simple docstring'''
import re
def _lowerCAmelCase ( lowercase : str ) ->bool:
"""simple docstring"""
lowercase__ = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowercase , lowercase ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator("+918827897895"))
| 318 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( lowercase : List[str] , lowercase : Dict , lowercase : int ) ->List[Any]:
"""simple docstring"""
lowercase__ = BertConfig.from_json_file(lowercase )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase__ = BertForPreTraining(lowercase )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase , lowercase , lowercase )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase )
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--bert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowerCAmelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 318 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class lowercase ( a_ , unittest.TestCase ):
"""simple docstring"""
_UpperCamelCase : Any = TextToVideoSDPipeline
_UpperCamelCase : Optional[Any] = TEXT_TO_IMAGE_PARAMS
_UpperCamelCase : Optional[int] = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
_UpperCamelCase : Dict = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def __UpperCAmelCase ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
_snake_case : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D') , up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D') , cross_attention_dim=32 , attention_head_dim=4 , )
_snake_case : Tuple = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowerCamelCase_ , set_alpha_to_one=lowerCamelCase_ , )
torch.manual_seed(0 )
_snake_case : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=1_28 , )
torch.manual_seed(0 )
_snake_case : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='gelu' , projection_dim=5_12 , )
_snake_case : Tuple = CLIPTextModel(lowerCamelCase_ )
_snake_case : Any = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_snake_case : Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
}
return components
def __UpperCAmelCase ( self : int , lowerCamelCase_ : str , lowerCamelCase_ : Any=0 ):
'''simple docstring'''
if str(lowerCamelCase_ ).startswith('mps' ):
_snake_case : Any = torch.manual_seed(lowerCamelCase_ )
else:
_snake_case : Optional[Any] = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
_snake_case : int = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'pt',
}
return inputs
def __UpperCAmelCase ( self : Any ):
'''simple docstring'''
_snake_case : Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_snake_case : List[str] = self.get_dummy_components()
_snake_case : Dict = TextToVideoSDPipeline(**lowerCamelCase_ )
_snake_case : Optional[Any] = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_snake_case : Dict = self.get_dummy_inputs(lowerCamelCase_ )
_snake_case : Optional[Any] = 'np'
_snake_case : Dict = sd_pipe(**lowerCamelCase_ ).frames
_snake_case : Optional[int] = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
_snake_case : List[Any] = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase_ , expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase_ , expected_max_diff=1e-2 )
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def __UpperCAmelCase ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.' )
def __UpperCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self : Dict ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Any ):
'''simple docstring'''
_snake_case : Union[str, Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy' )
_snake_case : Dict = TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b' )
_snake_case : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_snake_case : str = pipe.to('cuda' )
_snake_case : str = 'Spiderman is surfing'
_snake_case : Optional[int] = torch.Generator(device='cpu' ).manual_seed(0 )
_snake_case : Union[str, Any] = pipe(lowerCamelCase_ , generator=lowerCamelCase_ , num_inference_steps=25 , output_type='pt' ).frames
_snake_case : List[Any] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
_snake_case : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy' )
_snake_case : Any = TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b' )
_snake_case : Tuple = pipe.to('cuda' )
_snake_case : List[Any] = 'Spiderman is surfing'
_snake_case : int = torch.Generator(device='cpu' ).manual_seed(0 )
_snake_case : Any = pipe(lowerCamelCase_ , generator=lowerCamelCase_ , num_inference_steps=2 , output_type='pt' ).frames
_snake_case : Tuple = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 304 |
def A__( __lowerCAmelCase ):
_snake_case : Optional[Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def A__( __lowerCAmelCase = 1_00 ):
_snake_case : Any = 1
_snake_case : Optional[int] = 2
for i in range(2 , max_n + 1 ):
_snake_case : Union[str, Any] = pre_numerator
_snake_case : Optional[Any] = 2 * i // 3 if i % 3 == 0 else 1
_snake_case : Dict = cur_numerator
_snake_case : str = e_cont * pre_numerator + temp
return sum_digits(__lowerCAmelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 304 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__snake_case : List[str] = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'processing_layoutlmv2': ['LayoutLMv2Processor'],
'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : int = ['LayoutLMv2FeatureExtractor']
__snake_case : Optional[Any] = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[Any] = [
'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv2ForQuestionAnswering',
'LayoutLMv2ForSequenceClassification',
'LayoutLMv2ForTokenClassification',
'LayoutLMv2Layer',
'LayoutLMv2Model',
'LayoutLMv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__snake_case : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 615 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__snake_case : Tuple = logging.get_logger(__name__)
def _lowercase ( __snake_case ,__snake_case ) -> Dict:
__lowerCAmelCase : Optional[Any] = set()
__lowerCAmelCase : List[Any] = []
def parse_line(__snake_case ):
for line in fp:
if isinstance(__snake_case ,__snake_case ):
__lowerCAmelCase : Tuple = line.decode("UTF-8" )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(" " ):
# process a single warning and move it to `selected_warnings`.
if len(__snake_case ) > 0:
__lowerCAmelCase : List[Any] = "\n".join(__snake_case )
# Only keep the warnings specified in `targets`
if any(F""": {x}: """ in warning for x in targets ):
selected_warnings.add(__snake_case )
buffer.clear()
continue
else:
__lowerCAmelCase : List[str] = line.strip()
buffer.append(__snake_case )
if from_gh:
for filename in os.listdir(__snake_case ):
__lowerCAmelCase : List[str] = os.path.join(__snake_case ,__snake_case )
if not os.path.isdir(__snake_case ):
# read the file
if filename != "warnings.txt":
continue
with open(__snake_case ) as fp:
parse_line(__snake_case )
else:
try:
with zipfile.ZipFile(__snake_case ) as z:
for filename in z.namelist():
if not os.path.isdir(__snake_case ):
# read the file
if filename != "warnings.txt":
continue
with z.open(__snake_case ) as fp:
parse_line(__snake_case )
except Exception:
logger.warning(
F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.""" )
return selected_warnings
def _lowercase ( __snake_case ,__snake_case ) -> Any:
__lowerCAmelCase : Any = set()
__lowerCAmelCase : str = [os.path.join(__snake_case ,__snake_case ) for p in os.listdir(__snake_case ) if (p.endswith(".zip" ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(__snake_case ,__snake_case ) )
return selected_warnings
if __name__ == "__main__":
def _lowercase ( __snake_case ) -> Optional[Any]:
return values.split("," )
__snake_case : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='Where to store the downloaded artifacts and other result files.',
)
parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
# optional parameters
parser.add_argument(
'--targets',
default='DeprecationWarning,UserWarning,FutureWarning',
type=list_str,
help='Comma-separated list of target warning(s) which we want to extract.',
)
parser.add_argument(
'--from_gh',
action='store_true',
help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
)
__snake_case : Tuple = parser.parse_args()
__snake_case : Any = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__snake_case : str = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print('=' * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__snake_case : Tuple = extract_warnings(args.output_dir, args.targets)
__snake_case : List[str] = sorted(selected_warnings)
with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4) | 615 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class _a :
'''simple docstring'''
def __init__( self ,__a ,__a=13 ,__a=7 ,__a=6 ,__a=17 ,__a=23 ,__a=11 ,__a=True ,) -> List[Any]:
snake_case : List[str] = parent
snake_case : Union[str, Any] = batch_size
snake_case : List[str] = seq_length
snake_case : List[Any] = act_dim
snake_case : Optional[int] = state_dim
snake_case : Union[str, Any] = hidden_size
snake_case : Any = max_length
snake_case : Any = is_training
def snake_case_ ( self ) -> Union[str, Any]:
snake_case : Union[str, Any] = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
snake_case : Tuple = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
snake_case : Any = floats_tensor((self.batch_size, self.seq_length, 1) )
snake_case : List[Any] = floats_tensor((self.batch_size, self.seq_length, 1) )
snake_case : str = ids_tensor((self.batch_size, self.seq_length) ,vocab_size=1_000 )
snake_case : Any = random_attention_mask((self.batch_size, self.seq_length) )
snake_case : int = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def snake_case_ ( self ) -> Optional[int]:
return DecisionTransformerConfig(
batch_size=self.batch_size ,seq_length=self.seq_length ,act_dim=self.act_dim ,state_dim=self.state_dim ,hidden_size=self.hidden_size ,max_length=self.max_length ,)
def snake_case_ ( self ,__a ,__a ,__a ,__a ,__a ,__a ,__a ,) -> Dict:
snake_case : Any = DecisionTransformerModel(config=__a )
model.to(__a )
model.eval()
snake_case : Tuple = model(__a ,__a ,__a ,__a ,__a ,__a )
self.parent.assertEqual(result.state_preds.shape ,states.shape )
self.parent.assertEqual(result.action_preds.shape ,actions.shape )
self.parent.assertEqual(result.return_preds.shape ,returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def snake_case_ ( self ) -> int:
snake_case : Optional[Any] = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) : int = config_and_inputs
snake_case : int = {
"""states""": states,
"""actions""": actions,
"""rewards""": rewards,
"""returns_to_go""": returns_to_go,
"""timesteps""": timesteps,
"""attention_mask""": attention_mask,
}
return config, inputs_dict
@require_torch
class _a (a__, a__, a__, unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = (DecisionTransformerModel,) if is_torch_available() else ()
lowerCAmelCase_ : Tuple = ()
lowerCAmelCase_ : str = {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
lowerCAmelCase_ : List[str] = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
lowerCAmelCase_ : Dict = False
lowerCAmelCase_ : int = False
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : List[Any] = False
lowerCAmelCase_ : List[Any] = False
lowerCAmelCase_ : Any = False
lowerCAmelCase_ : Optional[Any] = False
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : Any = False
