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"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
_UpperCAmelCase = parser.add_subparsers(help='''diffusers-cli command helpers''' )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
_UpperCAmelCase = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , '''func''' ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE ):
if char == separator:
split_words.append(string[last_index:index] )
_UpperCAmelCase = index + 1
elif index + 1 == len(_SCREAMING_SNAKE_CASE ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 | 1 |
'''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
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = '▁'
UpperCAmelCase_ = {'vocab_file': 'sentencepiece.bpe.model'}
UpperCAmelCase_ = {
'vocab_file': {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
}
}
UpperCAmelCase_ = {
'facebook/xglm-564M': 2_0_4_8,
}
class lowerCAmelCase_ ( __UpperCamelCase ):
'''simple docstring'''
lowerCAmelCase_ : Tuple = VOCAB_FILES_NAMES
lowerCAmelCase_ : Dict = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ : str = ["input_ids", "attention_mask"]
def __init__( self : str , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int]="<s>" , _UpperCAmelCase : int="</s>" , _UpperCAmelCase : Any="</s>" , _UpperCAmelCase : str="<s>" , _UpperCAmelCase : List[Any]="<unk>" , _UpperCAmelCase : Union[str, Any]="<pad>" , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : Dict , ):
"""simple docstring"""
UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
UpperCAmelCase__ = 7
UpperCAmelCase__ = [f'''<madeupword{i}>''' for i in range(self.num_madeup_words )]
UpperCAmelCase__ = 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 , )
UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowerCAmelCase ) )
UpperCAmelCase__ = 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
UpperCAmelCase__ = 1
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCAmelCase__ = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
UpperCAmelCase__ = len(self.sp_model )
UpperCAmelCase__ = {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(_lowerCAmelCase )
UpperCAmelCase__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.__dict__.copy()
UpperCAmelCase__ = None
UpperCAmelCase__ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Optional[Any] , _UpperCAmelCase : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCAmelCase__ = {}
UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
UpperCAmelCase__ = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ):
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
"""simple docstring"""
UpperCAmelCase__ = [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 SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : str ):
"""simple docstring"""
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Tuple ):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCAmelCase__ = 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 SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Optional[int] ):
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = """""".join(_lowerCAmelCase ).replace(_lowerCAmelCase , """ """ ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase__ = 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:
UpperCAmelCase__ = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 370 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = tempfile.mkdtemp()
UpperCAmelCase__ = BlipImageProcessor()
UpperCAmelCase__ = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
UpperCAmelCase__ = BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
UpperCAmelCase__ = InstructBlipProcessor(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , **_UpperCAmelCase : Tuple ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).tokenizer
def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : List[str] ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).image_processor
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , **_UpperCAmelCase : Dict ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).qformer_tokenizer
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
UpperCAmelCase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCAmelCase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
UpperCAmelCase__ = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
UpperCAmelCase__ = processor(images=_UpperCAmelCase , 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 : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = """lower newer"""
UpperCAmelCase__ = processor(text=_UpperCAmelCase )
UpperCAmelCase__ = tokenizer(_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase )
UpperCAmelCase__ = qformer_tokenizer(_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["""qformer_""" + key] )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = """lower newer"""
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
# test if it raises when no input is passed
with pytest.raises(_UpperCAmelCase ):
processor()
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase__ = processor.batch_decode(_UpperCAmelCase )
UpperCAmelCase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_qformer_tokenizer()
UpperCAmelCase__ = InstructBlipProcessor(
tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase , qformer_tokenizer=_UpperCAmelCase )
UpperCAmelCase__ = """lower newer"""
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
| 61 | 0 |
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
UpperCamelCase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class A ( datasets.BuilderConfig ):
__UpperCAmelCase : int = 1_00_00
__UpperCAmelCase : Optional[List[str]] = None
__UpperCAmelCase : Optional[datasets.Features] = None
class A ( datasets.ArrowBasedBuilder ):
__UpperCAmelCase : List[Any] = ParquetConfig
def lowercase_ (self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowercase_ (self : Dict , __UpperCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
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}""" )
UpperCAmelCase__ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__UpperCAmelCase , (str, list, tuple) ):
UpperCAmelCase__ = data_files
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCAmelCase__ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase__ = [dl_manager.iter_files(__UpperCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
UpperCAmelCase__ = []
for split_name, files in data_files.items():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCAmelCase__ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase__ = [dl_manager.iter_files(__UpperCAmelCase ) 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(__UpperCAmelCase ):
with open(__UpperCAmelCase , "rb" ) as f:
UpperCAmelCase__ = datasets.Features.from_arrow_schema(pq.read_schema(__UpperCAmelCase ) )
break
splits.append(datasets.SplitGenerator(name=__UpperCAmelCase , gen_kwargs={"files": files} ) )
return splits
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : pa.Table ) -> pa.Table:
"""simple docstring"""
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
UpperCAmelCase__ = table_cast(__UpperCAmelCase , self.info.features.arrow_schema )
return pa_table
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = 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(__UpperCAmelCase ) ):
with open(__UpperCAmelCase , "rb" ) as f:
UpperCAmelCase__ = pq.ParquetFile(__UpperCAmelCase )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
UpperCAmelCase__ = 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(__UpperCAmelCase )
except ValueError as e:
logger.error(f"""Failed to read file '{file}' with error {type(__UpperCAmelCase )}: {e}""" )
raise
| 65 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCamelCase__ ( _A , _A , _A ):
if isinstance(_A , torch.Tensor ):
return image
elif isinstance(_A , PIL.Image.Image ):
a : Any = [image]
if isinstance(image[0] , PIL.Image.Image ):
a : List[str] = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
a : int = np.concatenate(_A , axis=0 )
a : int = np.array(_A ).astype(np.floataa ) / 255.0
a : str = image.transpose(0 , 3 , 1 , 2 )
a : str = 2.0 * image - 1.0
a : Optional[int] = torch.from_numpy(_A )
elif isinstance(image[0] , torch.Tensor ):
a : Optional[Any] = torch.cat(_A , dim=0 )
return image
def lowerCamelCase__ ( _A , _A , _A , _A=0.9995 ):
if not isinstance(_A , np.ndarray ):
a : Dict = True
a : Optional[Any] = va.device
a : Optional[int] = va.cpu().numpy()
a : Union[str, Any] = va.cpu().numpy()
a : Any = np.sum(va * va / (np.linalg.norm(_A ) * np.linalg.norm(_A )) )
if np.abs(_A ) > DOT_THRESHOLD:
a : Any = (1 - t) * va + t * va
else:
a : Any = np.arccos(_A )
a : Tuple = np.sin(_A )
a : Optional[Any] = theta_a * t
a : List[Any] = np.sin(_A )
a : Dict = np.sin(theta_a - theta_t ) / sin_theta_a
a : int = sin_theta_t / sin_theta_a
a : Any = sa * va + sa * va
if inputs_are_torch:
a : Dict = torch.from_numpy(_A ).to(_A )
return va
def lowerCamelCase__ ( _A , _A ):
a : Optional[int] = F.normalize(_A , dim=-1 )
a : str = F.normalize(_A , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def lowerCamelCase__ ( _A , _A ):
for param in model.parameters():
a : int = value
class a__( lowerCamelCase__ ):
def __init__( self : str , __snake_case : AutoencoderKL , __snake_case : CLIPTextModel , __snake_case : CLIPModel , __snake_case : CLIPTokenizer , __snake_case : UNetaDConditionModel , __snake_case : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , __snake_case : CLIPFeatureExtractor , __snake_case : List[str]=None , __snake_case : List[str]=None , __snake_case : List[Any]=None , ):
super().__init__()
self.register_modules(
vae=__snake_case , text_encoder=__snake_case , clip_model=__snake_case , tokenizer=__snake_case , unet=__snake_case , scheduler=__snake_case , feature_extractor=__snake_case , coca_model=__snake_case , coca_tokenizer=__snake_case , coca_transform=__snake_case , )
a : Optional[Any] = (
feature_extractor.size
if isinstance(feature_extractor.size , __snake_case )
else feature_extractor.size['shortest_edge']
)
a : Optional[int] = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , __snake_case )
set_requires_grad(self.clip_model , __snake_case )
def lowercase_ ( self : int , __snake_case : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
a : Union[str, Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__snake_case )
def lowercase_ ( self : Union[str, Any] ):
self.enable_attention_slicing(__snake_case )
def lowercase_ ( self : Optional[Any] ):
set_requires_grad(self.vae , __snake_case )
def lowercase_ ( self : Tuple ):
set_requires_grad(self.vae , __snake_case )
def lowercase_ ( self : int ):
set_requires_grad(self.unet , __snake_case )
def lowercase_ ( self : Union[str, Any] ):
set_requires_grad(self.unet , __snake_case )
def lowercase_ ( self : int , __snake_case : Dict , __snake_case : str , __snake_case : Optional[int] ):
# get the original timestep using init_timestep
a : Optional[Any] = min(int(num_inference_steps * strength ) , __snake_case )
a : Union[str, Any] = max(num_inference_steps - init_timestep , 0 )
a : List[Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowercase_ ( self : Dict , __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : Any , __snake_case : Optional[Any]=None ):
if not isinstance(__snake_case , torch.Tensor ):
raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(__snake_case )}""" )
a : Optional[Any] = image.to(device=__snake_case , dtype=__snake_case )
if isinstance(__snake_case , __snake_case ):
a : Optional[int] = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__snake_case )