def snake_case_ ( self ) -> List[Any]:
snake_case : int = DecisionTransformerModelTester(self )
snake_case : int = ConfigTester(self ,config_class=__a ,hidden_size=37 )
def snake_case_ ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def snake_case_ ( self ) -> int:
snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
@slow
def snake_case_ ( self ) -> List[Any]:
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case : Union[str, Any] = DecisionTransformerModel.from_pretrained(__a )
self.assertIsNotNone(__a )
def snake_case_ ( self ) -> Optional[Any]:
snake_case , snake_case : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : Optional[int] = model_class(__a )
snake_case : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case : Union[str, Any] = [*signature.parameters.keys()]
snake_case : Optional[Any] = [
"""states""",
"""actions""",
"""rewards""",
"""returns_to_go""",
"""timesteps""",
"""attention_mask""",
]
self.assertListEqual(arg_names[: len(__a )] ,__a )
@require_torch
class _a (unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case_ ( self ) -> List[Any]:
snake_case : Optional[int] = 2 # number of steps of autoregressive prediction we will perform
snake_case : Union[str, Any] = 10 # defined by the RL environment, may be normalized
snake_case : Tuple = DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert""" )
snake_case : List[Any] = model.to(__a )
snake_case : int = model.config
torch.manual_seed(0 )
snake_case : Optional[int] = torch.randn(1 ,1 ,config.state_dim ).to(device=__a ,dtype=torch.floataa ) # env.reset()
snake_case : List[str] = torch.tensor(
[[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]] ,device=__a )
snake_case : Optional[Any] = torch.tensor(__a ,device=__a ,dtype=torch.floataa ).reshape(1 ,1 ,1 )
snake_case : Optional[int] = state
snake_case : List[Any] = torch.zeros(1 ,0 ,config.act_dim ,device=__a ,dtype=torch.floataa )
snake_case : Tuple = torch.zeros(1 ,0 ,device=__a ,dtype=torch.floataa )
snake_case : Tuple = torch.tensor(0 ,device=__a ,dtype=torch.long ).reshape(1 ,1 )
for step in range(__a ):
snake_case : str = torch.cat([actions, torch.zeros(1 ,1 ,config.act_dim ,device=__a )] ,dim=1 )
snake_case : Tuple = torch.cat([rewards, torch.zeros(1 ,1 ,device=__a )] ,dim=1 )
snake_case : List[str] = torch.ones(1 ,states.shape[1] ).to(dtype=torch.long ,device=states.device )
with torch.no_grad():
snake_case , snake_case , snake_case : List[Any] = model(
states=__a ,actions=__a ,rewards=__a ,returns_to_go=__a ,timesteps=__a ,attention_mask=__a ,return_dict=__a ,)
self.assertEqual(action_pred.shape ,actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] ,expected_outputs[step] ,atol=1E-4 ) )
snake_case , snake_case , snake_case , snake_case : str = ( # env.step(action)
torch.randn(1 ,1 ,config.state_dim ).to(device=__a ,dtype=torch.floataa ),
1.0,
False,
{},
)
snake_case : Dict = action_pred[0, -1]
snake_case : Optional[int] = torch.cat([states, state] ,dim=1 )
snake_case : Tuple = returns_to_go[0, -1] - reward
snake_case : Tuple = torch.cat([returns_to_go, pred_return.reshape(1 ,1 ,1 )] ,dim=1 )
snake_case : List[str] = torch.cat(
[timesteps, torch.ones((1, 1) ,device=__a ,dtype=torch.long ) * (step + 1)] ,dim=1 )
| 116 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCamelCase__ ( __lowercase ):
snake_case : List[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case : str = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case : Any = 0.01
with locka.acquire():
with pytest.raises(__lowercase ):
snake_case : Union[str, Any] = time.time()
locka.acquire(__lowercase )
assert time.time() - _start > timeout
def lowerCamelCase__ ( __lowercase ):
snake_case : List[Any] = """a""" * 1_000 + """.lock"""
snake_case : List[str] = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(__lowercase )
assert len(os.path.basename(locka._lock_file ) ) <= 255
snake_case : Any = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(__lowercase ):
locka.acquire(0 )
| 116 | 1 |
from __future__ import annotations
lowercase_: Union[str, Any] = 1.6_021e-19 # units = C
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
"""simple docstring"""
if (conductivity, electron_conc, mobility).count(0) != 1:
raise ValueError("""You cannot supply more or less than 2 values""")
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""")
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""")
elif mobility < 0:
raise ValueError("""mobility cannot be negative""")
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 127 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowercase_: Union[str, Any] = logging.get_logger(__name__)
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
if isinstance(UpperCAmelCase_ , np.ndarray):
return list(tensor.shape)
snake_case__ : List[Any] = tf.shape(UpperCAmelCase_)
if tensor.shape == tf.TensorShape(UpperCAmelCase_):
return dynamic
snake_case__ : Tuple = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase_)]
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ = None , UpperCAmelCase_ = None):
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase_ , name=UpperCAmelCase_)
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=1e-5 , UpperCAmelCase_=-1):
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase_ , UpperCAmelCase_):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""")
# Get mean and variance on the axis to be normalized
snake_case__ , snake_case__ : Any = tf.nn.moments(UpperCAmelCase_ , axes=[axis] , keepdims=UpperCAmelCase_)
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
snake_case__ : Optional[Any] = [1] * inputs.shape.rank
snake_case__ : Optional[int] = shape_list(UpperCAmelCase_)[axis]
snake_case__ : Tuple = tf.reshape(UpperCAmelCase_ , UpperCAmelCase_)
snake_case__ : Tuple = tf.reshape(UpperCAmelCase_ , UpperCAmelCase_)
# Compute layer normalization using the batch_normalization
# function.
snake_case__ : List[str] = tf.nn.batch_normalization(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , offset=UpperCAmelCase_ , scale=UpperCAmelCase_ , variance_epsilon=UpperCAmelCase_ , )
return outputs
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_=0 , UpperCAmelCase_=-1):
"""simple docstring"""
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
snake_case__ : Optional[int] = tf.shape(UpperCAmelCase_)
snake_case__ : List[Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1])
snake_case__ : Optional[Any] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0)
return tf.reshape(UpperCAmelCase_ , UpperCAmelCase_)
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , tf.Tensor):
snake_case__ : int = tf.convert_to_tensor(UpperCAmelCase_) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
snake_case__ : Dict = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
snake_case__ : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
snake_case__ : Dict = (
tf.cast(1 , encoder_attention_mask.dtype) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = "input_ids"):
"""simple docstring"""
tf.debugging.assert_less(
UpperCAmelCase_ , tf.cast(UpperCAmelCase_ , dtype=tensor.dtype) , message=(
F'The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase_)}) must be smaller than the embedding '
F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
) , )
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
snake_case__ : List[str] = 64_512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
snake_case__ : Tuple = [x for x in data if len(UpperCAmelCase_) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
F'bytes: {bad_attributes}')
snake_case__ : Optional[int] = np.asarray(UpperCAmelCase_)
snake_case__ : Tuple = 1
snake_case__ : Optional[Any] = np.array_split(UpperCAmelCase_ , UpperCAmelCase_)
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
num_chunks += 1
snake_case__ : Tuple = np.array_split(UpperCAmelCase_ , UpperCAmelCase_)
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase_):
snake_case__ : List[str] = chunk_data
else:
snake_case__ : Union[str, Any] = data
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
if name in group.attrs:
snake_case__ : int = [n.decode("""utf8""") if hasattr(UpperCAmelCase_ , """decode""") else n for n in group.attrs[name]]
else:
snake_case__ : Tuple = []
snake_case__ : Optional[Any] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""") if hasattr(UpperCAmelCase_ , """decode""") else n for n in group.attrs["""%s%d""" % (name, chunk_id)]])
chunk_id += 1
return data
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
def _expand_single_ad_tensor(UpperCAmelCase_):
if isinstance(UpperCAmelCase_ , tf.Tensor) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase_ , axis=-1)
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase_)
| 127 | 1 |
"""simple docstring"""
def lowercase__ ( lowerCamelCase = 100 ):
_SCREAMING_SNAKE_CASE : Any = (n * (n + 1) // 2) ** 2
_SCREAMING_SNAKE_CASE : List[Any] = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'{solution() = }')
| 621 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 621 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__UpperCamelCase : int = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCamelCase : Optional[Any] = '''
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)["depth"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline("depth-estimation")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to("cuda")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> img = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
>>> prompt = "A robot, 4k photo"
>>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
>>> generator = torch.Generator(device="cuda").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save("robot_cat.png")
```
'''
def lowercase ( lowerCAmelCase : Dict , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : str=8):
"""simple docstring"""
_A : List[Any] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_A : Tuple = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCamelCase__ ( snake_case_ ):
"""simple docstring"""
def __init__( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> List[Any]:
super().__init__()
self.register_modules(
unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ , movq=UpperCAmelCase__ , )
_A : Dict = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def _lowerCamelCase ( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Optional[Any]:
if latents is None:
_A : Optional[int] = randn_tensor(UpperCAmelCase__ , generator=UpperCAmelCase__ , device=UpperCAmelCase__ , dtype=UpperCAmelCase__ )
else:
if latents.shape != shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
_A : Tuple = latents.to(UpperCAmelCase__ )
_A : Tuple = latents * scheduler.init_noise_sigma
return latents
def _lowerCamelCase ( self , UpperCAmelCase__=0 ) -> Dict:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
_A : int = torch.device(F"""cuda:{gpu_id}""" )
_A : Optional[int] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowerCamelCase ( self , UpperCAmelCase__=0 ) -> Optional[Any]:
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
_A : Any = torch.device(F"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=UpperCAmelCase__ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_A : Union[str, Any] = None
for cpu_offloaded_model in [self.unet, self.movq]:
_A , _A : str = cpu_offload_with_hook(UpperCAmelCase__ , UpperCAmelCase__ , prev_module_hook=UpperCAmelCase__ )