]
a : Optional[Any] = torch.cat(__snake_case , dim=0 )
else:
a : Union[str, Any] = self.vae.encode(__snake_case ).latent_dist.sample(__snake_case )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
a : List[str] = 0.18215 * init_latents
a : str = init_latents.repeat_interleave(__snake_case , dim=0 )
a : Dict = randn_tensor(init_latents.shape , generator=__snake_case , device=__snake_case , dtype=__snake_case )
# get latents
a : Dict = self.scheduler.add_noise(__snake_case , __snake_case , __snake_case )
a : int = init_latents
return latents
def lowercase_ ( self : List[str] , __snake_case : Dict ):
a : List[Any] = self.coca_transform(__snake_case ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
a : Optional[Any] = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
a : Union[str, Any] = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('<end_of_text>' )[0].replace('<start_of_text>' , '' ).rstrip(' .,' )
def lowercase_ ( self : Tuple , __snake_case : Any , __snake_case : Optional[Any] ):
a : List[Any] = self.feature_extractor.preprocess(__snake_case )
a : Optional[Any] = torch.from_numpy(clip_image_input['pixel_values'][0] ).unsqueeze(0 ).to(self.device ).half()
a : int = self.clip_model.get_image_features(__snake_case )
a : str = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__snake_case )
a : Tuple = image_embeddings_clip.repeat_interleave(__snake_case , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def lowercase_ ( self : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : List[Any] , ):
a : Optional[Any] = latents.detach().requires_grad_()
a : List[Any] = self.scheduler.scale_model_input(__snake_case , __snake_case )
# predict the noise residual
a : Any = self.unet(__snake_case , __snake_case , encoder_hidden_states=__snake_case ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
a : int = self.scheduler.alphas_cumprod[timestep]
a : Any = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
a : List[str] = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
a : Tuple = torch.sqrt(__snake_case )
a : str = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , __snake_case ):
a : List[Any] = self.scheduler.sigmas[index]
a : Optional[int] = latents - sigma * noise_pred
else:
raise ValueError(F"""scheduler type {type(self.scheduler )} not supported""" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
a : Union[str, Any] = 1 / 0.18215 * sample
a : str = self.vae.decode(__snake_case ).sample
a : List[Any] = (image / 2 + 0.5).clamp(0 , 1 )
a : Tuple = transforms.Resize(self.feature_extractor_size )(__snake_case )
a : List[str] = self.normalize(__snake_case ).to(latents.dtype )
a : List[str] = self.clip_model.get_image_features(__snake_case )
a : Tuple = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__snake_case )
a : int = spherical_dist_loss(__snake_case , __snake_case ).mean() * clip_guidance_scale
a : List[str] = -torch.autograd.grad(__snake_case , __snake_case )[0]
if isinstance(self.scheduler , __snake_case ):
a : List[Any] = latents.detach() + grads * (sigma**2)
a : Optional[int] = noise_pred_original
else:
a : List[Any] = noise_pred_original - torch.sqrt(__snake_case ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Optional[int] , __snake_case : Union[torch.FloatTensor, PIL.Image.Image] , __snake_case : Union[torch.FloatTensor, PIL.Image.Image] , __snake_case : Optional[str] = None , __snake_case : Optional[str] = None , __snake_case : Optional[int] = 5_12 , __snake_case : Optional[int] = 5_12 , __snake_case : float = 0.6 , __snake_case : Optional[int] = 50 , __snake_case : Optional[float] = 7.5 , __snake_case : Optional[int] = 1 , __snake_case : float = 0.0 , __snake_case : Optional[float] = 1_00 , __snake_case : Optional[torch.Generator] = None , __snake_case : Optional[str] = "pil" , __snake_case : bool = True , __snake_case : float = 0.8 , __snake_case : float = 0.1 , __snake_case : float = 0.1 , ):
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) != batch_size:
raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(__snake_case )} generators.""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if isinstance(__snake_case , torch.Generator ) and batch_size > 1:
a : Dict = [generator] + [None] * (batch_size - 1)
a : Any = [
('model', self.coca_model is None),
('tokenizer', self.coca_tokenizer is None),
('transform', self.coca_transform is None),
]
a : List[str] = [x[0] for x in coca_is_none if x[1]]
a : List[str] = ', '.join(__snake_case )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(__snake_case ):
raise ValueError(
F"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
F"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
a : int = self.get_image_description(__snake_case )
if style_prompt is None:
if len(__snake_case ):
raise ValueError(
F"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
F""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
a : Union[str, Any] = self.get_image_description(__snake_case )
# get prompt text embeddings for content and style
a : Optional[Any] = self.tokenizer(
__snake_case , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=__snake_case , return_tensors='pt' , )
a : Dict = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
a : Dict = self.tokenizer(
__snake_case , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=__snake_case , return_tensors='pt' , )
a : Dict = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
a : Any = slerp(__snake_case , __snake_case , __snake_case )
# duplicate text embeddings for each generation per prompt
a : Optional[Any] = text_embeddings.repeat_interleave(__snake_case , dim=0 )
# set timesteps
a : int = 'offset' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
a : Any = {}
if accepts_offset:
a : Optional[Any] = 1
self.scheduler.set_timesteps(__snake_case , **__snake_case )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
a , a : Tuple = self.get_timesteps(__snake_case , __snake_case , self.device )
a : Optional[int] = timesteps[:1].repeat(__snake_case )
# Preprocess image
a : Optional[Any] = preprocess(__snake_case , __snake_case , __snake_case )
a : List[Any] = self.prepare_latents(
__snake_case , __snake_case , __snake_case , text_embeddings.dtype , self.device , __snake_case )
a : str = preprocess(__snake_case , __snake_case , __snake_case )
a : Union[str, Any] = self.prepare_latents(
__snake_case , __snake_case , __snake_case , text_embeddings.dtype , self.device , __snake_case )
a : Union[str, Any] = slerp(__snake_case , __snake_case , __snake_case )
if clip_guidance_scale > 0:
a : Dict = self.get_clip_image_embeddings(__snake_case , __snake_case )
a : int = self.get_clip_image_embeddings(__snake_case , __snake_case )
a : List[str] = slerp(
__snake_case , __snake_case , __snake_case )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
a : int = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
a : Any = content_text_input.input_ids.shape[-1]
a : List[Any] = self.tokenizer([''] , padding='max_length' , max_length=__snake_case , return_tensors='pt' )
a : List[str] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
a : Dict = uncond_embeddings.repeat_interleave(__snake_case , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
a : Any = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
a : List[str] = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
a : List[str] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
a : int = torch.randn(__snake_case , generator=__snake_case , device='cpu' , dtype=__snake_case ).to(
self.device )
else:
a : Optional[int] = torch.randn(__snake_case , generator=__snake_case , device=self.device , dtype=__snake_case )
else:
if latents.shape != latents_shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
a : List[str] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
a : Any = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
a : Optional[Any] = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
a : Union[str, Any] = {}
if accepts_eta:
a : List[str] = eta
# check if the scheduler accepts generator
a : List[Any] = 'generator' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
a : Any = generator
with self.progress_bar(total=__snake_case ):
for i, t in enumerate(__snake_case ):
# expand the latents if we are doing classifier free guidance
a : Tuple = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
a : Dict = self.scheduler.scale_model_input(__snake_case , __snake_case )
# predict the noise residual
a : List[Any] = self.unet(__snake_case , __snake_case , encoder_hidden_states=__snake_case ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
a , a : List[str] = noise_pred.chunk(2 )
a : Union[str, Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
a : Optional[Any] = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
a , a : Union[str, Any] = self.cond_fn(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# compute the previous noisy sample x_t -> x_t-1
a : Any = self.scheduler.step(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
a : Tuple = 1 / 0.18215 * latents
a : Optional[int] = self.vae.decode(__snake_case ).sample
a : List[str] = (image / 2 + 0.5).clamp(0 , 1 )
a : Any = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a : str = self.numpy_to_pil(__snake_case )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=__snake_case , nsfw_content_detected=__snake_case ) | 297 | 0 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : Any = sorted(zip(lowercase__ , lowercase__ ) , key=lambda lowercase__ : x[0] / x[1] , reverse=lowercase__ )
_lowerCamelCase : Any = [i[0] for i in r], [i[1] for i in r]
_lowerCamelCase : List[str] = list(accumulate(lowercase__ ) )
_lowerCamelCase : List[Any] = bisect(lowercase__ , lowercase__ )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowercase__ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("""""", """|""", """|"""),
datarow=DataRow("""""", """|""", """|"""),
padding=1,
with_header_hide=None,
)
lowercase__ = []
lowercase__ = []
lowercase__ = {"""type""": """section""", """text""": {"""type""": """plain_text""", """text""": """No failed tests! 🤗""", """emoji""": True}}
lowercase__ = [
{
"""type""": """header""",
"""text""": {
"""type""": """plain_text""",
"""text""": F"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
"""emoji""": True,
},
}
]
lowercase__ = 0
for log in Path().glob("""*.log"""):
lowercase__ = 0
with open(log, """r""") as f:
for line in f:
lowercase__ = json.loads(line)
if line.get("""nodeid""", """""") != "":
lowercase__ = line["""nodeid"""]
if line.get("""duration""", None) is not None:
lowercase__ = F"{line['duration']:.4f}"
if line.get("""outcome""", """""") == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split("""_""")[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
lowercase__ = []
log.unlink()
lowercase__ = """"""
lowercase__ = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
lowercase__ = []
lowercase__ = {}
for test in failed_tests:
lowercase__ = test[0].split("""::""")
lowercase__ = data[0].split("""/""")[-1]
if data[0] not in filesafailed:
lowercase__ = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
lowercase__ = [test[0] for test in failed_table]
lowercase__ = list(set(files))
# Count number of instances in failed_tests
lowercase__ = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
lowercase__ = tabulate(
table,
headers=["""Test Location""", """Num Failed"""],
tablefmt=hf_table_format,
stralign="""right""",
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3000:
lowercase__ = """Too many failed tests, please see the full report in the Action results."""