# We'll offload the last model manually.
_A : Tuple = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _lowerCamelCase ( self ) -> Any:
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(UpperCAmelCase__ , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(UpperCAmelCase__ )
def __call__( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = 5_1_2 , UpperCAmelCase__ = 5_1_2 , UpperCAmelCase__ = 1_0_0 , UpperCAmelCase__ = 4.0 , UpperCAmelCase__ = 1 , UpperCAmelCase__ = None , UpperCAmelCase__ = None , UpperCAmelCase__ = "pil" , UpperCAmelCase__ = True , ) -> Optional[Any]:
_A : Optional[int] = self._execution_device
_A : str = guidance_scale > 1.0
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_A : Optional[Any] = torch.cat(UpperCAmelCase__ , dim=0 )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_A : Union[str, Any] = torch.cat(UpperCAmelCase__ , dim=0 )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_A : List[Any] = torch.cat(UpperCAmelCase__ , dim=0 )
_A : Tuple = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
_A : str = image_embeds.repeat_interleave(UpperCAmelCase__ , dim=0 )
_A : Any = negative_image_embeds.repeat_interleave(UpperCAmelCase__ , dim=0 )
_A : Any = hint.repeat_interleave(UpperCAmelCase__ , dim=0 )
_A : List[str] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=UpperCAmelCase__ )
_A : Any = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=UpperCAmelCase__ )
self.scheduler.set_timesteps(UpperCAmelCase__ , device=UpperCAmelCase__ )
_A : Tuple = self.scheduler.timesteps
_A : Union[str, Any] = self.movq.config.latent_channels
_A , _A : List[str] = downscale_height_and_width(UpperCAmelCase__ , UpperCAmelCase__ , self.movq_scale_factor )
# create initial latent
_A : List[Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , self.scheduler , )
for i, t in enumerate(self.progress_bar(UpperCAmelCase__ ) ):
# expand the latents if we are doing classifier free guidance
_A : Tuple = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_A : Tuple = {'''image_embeds''': image_embeds, '''hint''': hint}
_A : List[Any] = self.unet(
sample=UpperCAmelCase__ , timestep=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , added_cond_kwargs=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , )[0]
if do_classifier_free_guidance:
_A , _A : Union[str, Any] = noise_pred.split(latents.shape[1] , dim=1 )
_A , _A : Union[str, Any] = noise_pred.chunk(2 )
_A , _A : Optional[Any] = variance_pred.chunk(2 )
_A : Any = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_A : Dict = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
_A , _A : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_A : Optional[Any] = self.scheduler.step(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , generator=UpperCAmelCase__ , )[0]
# post-processing
_A : Tuple = self.movq.decode(UpperCAmelCase__ , force_not_quantize=UpperCAmelCase__ )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
_A : Union[str, Any] = image * 0.5 + 0.5
_A : Tuple = image.clamp(0 , 1 )
_A : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_A : Tuple = self.numpy_to_pil(UpperCAmelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCAmelCase__ )
| 417 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
__UpperCamelCase : Union[str, Any] = namedtuple('''covid_data''', '''cases deaths recovered''')
def lowercase ( lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/"):
"""simple docstring"""
_A : int = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(lowerCAmelCase).content).xpath(lowerCAmelCase))
__UpperCamelCase : List[Any] = '''Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}'''
print(fmt.format(*covid_stats()))
| 417 | 1 |
"""simple docstring"""
a__ : int = 8.314462 # Unit - J mol-1 K-1
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 589 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
a__ : int = logging.get_logger()
@dataclass
class __magic_name__ :
UpperCamelCase : nn.Module
UpperCamelCase : List[nn.Module] = field(default_factory=_UpperCamelCase )
UpperCamelCase : list = field(default_factory=_UpperCamelCase )
def _lowerCamelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ):
"""simple docstring"""
_lowerCAmelCase = len(list(m.modules() ) ) == 1 or isinstance(__magic_name__ , nn.Convad ) or isinstance(__magic_name__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(__magic_name__ )
def __call__( self , __magic_name__ ):
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(__magic_name__ )
[x.remove() for x in self.handles]
return self
@property
def _lowerCamelCase ( self ):
"""simple docstring"""
return list(filter(lambda __magic_name__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class __magic_name__ :
UpperCamelCase : nn.Module
UpperCamelCase : nn.Module
UpperCamelCase : int = 1
UpperCamelCase : List = field(default_factory=_UpperCamelCase )
UpperCamelCase : List = field(default_factory=_UpperCamelCase )
UpperCamelCase : bool = True
def __call__( self , __magic_name__ ):
"""simple docstring"""
_lowerCAmelCase = Tracker(self.dest )(__magic_name__ ).parametrized
_lowerCAmelCase = Tracker(self.src )(__magic_name__ ).parametrized
_lowerCAmelCase = list(filter(lambda __magic_name__ : type(__magic_name__ ) not in self.src_skip , __magic_name__ ) )
_lowerCAmelCase = list(filter(lambda __magic_name__ : type(__magic_name__ ) not in self.dest_skip , __magic_name__ ) )
if len(__magic_name__ ) != len(__magic_name__ ) and self.raise_if_mismatch:
raise Exception(
F'''Numbers of operations are different. Source module has {len(__magic_name__ )} operations while'''
F''' destination module has {len(__magic_name__ )}.''' )
for dest_m, src_m in zip(__magic_name__ , __magic_name__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
class __magic_name__ ( nn.Module ):
def __init__( self , __magic_name__ ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F'''Unexpected layer name {k}'''
_lowerCAmelCase = len(__magic_name__ ) + 1
feature_blocks.append((F'''res{block_index}''', v) )
_lowerCAmelCase = nn.ModuleDict(__magic_name__ )
def _lowerCamelCase ( self , __magic_name__ ):
"""simple docstring"""
return get_trunk_forward_outputs(
__magic_name__ , out_feat_keys=__magic_name__ , feature_blocks=self._feature_blocks , )
class __magic_name__ ( _UpperCamelCase ):
def _lowerCamelCase ( self , __magic_name__ ):
"""simple docstring"""
_lowerCAmelCase = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self , __magic_name__ ):
"""simple docstring"""
if x not in self:
_lowerCAmelCase = self.convert_name_to_timm(__magic_name__ )
_lowerCAmelCase = partial(lambda: (timm.create_model(__magic_name__ , pretrained=__magic_name__ ).eval(), None) )
else:
_lowerCAmelCase = super().__getitem__(__magic_name__ )
return val
class __magic_name__ ( _UpperCamelCase ):
def __getitem__( self , __magic_name__ ):
"""simple docstring"""
if "seer" in x and "in1k" not in x:
_lowerCAmelCase = RegNetModel
else:
_lowerCAmelCase = RegNetForImageClassification
return val
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
for from_key, to_key in keys:
_lowerCAmelCase = from_state_dict[from_key].clone()
print(F'''Copied key={from_key} to={to_key}''' )
return to_state_dict
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = True, ):
"""simple docstring"""
print(F'''Converting {name}...''' )
with torch.no_grad():
_lowerCAmelCase , _lowerCAmelCase = from_model_func()
_lowerCAmelCase = our_model_func(__lowerCamelCase ).eval()
_lowerCAmelCase = ModuleTransfer(src=__lowerCamelCase, dest=__lowerCamelCase, raise_if_mismatch=__lowerCamelCase )
_lowerCAmelCase = torch.randn((1, 3, 2_2_4, 2_2_4) )
module_transfer(__lowerCamelCase )
if from_state_dict is not None:
_lowerCAmelCase = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
_lowerCAmelCase = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
_lowerCAmelCase = manually_copy_vissl_head(__lowerCamelCase, our_model.state_dict(), __lowerCamelCase )
our_model.load_state_dict(__lowerCamelCase )
_lowerCAmelCase = our_model(__lowerCamelCase, output_hidden_states=__lowerCamelCase )
_lowerCAmelCase = (
our_outputs.logits if isinstance(__lowerCamelCase, __lowerCamelCase ) else our_outputs.last_hidden_state
)
_lowerCAmelCase = from_model(__lowerCamelCase )
_lowerCAmelCase = from_output[-1] if type(__lowerCamelCase ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
_lowerCAmelCase = our_outputs.hidden_states[-1]
assert torch.allclose(__lowerCamelCase, __lowerCamelCase ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add model', use_temp_dir=__lowerCamelCase, )
_lowerCAmelCase = 2_2_4 if 'seer' not in name else 3_8_4
# we can use the convnext one
_lowerCAmelCase = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k', size=__lowerCamelCase )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add image processor', use_temp_dir=__lowerCamelCase, )
print(F'''Pushed {name}''' )
def A__ ( __lowerCamelCase, __lowerCamelCase = None, __lowerCamelCase = True ):
"""simple docstring"""
_lowerCAmelCase = 'imagenet-1k-id2label.json'
_lowerCAmelCase = 1_0_0_0
_lowerCAmelCase = (1, num_labels)
_lowerCAmelCase = 'huggingface/label-files'
_lowerCAmelCase = num_labels
_lowerCAmelCase = json.load(open(cached_download(hf_hub_url(__lowerCamelCase, __lowerCamelCase, repo_type='dataset' ) ), 'r' ) )
_lowerCAmelCase = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = idalabel
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = partial(__lowerCamelCase, num_labels=__lowerCamelCase, idalabel=__lowerCamelCase, labelaid=__lowerCamelCase )