lowercase__ = len(err) + 10
lowercase__ = message[: 3000 - offset] + F"\n...\n```\n{err}"
print(F"### {message}")
else:
lowercase__ = """No failed tests! 🤗"""
print(F"## {message}")
payload.append(no_error_payload)
if os.environ.get("""TEST_TYPE""", """""") != "":
from slack_sdk import WebClient
lowercase__ = WebClient(token=os.environ["""SLACK_API_TOKEN"""])
if message != "No failed tests! 🤗":
lowercase__ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": message,
},
}
payload.append(md_report)
lowercase__ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": """*For more details:*""",
},
"""accessory""": {
"""type""": """button""",
"""text""": {
"""type""": """plain_text""",
"""text""": """Check Action results""",
"""emoji""": True,
},
"""url""": F"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
},
}
payload.append(action_button)
lowercase__ = {
"""type""": """context""",
"""elements""": [
{
"""type""": """plain_text""",
"""text""": F"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
}
],
}
payload.append(date_report)
lowercase__ = client.chat_postMessage(channel="""#accelerate-ci-daily""", text=message, blocks=payload)
lowercase__ = response.data["""ts"""]
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
lowercase__ = """"""
for i, row in enumerate(test_failures):
if row[0] != test_class:
lowercase__ = row[0]
else:
lowercase__ = """"""
lowercase__ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": F"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
},
}
client.chat_postMessage(
channel="""#accelerate-ci-daily""",
thread_ts=ts,
blocks=[payload],
) | 12 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
_SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
_SCREAMING_SNAKE_CASE : str = 2_56
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = ['melgan']
def __init__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
super().__init__()
# From MELGAN
snake_case = math.log(1E-5 ) # Matches MelGAN training.
snake_case = 4.0 # Largest value for most examples
snake_case = 1_2_8
self.register_modules(
notes_encoder=__snake_case , continuous_encoder=__snake_case , decoder=__snake_case , scheduler=__snake_case , melgan=__snake_case , )
def a_ ( self , __snake_case , __snake_case=(-1.0, 1.0) , __snake_case=False ):
snake_case , snake_case = output_range
if clip:
snake_case = torch.clip(__snake_case , self.min_value , self.max_value )
# Scale to [0, 1].
snake_case = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def a_ ( self , __snake_case , __snake_case=(-1.0, 1.0) , __snake_case=False ):
snake_case , snake_case = input_range
snake_case = torch.clip(__snake_case , __snake_case , __snake_case ) if clip else outputs
# Scale to [0, 1].
snake_case = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def a_ ( self , __snake_case , __snake_case , __snake_case ):
snake_case = input_tokens > 0
snake_case , snake_case = self.notes_encoder(
encoder_input_tokens=__snake_case , encoder_inputs_mask=__snake_case )
snake_case , snake_case = self.continuous_encoder(
encoder_inputs=__snake_case , encoder_inputs_mask=__snake_case )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def a_ ( self , __snake_case , __snake_case , __snake_case ):
snake_case = noise_time
if not torch.is_tensor(__snake_case ):
snake_case = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(__snake_case ) and len(timesteps.shape ) == 0:
snake_case = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
snake_case = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
snake_case = self.decoder(
encodings_and_masks=__snake_case , decoder_input_tokens=__snake_case , decoder_noise_time=__snake_case )
return logits
@torch.no_grad()
def __call__( self , __snake_case , __snake_case = None , __snake_case = 1_0_0 , __snake_case = True , __snake_case = "numpy" , __snake_case = None , __snake_case = 1 , ):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__snake_case , __snake_case ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(__snake_case )}.''' )
snake_case = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
snake_case = np.zeros([1, 0, self.n_dims] , np.floataa )
snake_case = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__snake_case , device=self.device )
for i, encoder_input_tokens in enumerate(__snake_case ):
if i == 0:
snake_case = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
snake_case = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__snake_case , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
snake_case = ones
snake_case = self.scale_features(
__snake_case , output_range=[-1.0, 1.0] , clip=__snake_case )
snake_case = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=__snake_case , continuous_mask=__snake_case , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
snake_case = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=__snake_case , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(__snake_case )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
snake_case = self.decode(
encodings_and_masks=__snake_case , input_tokens=__snake_case , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
snake_case = self.scheduler.step(__snake_case , __snake_case , __snake_case , generator=__snake_case ).prev_sample
snake_case = self.scale_to_features(__snake_case , input_range=[-1.0, 1.0] )
snake_case = mel[:1]
snake_case = mel.cpu().float().numpy()
snake_case = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__snake_case , __snake_case )
logger.info('''Generated segment''' , __snake_case )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
'''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
'''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )
if output_type == "numpy":
snake_case = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
snake_case = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=__snake_case )
| 127 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class A__ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
"""simple docstring"""
def __init__( self , __snake_case , __snake_case , __snake_case , __snake_case = 1.0 , __snake_case = None , ):
super().__init__()
snake_case = initial_learning_rate
snake_case = warmup_steps
snake_case = power
snake_case = decay_schedule_fn
snake_case = name
def __call__( self , __snake_case ):
with tf.name_scope(self.name or '''WarmUp''' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
snake_case = tf.cast(__snake_case , tf.floataa )
snake_case = tf.cast(self.warmup_steps , tf.floataa )
snake_case = global_step_float / warmup_steps_float
snake_case = self.initial_learning_rate * tf.math.pow(__snake_case , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=__snake_case , )
def a_ ( self ):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ = 0.0 ,UpperCamelCase_ = 0.9 ,UpperCamelCase_ = 0.999 ,UpperCamelCase_ = 1e-8 ,UpperCamelCase_ = None ,UpperCamelCase_ = None ,UpperCamelCase_ = 0.0 ,UpperCamelCase_ = 1.0 ,UpperCamelCase_ = None ,):
"""simple docstring"""
snake_case = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=UpperCamelCase_ ,decay_steps=num_train_steps - num_warmup_steps ,end_learning_rate=init_lr * min_lr_ratio ,power=UpperCamelCase_ ,)
if num_warmup_steps:
snake_case = WarmUp(
initial_learning_rate=UpperCamelCase_ ,decay_schedule_fn=UpperCamelCase_ ,warmup_steps=UpperCamelCase_ ,)
if weight_decay_rate > 0.0:
snake_case = AdamWeightDecay(
learning_rate=UpperCamelCase_ ,weight_decay_rate=UpperCamelCase_ ,beta_a=UpperCamelCase_ ,beta_a=UpperCamelCase_ ,epsilon=UpperCamelCase_ ,clipnorm=UpperCamelCase_ ,global_clipnorm=UpperCamelCase_ ,exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''] ,include_in_weight_decay=UpperCamelCase_ ,)
else:
snake_case = tf.keras.optimizers.Adam(
learning_rate=UpperCamelCase_ ,beta_a=UpperCamelCase_ ,beta_a=UpperCamelCase_ ,epsilon=UpperCamelCase_ ,clipnorm=UpperCamelCase_ ,global_clipnorm=UpperCamelCase_ ,)
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class A__ ( snake_case__ ):
"""simple docstring"""
def __init__( self , __snake_case = 0.001 , __snake_case = 0.9 , __snake_case = 0.999 , __snake_case = 1E-7 , __snake_case = False , __snake_case = 0.0 , __snake_case = None , __snake_case = None , __snake_case = "AdamWeightDecay" , **__snake_case , ):
super().__init__(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , **__snake_case )
snake_case = weight_decay_rate
snake_case = include_in_weight_decay
snake_case = exclude_from_weight_decay
@classmethod
def a_ ( cls , __snake_case ):
snake_case = {'''WarmUp''': WarmUp}
return super(__snake_case , cls ).from_config(__snake_case , custom_objects=__snake_case )
def a_ ( self , __snake_case , __snake_case , __snake_case ):
super(__snake_case , self )._prepare_local(__snake_case , __snake_case , __snake_case )
snake_case = tf.constant(
self.weight_decay_rate , name='''adam_weight_decay_rate''' )
def a_ ( self , __snake_case , __snake_case , __snake_case ):