_lowerCAmelCase = {
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8], groups_width=8, layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 1_2], hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4], groups_width=1_6, layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8], groups_width=2_4, layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2], groups_width=1_6, layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 1_0, 2], hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2], groups_width=2_4, layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 1_5, 2], hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8], groups_width=4_8, layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 1_4, 2], hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0], groups_width=4_0, layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 1_0, 1], hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4], groups_width=5_6, layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 1_5, 1], hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0], groups_width=1_2_0, layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 1_1, 1], hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0], groups_width=1_1_2, layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 1_3, 1], hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8], groups_width=1_2_8, layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 1_3, 1], hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0], groups_width=1_6_8, layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8], groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0], groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8], groups_width=1_6 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8], groups_width=1_6 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 1_7, 2], hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8], groups_width=2_4 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 1_3, 1], hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2], groups_width=2_4 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 1_2, 2], hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8], groups_width=6_4 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 1_4, 2], hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6], groups_width=7_2 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 1_0, 1], hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6], groups_width=5_6 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 1_1, 1], hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0], groups_width=1_1_2 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 1_1, 1], hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4], groups_width=1_1_2 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 1_2, 1], hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2], groups_width=2_3_2 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 1_2, 1], hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2], groups_width=2_3_2 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 1_2, 1], hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0], groups_width=3_2_8 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 1_7, 1], hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2], groups_width=2_6_4 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 1_6, 1], hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8], groups_width=6_4_0 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 1_7, 1], hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0], groups_width=1_0_1_0 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 1_2, 1], hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2], groups_width=2_3_2 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 1_2, 1], hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0], groups_width=3_2_8 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 1_7, 1], hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2], groups_width=2_6_4 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 1_6, 1], hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8], groups_width=6_4_0 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 1_7, 1], hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0], groups_width=1_0_1_0 ),
}
_lowerCAmelCase = NameToOurModelFuncMap()
_lowerCAmelCase = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(__lowerCamelCase, __lowerCamelCase ) -> Tuple[nn.Module, Dict]:
_lowerCAmelCase = torch.hub.load_state_dict_from_url(__lowerCamelCase, model_dir=str(__lowerCamelCase ), map_location='cpu' )
_lowerCAmelCase = model_func()
# check if we have a head, if yes add it
_lowerCAmelCase = files['classy_state_dict']['base_model']['model']
_lowerCAmelCase = model_state_dict['trunk']
model.load_state_dict(__lowerCamelCase )
return model.eval(), model_state_dict["heads"]
# pretrained
_lowerCAmelCase = partial(
__lowerCamelCase, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
_lowerCAmelCase = partial(
__lowerCamelCase, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
_lowerCAmelCase = partial(
__lowerCamelCase, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
_lowerCAmelCase = partial(
__lowerCamelCase, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=2_7, group_width=1_0_1_0, w_a=1_7_4_4, w_a=620.83, w_m=2.52 ) ) ), )
# IN1K finetuned
_lowerCAmelCase = partial(
__lowerCamelCase, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
_lowerCAmelCase = partial(
__lowerCamelCase, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
_lowerCAmelCase = partial(
__lowerCamelCase, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
_lowerCAmelCase = partial(
__lowerCamelCase, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=2_7, group_width=1_0_1_0, w_a=1_7_4_4, w_a=620.83, w_m=2.52 ) ) ), )
if model_name:
convert_weight_and_push(
__lowerCamelCase, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], __lowerCamelCase, __lowerCamelCase, )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
__lowerCamelCase, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, )
return config, expected_shape
if __name__ == "__main__":
a__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
""" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
a__ : Optional[int] = parser.parse_args()
a__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 589 | 1 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def _snake_case ( ) -> Dict:
__a : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=lowercase , default="""biencoder-nq-dev.json""" , help="""Path to raw DPR training data""" , )
parser.add_argument(
"""--evaluation_set""" , type=lowercase , help="""where to store parsed evaluation_set file""" , )
parser.add_argument(
"""--gold_data_path""" , type=lowercase , help="""where to store parsed gold_data_path file""" , )
__a : Union[str, Any] = parser.parse_args()
with open(args.src_path , """r""" ) as src_file, open(args.evaluation_set , """w""" ) as eval_file, open(
args.gold_data_path , """w""" ) as gold_file:
__a : List[str] = json.load(lowercase )
for dpr_record in tqdm(lowercase ):
__a : Dict = dpr_record["""question"""]
__a : Dict = [context["""title"""] for context in dpr_record["""positive_ctxs"""]]
eval_file.write(question + """\n""" )
gold_file.write("""\t""".join(lowercase ) + """\n""" )
if __name__ == "__main__":
main() | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
__a : List[str] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__a : int = mid + 1
else:
__a : int = mid
return lo
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Any = len(lowercase )
while lo < hi:
__a : Any = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__a : List[str] = mid + 1
else:
__a : Any = mid
return lo
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> None:
sorted_collection.insert(bisect_left(lowercase , lowercase , lowercase , lowercase ) , lowercase )
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> None:
sorted_collection.insert(bisect_right(lowercase , lowercase , lowercase , lowercase ) , lowercase )
def _snake_case ( lowercase , lowercase ) -> int | None:
__a : Dict = 0
__a : Any = len(lowercase ) - 1
while left <= right:
__a : str = left + (right - left) // 2
__a : List[Any] = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__a : Optional[Any] = midpoint - 1
else:
__a : Optional[int] = midpoint + 1
return None
def _snake_case ( lowercase , lowercase ) -> int | None:
__a : Optional[int] = bisect.bisect_left(lowercase , lowercase )
if index != len(lowercase ) and sorted_collection[index] == item:
return index
return None
def _snake_case ( lowercase , lowercase , lowercase , lowercase ) -> int | None:
if right < left:
return None
__a : Any = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(lowercase , lowercase , lowercase , midpoint - 1 )
else:
return binary_search_by_recursion(lowercase , lowercase , midpoint + 1 , lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : List[Any] = input('Enter numbers separated by comma:\n').strip()
__SCREAMING_SNAKE_CASE : Optional[Any] = sorted(int(item) for item in user_input.split(','))
__SCREAMING_SNAKE_CASE : List[str] = int(input('Enter a single number to be found in the list:\n'))
__SCREAMING_SNAKE_CASE : Optional[int] = binary_search(collection, target)
if result is None:
print(f'''{target} was not found in {collection}.''')
else:
print(f'''{target} was found at position {result} in {collection}.''') | 697 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
find_tied_parameters,
get_balanced_memory,
infer_auto_device_map,
load_checkpoint_in_model,
offload_weight,
set_module_tensor_to_device,
)
if is_bnb_available():
import bitsandbytes as bnb
from copy import deepcopy
A__ : List[Any] = logging.getLogger(__name__)
def UpperCamelCase( __UpperCamelCase : torch.nn.Module ,__UpperCamelCase : BnbQuantizationConfig ,__UpperCamelCase : Union[str, os.PathLike] = None ,__UpperCamelCase : Optional[Dict[str, Union[int, str, torch.device]]] = None ,__UpperCamelCase : Optional[List[str]] = None ,__UpperCamelCase : Optional[Dict[Union[int, str], Union[int, str]]] = None ,__UpperCamelCase : Optional[Union[str, os.PathLike]] = None ,__UpperCamelCase : bool = False ,):
lowerCAmelCase_ : Dict = bnb_quantization_config.load_in_abit
lowerCAmelCase_ : int = bnb_quantization_config.load_in_abit
if load_in_abit and not is_abit_bnb_available():
raise ImportError(
'''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
''' make sure you have the latest version of `bitsandbytes` installed.''' )
if load_in_abit and not is_abit_bnb_available():
raise ValueError(
'''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
'''make sure you have the latest version of `bitsandbytes` installed.''' )
lowerCAmelCase_ : Optional[int] = []
# custom device map
if isinstance(__UpperCamelCase ,__UpperCamelCase ) and len(device_map.keys() ) > 1:
lowerCAmelCase_ : Dict = [key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if bnb_quantization_config.skip_modules is None:
lowerCAmelCase_ : List[Any] = get_keys_to_not_convert(__UpperCamelCase )
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(__UpperCamelCase )
lowerCAmelCase_ : Optional[Any] = bnb_quantization_config.skip_modules
# We add the modules we want to keep in full precision
if bnb_quantization_config.keep_in_fpaa_modules is None:
lowerCAmelCase_ : int = []
lowerCAmelCase_ : Any = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(__UpperCamelCase )
# compatibility with peft
lowerCAmelCase_ : List[str] = load_in_abit
lowerCAmelCase_ : Tuple = load_in_abit
lowerCAmelCase_ : str = get_parameter_device(__UpperCamelCase )
if model_device.type != "meta":
# quantization of an already loaded model
logger.warning(
'''It is not recommended to quantize a loaded model. '''
'''The model should be instantiated under the `init_empty_weights` context manager.''' )
lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(__UpperCamelCase ,__UpperCamelCase ,modules_to_not_convert=__UpperCamelCase )
# convert param to the right dtype
lowerCAmelCase_ : Optional[int] = bnb_quantization_config.torch_dtype
for name, param in model.state_dict().items():
if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
param.to(torch.floataa )
if param.dtype != torch.floataa:
lowerCAmelCase_ : List[str] = name.replace('''.weight''' ,'''''' ).replace('''.bias''' ,'''''' )
lowerCAmelCase_ : Dict = getattr(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
if param is not None:
param.to(torch.floataa )
elif torch.is_floating_point(__UpperCamelCase ):
param.to(__UpperCamelCase )
if model_device.type == "cuda":
# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
model.cuda(torch.cuda.current_device() )
torch.cuda.empty_cache()
elif torch.cuda.is_available():
model.to(torch.cuda.current_device() )
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info(
f"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
'''We move the model to cuda.''' )
return model
elif weights_location is None:
raise RuntimeError(
f"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """ )
else:
with init_empty_weights():
lowerCAmelCase_ : Optional[Any] = replace_with_bnb_layers(
__UpperCamelCase ,__UpperCamelCase ,modules_to_not_convert=__UpperCamelCase )
lowerCAmelCase_ : int = get_quantized_model_device_map(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,max_memory=__UpperCamelCase ,no_split_module_classes=__UpperCamelCase ,)
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : int = any(x in list(device_map.values() ) for x in ['''cpu''', '''disk'''] )
load_checkpoint_in_model(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,dtype=bnb_quantization_config.torch_dtype ,offload_folder=__UpperCamelCase ,offload_state_dict=__UpperCamelCase ,keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules ,offload_abit_bnb=load_in_abit and offload ,)
return dispatch_model(__UpperCamelCase ,device_map=__UpperCamelCase ,offload_dir=__UpperCamelCase )
def UpperCamelCase( __UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Optional[Any] ,__UpperCamelCase : Optional[int]=None ,__UpperCamelCase : Dict=None ,__UpperCamelCase : str=None ):
if device_map is None:
if torch.cuda.is_available():
lowerCAmelCase_ : str = {'''''': torch.cuda.current_device()}
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.''' )
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
'''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
'''\'sequential\'.''' )
lowerCAmelCase_ : List[Any] = {}
special_dtypes.update(
{
name: bnb_quantization_config.torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.skip_modules )
} )
special_dtypes.update(
{
name: torch.floataa
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
} )
lowerCAmelCase_ : Union[str, Any] = {}
lowerCAmelCase_ : Any = special_dtypes
lowerCAmelCase_ : Tuple = no_split_module_classes
lowerCAmelCase_ : Optional[int] = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
lowerCAmelCase_ : Tuple = get_balanced_memory(
__UpperCamelCase ,low_zero=(device_map == '''balanced_low_0''') ,max_memory=__UpperCamelCase ,**__UpperCamelCase ,)
lowerCAmelCase_ : Tuple = max_memory
lowerCAmelCase_ : Dict = infer_auto_device_map(__UpperCamelCase ,**__UpperCamelCase )
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
# check if don't have any quantized module on the cpu
lowerCAmelCase_ : int = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
lowerCAmelCase_ : Tuple = {
key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
}
for device in ["cpu", "disk"]:
if device in device_map_without_some_modules.values():
if bnb_quantization_config.load_in_abit:
raise ValueError(
'''
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
these modules in `torch_dtype`, you need to pass a custom `device_map` to
`load_and_quantize_model`. Check
https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
for more details.
''' )
else:
logger.info(
'''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit''' )
del device_map_without_some_modules
return device_map
def UpperCamelCase( __UpperCamelCase : Any ,__UpperCamelCase : List[str] ,__UpperCamelCase : int=None ,__UpperCamelCase : List[str]=None ):
if modules_to_not_convert is None:
lowerCAmelCase_ : Any = []
lowerCAmelCase_ , lowerCAmelCase_ : Dict = _replace_with_bnb_layers(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def UpperCamelCase( __UpperCamelCase : Any ,__UpperCamelCase : Optional[Any] ,__UpperCamelCase : str=None ,__UpperCamelCase : Dict=None ,):
lowerCAmelCase_ : List[Any] = False
for name, module in model.named_children():
if current_key_name is None:
lowerCAmelCase_ : str = []
current_key_name.append(__UpperCamelCase )
if isinstance(__UpperCamelCase ,nn.Linear ) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
lowerCAmelCase_ : Optional[Any] = '''.'''.join(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = True
for key in modules_to_not_convert:
if (
(key in current_key_name_str) and (key + "." in current_key_name_str)
) or key == current_key_name_str:
lowerCAmelCase_ : Any = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Optional[Any] = bnb.nn.LinearabitLt(
module.in_features ,module.out_features ,module.bias is not None ,has_fpaa_weights=__UpperCamelCase ,threshold=bnb_quantization_config.llm_inta_threshold ,)
elif bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Optional[Any] = bnb.nn.Linearabit(
module.in_features ,module.out_features ,module.bias is not None ,bnb_quantization_config.bnb_abit_compute_dtype ,compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant ,quant_type=bnb_quantization_config.bnb_abit_quant_type ,)
else:
raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False''' )
lowerCAmelCase_ : List[str] = module.weight.data
if module.bias is not None:
lowerCAmelCase_ : List[str] = module.bias.data
bnb_module.requires_grad_(__UpperCamelCase )
setattr(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
lowerCAmelCase_ : Optional[Any] = True
if len(list(module.children() ) ) > 0:
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = _replace_with_bnb_layers(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
lowerCAmelCase_ : List[str] = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def UpperCamelCase( __UpperCamelCase : List[Any] ):
# Create a copy of the model
with init_empty_weights():
lowerCAmelCase_ : Optional[Any] = deepcopy(__UpperCamelCase ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
lowerCAmelCase_ : List[str] = find_tied_parameters(__UpperCamelCase )
# For compatibility with Accelerate < 0.18
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCAmelCase_ : Union[str, Any] = sum(list(tied_params.values() ) ,[] ) + list(tied_params.keys() )
else:
lowerCAmelCase_ : List[Any] = sum(__UpperCamelCase ,[] )
lowerCAmelCase_ : Tuple = len(__UpperCamelCase ) > 0
# Check if it is a base model
lowerCAmelCase_ : Optional[Any] = False
if hasattr(__UpperCamelCase ,'''base_model_prefix''' ):
lowerCAmelCase_ : List[Any] = not hasattr(__UpperCamelCase ,model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
lowerCAmelCase_ : List[str] = list(model.named_children() )
lowerCAmelCase_ : Any = [list_modules[-1][0]]
# add last module together with tied weights
lowerCAmelCase_ : Tuple = set(__UpperCamelCase ) - set(__UpperCamelCase )
lowerCAmelCase_ : List[str] = list(set(__UpperCamelCase ) ) + list(__UpperCamelCase )
# remove ".weight" from the keys
lowerCAmelCase_ : str = ['''.weight''', '''.bias''']
lowerCAmelCase_ : str = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowerCAmelCase_ : List[str] = name.replace(__UpperCamelCase ,'''''' )
filtered_module_names.append(__UpperCamelCase )
return filtered_module_names
def UpperCamelCase( __UpperCamelCase : Optional[int] ):
for m in model.modules():
if isinstance(__UpperCamelCase ,bnb.nn.Linearabit ):
return True
return False
def UpperCamelCase( __UpperCamelCase : nn.Module ):
return next(parameter.parameters() ).device
def UpperCamelCase( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : List[Any] ,__UpperCamelCase : Tuple ,__UpperCamelCase : int ,__UpperCamelCase : Optional[Any] ,__UpperCamelCase : Any ,__UpperCamelCase : Optional[int] ):
# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
if fpaa_statistics is None:
set_module_tensor_to_device(__UpperCamelCase ,__UpperCamelCase ,0 ,dtype=__UpperCamelCase ,value=__UpperCamelCase )
lowerCAmelCase_ : Tuple = param_name
lowerCAmelCase_ : Optional[int] = model
if "." in tensor_name:
lowerCAmelCase_ : Dict = tensor_name.split('''.''' )
for split in splits[:-1]:
lowerCAmelCase_ : str = getattr(__UpperCamelCase ,__UpperCamelCase )
if new_module is None:
raise ValueError(f"""{module} has no attribute {split}.""" )
lowerCAmelCase_ : List[Any] = new_module
lowerCAmelCase_ : Tuple = splits[-1]
# offload weights
lowerCAmelCase_ : Dict = False
offload_weight(module._parameters[tensor_name] ,__UpperCamelCase ,__UpperCamelCase ,index=__UpperCamelCase )
if hasattr(module._parameters[tensor_name] ,'''SCB''' ):
offload_weight(
module._parameters[tensor_name].SCB ,param_name.replace('''weight''' ,'''SCB''' ) ,__UpperCamelCase ,index=__UpperCamelCase ,)
else:
offload_weight(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,index=__UpperCamelCase )
offload_weight(__UpperCamelCase ,param_name.replace('''weight''' ,'''SCB''' ) ,__UpperCamelCase ,index=__UpperCamelCase )
set_module_tensor_to_device(__UpperCamelCase ,__UpperCamelCase ,'''meta''' ,dtype=__UpperCamelCase ,value=torch.empty(*param.size() ) )
| 171 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __snake_case ( UpperCamelCase_ ):
_a = '''Salesforce/blip-image-captioning-base'''
_a = (
'''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
'''image to caption, and returns a text that contains the description in English.'''