snake_case = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''] , use_locking=self._use_locking , )
return tf.no_op()
def a_ ( self , __snake_case , __snake_case=None , **__snake_case ):
snake_case , snake_case = list(zip(*__snake_case ) )
return super(__snake_case , self ).apply_gradients(zip(__snake_case , __snake_case ) , name=__snake_case , **__snake_case )
def a_ ( self , __snake_case , __snake_case , __snake_case ):
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
snake_case = apply_state or {}
snake_case = apply_state.get((var_device, var_dtype) )
if coefficients is None:
snake_case = self._fallback_apply_state(__snake_case , __snake_case )
snake_case = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def a_ ( self , __snake_case , __snake_case , __snake_case=None ):
snake_case , snake_case = self._get_lr(var.device , var.dtype.base_dtype , __snake_case )
snake_case = self._decay_weights_op(__snake_case , __snake_case , __snake_case )
with tf.control_dependencies([decay] ):
return super(__snake_case , self )._resource_apply_dense(__snake_case , __snake_case , **__snake_case )
def a_ ( self , __snake_case , __snake_case , __snake_case , __snake_case=None ):
snake_case , snake_case = self._get_lr(var.device , var.dtype.base_dtype , __snake_case )
snake_case = self._decay_weights_op(__snake_case , __snake_case , __snake_case )
with tf.control_dependencies([decay] ):
return super(__snake_case , self )._resource_apply_sparse(__snake_case , __snake_case , __snake_case , **__snake_case )
def a_ ( self ):
snake_case = super().get_config()
config.update({'''weight_decay_rate''': self.weight_decay_rate} )
return config
def a_ ( self , __snake_case ):
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(__snake_case , __snake_case ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(__snake_case , __snake_case ) is not None:
return False
return True
class A__ ( snake_case__ ):
"""simple docstring"""
def __init__( self ):
snake_case = []
snake_case = None
@property
def a_ ( self ):
if self._accum_steps is None:
snake_case = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=__snake_case , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def a_ ( self ):
if not self._gradients:
raise ValueError('''The accumulator should be called first to initialize the gradients''' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self , __snake_case ):
if not self._gradients:
snake_case = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(__snake_case ) , trainable=__snake_case , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(__snake_case ) != len(self._gradients ):
raise ValueError(F'''Expected {len(self._gradients )} gradients, but got {len(__snake_case )}''' )
for accum_gradient, gradient in zip(self._gradients , __snake_case ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(__snake_case )
self._accum_steps.assign_add(1 )
def a_ ( self ):
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(__snake_case ) )
| 127 | 1 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
a =len(lowercase )
a =[[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
a =True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
a =False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
a =subset[i - 1][j]
if arr[i - 1] <= j:
a =subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod() | 351 |
"""simple docstring"""
from __future__ import annotations
def _A ( lowercase , lowercase , lowercase , ):
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative in a semiconductor''' )
elif hole_conc < 0:
raise ValueError('''Hole concentration cannot be negative in a semiconductor''' )
elif intrinsic_conc < 0:
raise ValueError(
'''Intrinsic concentration cannot be negative in a semiconductor''' )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod() | 215 | 0 |
"""simple docstring"""
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCAmelCase__ : str = TypeVar('T')
class lowerCAmelCase_ (Generic[T] ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ = True ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : dict[T, list[T]] = {} # dictionary of lists
SCREAMING_SNAKE_CASE__ : Tuple = directed
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> GraphAdjacencyList[T]:
"""simple docstring"""
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(SCREAMING_SNAKE_CASE__ )
self.adj_list[destination_vertex].append(SCREAMING_SNAKE_CASE__ )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [destination_vertex]
SCREAMING_SNAKE_CASE__ : Tuple = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(SCREAMING_SNAKE_CASE__ )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
SCREAMING_SNAKE_CASE__ : Any = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
SCREAMING_SNAKE_CASE__ : Any = [destination_vertex]
SCREAMING_SNAKE_CASE__ : Optional[int] = []
return self
def __repr__(self ) -> str:
"""simple docstring"""
return pformat(self.adj_list )
| 25 |
"""simple docstring"""
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 lowerCAmelCase_ (a__ , a__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = IFPipeline
__UpperCamelCase : Dict = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
__UpperCamelCase : Any = TEXT_TO_IMAGE_BATCH_PARAMS
__UpperCamelCase : Union[str, Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
return self._get_dummy_components()
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ) -> List[Any]:
"""simple docstring"""
if str(SCREAMING_SNAKE_CASE__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : Dict = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE__ : Any = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
self._test_save_load_local()
def __magic_name__ (self ) -> Dict:
"""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 __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict = IFSuperResolutionPipeline.from_pretrained(
"""DeepFloyd/IF-II-L-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa , text_encoder=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("""cuda""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe_a.encode_prompt("""anime turtle""" , device="""cuda""" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
SCREAMING_SNAKE_CASE__ : Union[str, Any] = IFImgaImgPipeline(**pipe_a.components )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