)
_a = '''image_captioner'''
_a = AutoModelForVisionaSeq
_a = ['''image''']
_a = ['''text''']
def __init__( self : Optional[Any] , *A_ : Dict , **A_ : List[str]):
requires_backends(self , ['''vision'''])
super().__init__(*A_ , **A_)
def UpperCAmelCase__ ( self : Any , A_ : "Image"):
return self.pre_processor(images=A_ , return_tensors='''pt''')
def UpperCAmelCase__ ( self : Dict , A_ : Any):
return self.model.generate(**A_)
def UpperCAmelCase__ ( self : List[str] , A_ : Any):
return self.pre_processor.batch_decode(A_ , skip_special_tokens=A_)[0].strip()
| 171 | 1 |
def A_ ( lowercase_ ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def A_ ( lowercase_ ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 0
while number > 0:
SCREAMING_SNAKE_CASE = number % 1_0
sum_of_digits += last_digit
SCREAMING_SNAKE_CASE = number // 1_0 # Removing the last_digit from the given number
return sum_of_digits
def A_ ( lowercase_ = 1_0_0 ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE = factorial(_lowerCamelCase )
SCREAMING_SNAKE_CASE = split_and_add(_lowerCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 706 |
import argparse
from collections import defaultdict
import yaml
__UpperCAmelCase = "docs/source/en/_toctree.yml"
def A_ ( lowercase_ ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = defaultdict(lowercase_ )
for doc in model_doc:
counts[doc["local"]] += 1
SCREAMING_SNAKE_CASE = [key for key, value in counts.items() if value > 1]
SCREAMING_SNAKE_CASE = []
for duplicate_key in duplicates:
SCREAMING_SNAKE_CASE = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key} )
if len(lowercase_ ) > 1:
raise ValueError(
f'''{duplicate_key} is present several times in the documentation table of content at '''
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1] )
# Sort
return sorted(lowercase_ , key=lambda lowercase_ : s["title"].lower() )
def A_ ( lowercase_=False ) ->List[Any]:
"""simple docstring"""
with open(lowercase_ , encoding='utf-8' ) as f:
SCREAMING_SNAKE_CASE = yaml.safe_load(f.read() )
# Get to the API doc
SCREAMING_SNAKE_CASE = 0
while content[api_idx]["title"] != "API":
api_idx += 1
SCREAMING_SNAKE_CASE = content[api_idx]['sections']
# Then to the model doc
SCREAMING_SNAKE_CASE = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
SCREAMING_SNAKE_CASE = api_doc[model_idx]['sections']
SCREAMING_SNAKE_CASE = [(idx, section) for idx, section in enumerate(lowercase_ ) if 'sections' in section]
SCREAMING_SNAKE_CASE = False
for idx, modality_doc in modalities_docs:
SCREAMING_SNAKE_CASE = modality_doc['sections']
SCREAMING_SNAKE_CASE = clean_model_doc_toc(lowercase_ )
if old_modality_doc != new_modality_doc:
SCREAMING_SNAKE_CASE = True
if overwrite:
SCREAMING_SNAKE_CASE = new_modality_doc
if diff:
if overwrite:
SCREAMING_SNAKE_CASE = model_doc
SCREAMING_SNAKE_CASE = api_doc
with open(lowercase_ , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(lowercase_ , allow_unicode=lowercase_ ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
__UpperCAmelCase = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 259 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a : Optional[Any] = {
'''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
'''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[str] = ['''BertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[str] = [
'''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BertForMaskedLM''',
'''BertForMultipleChoice''',
'''BertForNextSentencePrediction''',
'''BertForPreTraining''',
'''BertForQuestionAnswering''',
'''BertForSequenceClassification''',
'''BertForTokenClassification''',
'''BertLayer''',
'''BertLMHeadModel''',
'''BertModel''',
'''BertPreTrainedModel''',
'''load_tf_weights_in_bert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = [
'''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBertEmbeddings''',
'''TFBertForMaskedLM''',
'''TFBertForMultipleChoice''',
'''TFBertForNextSentencePrediction''',
'''TFBertForPreTraining''',
'''TFBertForQuestionAnswering''',
'''TFBertForSequenceClassification''',
'''TFBertForTokenClassification''',
'''TFBertLMHeadModel''',
'''TFBertMainLayer''',
'''TFBertModel''',
'''TFBertPreTrainedModel''',
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[Any] = ['''TFBertTokenizer''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = [
'''FlaxBertForCausalLM''',
'''FlaxBertForMaskedLM''',
'''FlaxBertForMultipleChoice''',
'''FlaxBertForNextSentencePrediction''',
'''FlaxBertForPreTraining''',
'''FlaxBertForQuestionAnswering''',
'''FlaxBertForSequenceClassification''',
'''FlaxBertForTokenClassification''',
'''FlaxBertModel''',
'''FlaxBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
_a : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 598 |
'''simple docstring'''
import sys
from collections import defaultdict
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] ):
"""simple docstring"""
_lowercase = []
def snake_case ( self : Optional[Any] , __A : List[str] ):
"""simple docstring"""
return self.node_position[vertex]
def snake_case ( self : Any , __A : Dict , __A : List[str] ):
"""simple docstring"""
_lowercase = pos
def snake_case ( self : Optional[int] , __A : Any , __A : List[Any] , __A : Union[str, Any] , __A : Any ):
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_lowercase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_lowercase = 2 * start + 1
else:
_lowercase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_lowercase , _lowercase = heap[smallest_child], positions[smallest_child]
_lowercase , _lowercase = (
heap[start],
positions[start],
)
_lowercase , _lowercase = temp, tempa
_lowercase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , __A )
self.top_to_bottom(__A , __A , __A , __A )
def snake_case ( self : Dict , __A : Tuple , __A : Union[str, Any] , __A : Union[str, Any] , __A : Union[str, Any] ):
"""simple docstring"""
_lowercase = position[index]
while index != 0:
_lowercase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
_lowercase = heap[parent]
_lowercase = position[parent]
self.set_position(position[parent] , __A )
else:
_lowercase = val
_lowercase = temp
self.set_position(__A , __A )
break
_lowercase = parent
else:
_lowercase = val
_lowercase = temp
self.set_position(__A , 0 )
def snake_case ( self : int , __A : List[str] , __A : List[str] ):
"""simple docstring"""
_lowercase = len(__A ) // 2 - 1
for i in range(__A , -1 , -1 ):
self.top_to_bottom(__A , __A , len(__A ) , __A )
def snake_case ( self : int , __A : Optional[int] , __A : str ):
"""simple docstring"""
_lowercase = positions[0]
_lowercase = sys.maxsize
self.top_to_bottom(__A , 0 , len(__A ) , __A )
return temp
def A__ ( A_ ) -> int:
_lowercase = Heap()
_lowercase = [0] * len(A_ )
_lowercase = [-1] * len(A_ ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_lowercase = [] # Heap of Distance of vertices from their neighboring vertex
_lowercase = []
for vertex in range(len(A_ ) ):
distance_tv.append(sys.maxsize )
positions.append(A_ )
heap.node_position.append(A_ )
_lowercase = []
_lowercase = 1
_lowercase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_lowercase = 0
_lowercase = distance
heap.heapify(A_ , A_ )
for _ in range(1 , len(A_ ) ):
_lowercase = heap.delete_minimum(A_ , A_ )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_lowercase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(A_ )]
):
_lowercase = distance
heap.bottom_to_top(
A_ , heap.get_position(A_ ) , A_ , A_ )
_lowercase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
__magic_name__ : List[str] = int(input('''Enter number of edges: ''').strip())
__magic_name__ : List[Any] = defaultdict(list)
for _ in range(edges_number):
__magic_name__ : Union[str, Any] = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 497 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:Tuple = [
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
SCREAMING_SNAKE_CASE__:int = [
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def _lowerCamelCase( a ):
__a = torch.load(a , map_location="cpu" )
return sd
def _lowerCamelCase( a , a , a=rename_keys_prefix ):
__a = OrderedDict()
__a = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
__a = key
for name_pair in rename_keys_prefix:
__a = new_key.replace(name_pair[0] , name_pair[1] )
__a = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
__a = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def _lowerCamelCase( a , a ):
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."
# Get Config
if "pre" in checkpoint_path:
__a = "pretraining"
if "vcr" in checkpoint_path:
__a = {"visual_embedding_dim": 5_1_2}
elif "vqa_advanced" in checkpoint_path:
__a = {"visual_embedding_dim": 2_0_4_8}
elif "vqa" in checkpoint_path:
__a = {"visual_embedding_dim": 2_0_4_8}
elif "nlvr" in checkpoint_path:
__a = {"visual_embedding_dim": 1_0_2_4}
else:
raise NotImplementedError(F"No implementation found for `{checkpoint_path}`." )
else:
if "vcr" in checkpoint_path:
__a = {"visual_embedding_dim": 5_1_2}
__a = "multichoice"
elif "vqa_advanced" in checkpoint_path:
__a = {"visual_embedding_dim": 2_0_4_8}
__a = "vqa_advanced"
elif "vqa" in checkpoint_path:
__a = {"visual_embedding_dim": 2_0_4_8, "num_labels": 3_1_2_9}
__a = "vqa"
elif "nlvr" in checkpoint_path:
__a = {
"visual_embedding_dim": 1_0_2_4,
"num_labels": 2,
}
__a = "nlvr"
__a = VisualBertConfig(**a )
# Load State Dict
__a = load_state_dict(a )
__a = get_new_dict(a , a )
if model_type == "pretraining":
__a = VisualBertForPreTraining(a )
elif model_type == "vqa":
__a = VisualBertForQuestionAnswering(a )
elif model_type == "nlvr":
__a = VisualBertForVisualReasoning(a )
elif model_type == "multichoice":
__a = VisualBertForMultipleChoice(a )
model.load_state_dict(a )
# Save Checkpoints
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__:Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
SCREAMING_SNAKE_CASE__:Optional[int] = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 704 | """simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _lowerCamelCase( a , a , a ):
__a = OmegaConf.load(a )
__a = torch.load(a , map_location="cpu" )["model"]
__a = list(state_dict.keys() )
# extract state_dict for VQVAE
__a = {}
__a = "first_stage_model."
for key in keys:
if key.startswith(a ):
__a = state_dict[key]
# extract state_dict for UNetLDM
__a = {}
__a = "model.diffusion_model."
for key in keys:
if key.startswith(a ):
__a = state_dict[key]
__a = config.model.params.first_stage_config.params
__a = config.model.params.unet_config.params
__a = VQModel(**a ).eval()
vqvae.load_state_dict(a )
__a = UNetLDMModel(**a ).eval()
unet.load_state_dict(a )
__a = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule="scaled_linear" , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=a , )
__a = LDMPipeline(a , a , a )
pipeline.save_pretrained(a )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__:List[Any] = argparse.ArgumentParser()
parser.add_argument("""--checkpoint_path""", type=str, required=True)
parser.add_argument("""--config_path""", type=str, required=True)
parser.add_argument("""--output_path""", type=str, required=True)
SCREAMING_SNAKE_CASE__:Union[str, Any] = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 67 | 0 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowerCAmelCase ( unittest.TestCase , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[str] = load_tool("""text-classification""" )
self.tool.setup()
snake_case_ : Union[str, Any] = load_tool("""text-classification""" , remote=_lowercase )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = self.tool("""That's quite cool""" , ["""positive""", """negative"""] )
self.assertEqual(_lowercase , """positive""" )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.remote_tool("""That's quite cool""" , ["""positive""", """negative"""] )
self.assertEqual(_lowercase , """positive""" )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.tool(text="""That's quite cool""" , labels=["""positive""", """negative"""] )
self.assertEqual(_lowercase , """positive""" )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = self.remote_tool(text="""That's quite cool""" , labels=["""positive""", """negative"""] )
self.assertEqual(_lowercase , """positive""" )
| 58 |
'''simple docstring'''
def UpperCamelCase_ ( A__ , A__ , A__ ):
def count_of_possible_combinations(A__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(A__ )
def UpperCamelCase_ ( A__ , A__ , A__ ):
def count_of_possible_combinations_with_dp_array(
A__ , A__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
a_ = sum(
count_of_possible_combinations_with_dp_array(target - item , A__ )
for item in array )
a_ = answer
return answer
a_ = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(A__ , A__ )
def UpperCamelCase_ ( A__ , A__ , A__ ):
a_ = [0] * (target + 1)
a_ = 1
for i in range(1 , target + 1 ):
for j in range(A__ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ =3
lowercase__ =5
lowercase__ =[1, 2, 5]
print(combination_sum_iv(n, array, target))
| 263 | 0 |
from __future__ import annotations
snake_case_ : str = 1.60_21e-19 # units = C
def lowerCamelCase( a__ ,a__ ,a__ ,):
if (conductivity, electron_conc, mobility).count(0) != 1:
raise ValueError('''You cannot supply more or less than 2 values''')
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''')
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''')
elif mobility < 0:
raise ValueError('''mobility cannot be negative''')
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 191 |
from collections.abc import Generator
def lowerCamelCase( ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =0, 1
while True:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =b, a + b
yield b
def lowerCamelCase( a__ = 1000):
_SCREAMING_SNAKE_CASE =1
_SCREAMING_SNAKE_CASE =fibonacci_generator()
while len(str(next(a__))) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip()))) | 191 | 1 |
import os
import sys
import unittest
__a: Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
__a: Dict = os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
__a: Union[str, Any] = os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : str ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = get_test_to_tester_mapping(lowerCamelCase )
_UpperCAmelCase = get_test_to_tester_mapping(lowerCamelCase )
_UpperCAmelCase = {"""BertModelTest""": """BertModelTester"""}
_UpperCAmelCase = {
"""BlipModelTest""": """BlipModelTester""",
"""BlipTextImageModelTest""": """BlipTextImageModelsModelTester""",
"""BlipTextModelTest""": """BlipTextModelTester""",
"""BlipTextRetrievalModelTest""": """BlipTextRetrievalModelTester""",
"""BlipVQAModelTest""": """BlipVQAModelTester""",
"""BlipVisionModelTest""": """BlipVisionModelTester""",
}
self.assertEqual(get_test_info.to_json(lowerCamelCase ) , lowerCamelCase )
self.assertEqual(get_test_info.to_json(lowerCamelCase ) , lowerCamelCase )
def lowerCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase = get_model_to_test_mapping(lowerCamelCase )
_UpperCAmelCase = get_model_to_test_mapping(lowerCamelCase )
_UpperCAmelCase = {
"""BertForMaskedLM""": ["""BertModelTest"""],
"""BertForMultipleChoice""": ["""BertModelTest"""],
"""BertForNextSentencePrediction""": ["""BertModelTest"""],
"""BertForPreTraining""": ["""BertModelTest"""],
"""BertForQuestionAnswering""": ["""BertModelTest"""],
"""BertForSequenceClassification""": ["""BertModelTest"""],
"""BertForTokenClassification""": ["""BertModelTest"""],
"""BertLMHeadModel""": ["""BertModelTest"""],
"""BertModel""": ["""BertModelTest"""],
}
_UpperCAmelCase = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelTest"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTest"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTest"""],
"""BlipModel""": ["""BlipModelTest"""],
"""BlipTextModel""": ["""BlipTextModelTest"""],
"""BlipVisionModel""": ["""BlipVisionModelTest"""],
}
self.assertEqual(get_test_info.to_json(lowerCamelCase ) , lowerCamelCase )
self.assertEqual(get_test_info.to_json(lowerCamelCase ) , lowerCamelCase )
def lowerCamelCase ( self : int ) -> Any:
"""simple docstring"""
_UpperCAmelCase = get_model_to_tester_mapping(lowerCamelCase )
_UpperCAmelCase = get_model_to_tester_mapping(lowerCamelCase )
_UpperCAmelCase = {
"""BertForMaskedLM""": ["""BertModelTester"""],
"""BertForMultipleChoice""": ["""BertModelTester"""],
"""BertForNextSentencePrediction""": ["""BertModelTester"""],
"""BertForPreTraining""": ["""BertModelTester"""],
"""BertForQuestionAnswering""": ["""BertModelTester"""],
"""BertForSequenceClassification""": ["""BertModelTester"""],
"""BertForTokenClassification""": ["""BertModelTester"""],
"""BertLMHeadModel""": ["""BertModelTester"""],
"""BertModel""": ["""BertModelTester"""],
}
_UpperCAmelCase = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelsModelTester"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTester"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTester"""],
"""BlipModel""": ["""BlipModelTester"""],
"""BlipTextModel""": ["""BlipTextModelTester"""],
"""BlipVisionModel""": ["""BlipVisionModelTester"""],
}
self.assertEqual(get_test_info.to_json(lowerCamelCase ) , lowerCamelCase )
self.assertEqual(get_test_info.to_json(lowerCamelCase ) , lowerCamelCase ) | 108 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def a_ ( __magic_name__ ) -> int:
"""simple docstring"""
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , set() )
@pytest.fixture
def a_ ( __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
class a_ :
def __init__( self : List[Any] , UpperCAmelCase__ : Optional[int] ):
"""simple docstring"""
snake_case : Any = metric_id
class a_ :
A__ : Dict = [MetricMock(a ) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]
def lowerCAmelCase( self : Any ):
"""simple docstring"""
return self._metrics
monkeypatch.setattr('''datasets.inspect.huggingface_hub''' , HfhMock() )
@pytest.mark.parametrize(
'''func, args''' , [(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))] )
def a_ ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> Any:
"""simple docstring"""
if "tmp_path" in args:
snake_case : str = tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args )
with pytest.warns(__magic_name__ , match='''https://huggingface.co/docs/evaluate''' ):
func(*__magic_name__ )
| 598 | 0 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowercase_ = DebertaVaTokenizer
lowercase_ = DebertaVaTokenizerFast
lowercase_ = True
lowercase_ = True
def SCREAMING_SNAKE_CASE_ (self : str) ->Optional[int]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase__: str =DebertaVaTokenizer(UpperCAmelCase_ , unk_token="<unk>")
tokenizer.save_pretrained(self.tmpdirname)
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : List[str]) ->Any:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] ="this is a test"
lowerCamelCase__: Union[str, Any] ="this is a test"
return input_text, output_text
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: List[Any] ="<pad>"
lowerCamelCase__: str =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_) , UpperCAmelCase_)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_) , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : str) ->Tuple:
'''simple docstring'''
lowerCamelCase__: List[str] =list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , "<pad>")
self.assertEqual(vocab_keys[1] , "<unk>")
self.assertEqual(vocab_keys[-1] , "[PAD]")
self.assertEqual(len(UpperCAmelCase_) , 30_001)
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 30_000)
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->int:
'''simple docstring'''
lowerCamelCase__: str =" \tHeLLo!how \n Are yoU? "
lowerCamelCase__: str =["▁hello", "!", "how", "▁are", "▁you", "?"]
# fmt: on
lowerCamelCase__: Optional[int] =DebertaVaTokenizer(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_)
lowerCamelCase__: Optional[int] =tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Dict =DebertaVaTokenizerFast(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_)
lowerCamelCase__: Dict =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.")
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->List[str]:
'''simple docstring'''
pass
@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.")
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Optional[int]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: List[Any] ="I was born in 92000, and this is falsé."
lowerCamelCase__: Union[str, Any] =["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
lowerCamelCase__: List[str] =DebertaVaTokenizer(UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: int =tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Any =DebertaVaTokenizerFast(UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: int =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : str) ->List[str]:
'''simple docstring'''
lowerCamelCase__: Optional[Any] ="I was born in 92000, and this is falsé."
lowerCamelCase__: Optional[int] =["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
lowerCamelCase__: Any =DebertaVaTokenizer(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: Any =tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: List[Any] =DebertaVaTokenizerFast(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : int) ->List[str]:
'''simple docstring'''
lowerCamelCase__: Dict ="I was born in 92000, and this is falsé."
lowerCamelCase__: Union[str, Any] =["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
lowerCamelCase__: int =DebertaVaTokenizer(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =DebertaVaTokenizerFast(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: Optional[int] =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : int) ->Any:
'''simple docstring'''
lowerCamelCase__: Optional[Any] ="I was born in 92000, and this is falsé."
lowerCamelCase__: Dict =["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
# fmt: on
lowerCamelCase__: Dict =DebertaVaTokenizer(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: Optional[int] =tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: int =DebertaVaTokenizerFast(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: Optional[int] =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->str:
'''simple docstring'''
lowerCamelCase__: List[str] =" \tHeLLo!how \n Are yoU? "
lowerCamelCase__: Union[str, Any] =["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
# fmt: on
lowerCamelCase__: Optional[int] =DebertaVaTokenizer(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: str =DebertaVaTokenizerFast(UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , split_by_punct=UpperCAmelCase_)
lowerCamelCase__: Dict =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: Dict =self.get_tokenizer()
lowerCamelCase__: Optional[int] =self.get_rust_tokenizer()
lowerCamelCase__: Union[str, Any] ="I was born in 92000, and this is falsé."
lowerCamelCase__: Optional[Any] =tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
lowerCamelCase__: Union[str, Any] =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_))
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: List[str] =tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
lowerCamelCase__: int =rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Dict =self.get_rust_tokenizer()
lowerCamelCase__: Optional[int] =tokenizer.encode(UpperCAmelCase_)
lowerCamelCase__: Optional[int] =rust_tokenizer.encode(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : str) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Optional[Any] ="This is a test"
lowerCamelCase__: Optional[Any] =[13, 1, 4_398, 25, 21, 1_289]
lowerCamelCase__: Tuple =["▁", "T", "his", "▁is", "▁a", "▁test"]
lowerCamelCase__: str =["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
lowerCamelCase__: List[Any] =DebertaVaTokenizer(UpperCAmelCase_ , keep_accents=UpperCAmelCase_)
lowerCamelCase__: Optional[int] =DebertaVaTokenizerFast(UpperCAmelCase_ , keep_accents=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Dict =tokenizer.tokenize(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: List[Any] =tokenizer.convert_ids_to_tokens(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: List[str] =rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Dict =rust_tokenizer.tokenize(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: List[str] =rust_tokenizer.convert_ids_to_tokens(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
# fmt: off
lowerCamelCase__: Union[str, Any] ="I was born in 92000, and this is falsé."
lowerCamelCase__: List[str] =[13, 1, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]
lowerCamelCase__: List[Any] =["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
lowerCamelCase__: List[str] =["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
# fmt: on
lowerCamelCase__: Tuple =tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Tuple =tokenizer.tokenize(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Tuple =tokenizer.convert_ids_to_tokens(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Tuple =rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Any =rust_tokenizer.tokenize(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: str =rust_tokenizer.convert_ids_to_tokens(UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: int =DebertaVaTokenizer(UpperCAmelCase_)
lowerCamelCase__: Optional[int] =tokenizer.encode("sequence builders")
lowerCamelCase__: Dict =tokenizer.encode("multi-sequence build")
lowerCamelCase__: Union[str, Any] =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ , UpperCAmelCase_)
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , UpperCAmelCase_)
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , UpperCAmelCase_ , )
@slow
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->Any:
'''simple docstring'''
lowerCamelCase__: Dict ={"input_ids": [[1, 39_867, 36, 19_390, 486, 27, 35_052, 81_436, 18, 60_685, 1_225, 7, 35_052, 81_436, 18, 9_367, 16_899, 18, 15_937, 53, 594, 773, 18, 16_287, 30_465, 36, 15_937, 6, 41_139, 38, 36_979, 60_763, 191, 6, 34_132, 99, 6, 50_538, 390, 43_230, 6, 34_132, 2_779, 20_850, 14, 699, 1_072, 1_194, 36, 382, 10_901, 53, 7, 699, 1_072, 2_084, 36, 20_422, 630, 53, 19, 105, 3_049, 1_896, 1_053, 16_899, 1_506, 11, 37_978, 4_243, 7, 1_237, 31_869, 200, 16_566, 654, 6, 35_052, 81_436, 7, 55_630, 13_593, 4, 2], [1, 26, 15_011, 13, 667, 8, 1_053, 18, 23_611, 1_237, 72_356, 12_820, 34, 104_134, 1_209, 35, 13_313, 6_627, 21, 202, 347, 7, 164, 2_399, 11, 46, 4_485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_232, 2_864, 15_785, 14_951, 105, 5, 8_581, 1_250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ , model_name="microsoft/deberta-v2-xlarge" , revision="ad6e42c1532ddf3a15c39246b63f5559d558b670" , )
| 437 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "xlm-roberta-xl"
def __init__(self : Tuple , UpperCAmelCase_ : Union[str, Any]=250_880 , UpperCAmelCase_ : Optional[int]=2_560 , UpperCAmelCase_ : Union[str, Any]=36 , UpperCAmelCase_ : str=32 , UpperCAmelCase_ : Dict=10_240 , UpperCAmelCase_ : Optional[int]="gelu" , UpperCAmelCase_ : Tuple=0.1 , UpperCAmelCase_ : List[Any]=0.1 , UpperCAmelCase_ : List[Any]=514 , UpperCAmelCase_ : List[Any]=1 , UpperCAmelCase_ : Dict=0.02 , UpperCAmelCase_ : Dict=1E-0_5 , UpperCAmelCase_ : int=1 , UpperCAmelCase_ : Dict=0 , UpperCAmelCase_ : Optional[Any]=2 , UpperCAmelCase_ : Union[str, Any]="absolute" , UpperCAmelCase_ : str=True , UpperCAmelCase_ : str=None , **UpperCAmelCase_ : List[str] , ) ->int:
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: List[str] =vocab_size
lowerCamelCase__: List[Any] =hidden_size
lowerCamelCase__: Any =num_hidden_layers
lowerCamelCase__: Optional[Any] =num_attention_heads
lowerCamelCase__: Dict =hidden_act
lowerCamelCase__: str =intermediate_size
lowerCamelCase__: List[Any] =hidden_dropout_prob
lowerCamelCase__: List[str] =attention_probs_dropout_prob
lowerCamelCase__: Union[str, Any] =max_position_embeddings
lowerCamelCase__: Optional[int] =type_vocab_size
lowerCamelCase__: Tuple =initializer_range
lowerCamelCase__: Tuple =layer_norm_eps
lowerCamelCase__: Dict =position_embedding_type
lowerCamelCase__: Optional[int] =use_cache
lowerCamelCase__: List[str] =classifier_dropout
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
lowerCamelCase__: Union[str, Any] ={0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCamelCase__: Optional[int] ={0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
])
| 437 | 1 |
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