SCREAMING_SNAKE_CASE__ : Optional[Any] = IFInpaintingPipeline(**pipe_a.components )
SCREAMING_SNAKE_CASE__ : int = 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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
"""simple docstring"""
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , generator=SCREAMING_SNAKE_CASE__ , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : int = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE__ : Any = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
SCREAMING_SNAKE_CASE__ : Optional[int] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : int = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[str] = output.images[0]
assert image.shape == (2_56, 2_56, 3)
SCREAMING_SNAKE_CASE__ : List[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE__ : 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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , generator=SCREAMING_SNAKE_CASE__ , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : Dict = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
SCREAMING_SNAKE_CASE__ : str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[Any] = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , original_image=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[Any] = output.images[0]
assert image.shape == (2_56, 2_56, 3)
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE__ : str = 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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , mask_image=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , generator=SCREAMING_SNAKE_CASE__ , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[Any] = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
SCREAMING_SNAKE_CASE__ : Optional[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : int = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(1 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , mask_image=SCREAMING_SNAKE_CASE__ , original_image=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : Dict = output.images[0]
assert image.shape == (2_56, 2_56, 3)
SCREAMING_SNAKE_CASE__ : List[str] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE__ : 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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def lowercase_ ( ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 25 | 1 |
import numpy
# List of input, output pairs
__UpperCAmelCase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__UpperCAmelCase = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
__UpperCAmelCase = [2, 4, 1, 5]
__UpperCAmelCase = len(train_data)
__UpperCAmelCase = 0.009
def A__ ( __lowerCamelCase, __lowerCamelCase="train" ):
return calculate_hypothesis_value(__lowerCamelCase, __lowerCamelCase ) - output(
__lowerCamelCase, __lowerCamelCase )
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
for i in range(len(__lowerCamelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def A__ ( __lowerCamelCase, __lowerCamelCase ):
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def A__ ( __lowerCamelCase, __lowerCamelCase ):
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def A__ ( __lowerCamelCase, __lowerCamelCase=m ):
SCREAMING_SNAKE_CASE_ = 0
for i in range(__lowerCamelCase ):
if index == -1:
summation_value += _error(__lowerCamelCase )
else:
summation_value += _error(__lowerCamelCase ) * train_data[i][0][index]
return summation_value
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = summation_of_cost_derivative(__lowerCamelCase, __lowerCamelCase ) / m
return cost_derivative_value
def A__ ( ):
global parameter_vector
# Tune these values to set a tolerance value for predicted output
SCREAMING_SNAKE_CASE_ = 0.00_00_02
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
while True:
j += 1
SCREAMING_SNAKE_CASE_ = [0, 0, 0, 0]
for i in range(0, len(__lowerCamelCase ) ):
SCREAMING_SNAKE_CASE_ = get_cost_derivative(i - 1 )
SCREAMING_SNAKE_CASE_ = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
__lowerCamelCase, __lowerCamelCase, atol=__lowerCamelCase, rtol=__lowerCamelCase, ):
break
SCREAMING_SNAKE_CASE_ = temp_parameter_vector
print(('''Number of iterations:''', j) )
def A__ ( ):
for i in range(len(__lowerCamelCase ) ):
print(('''Actual output value:''', output(__lowerCamelCase, '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(__lowerCamelCase, '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 257 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def A__ ( ):
SCREAMING_SNAKE_CASE_ = 9
SCREAMING_SNAKE_CASE_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
SCREAMING_SNAKE_CASE_ = kruskal(__lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(__lowerCamelCase ) == sorted(__lowerCamelCase )
| 257 | 1 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__snake_case = pd.read_csv('''sample_data.csv''', header=None)
__snake_case = df.shape[:1][0]
# If you're using some other dataset input the target column
__snake_case = df.iloc[:, 1:2]
__snake_case = actual_data.values.reshape(len_data, 1)
__snake_case = MinMaxScaler().fit_transform(actual_data)
__snake_case = 10
__snake_case = 5
__snake_case = 20
__snake_case = len_data - periods * look_back
__snake_case = actual_data[:division]
__snake_case = actual_data[division - look_back :]
__snake_case , __snake_case = [], []
__snake_case , __snake_case = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
__snake_case = np.array(train_x)
__snake_case = np.array(test_x)
__snake_case = np.array([list(i.ravel()) for i in train_y])
__snake_case = np.array([list(i.ravel()) for i in test_y])
__snake_case = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss='''mean_squared_error''', optimizer='''adam''')
__snake_case = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
__snake_case = model.predict(x_test) | 97 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase : int ={
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ASTConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Union[str, Any] =[
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ASTForAudioClassification''',
'''ASTModel''',
'''ASTPreTrainedModel''',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[int] =['''ASTFeatureExtractor''']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
lowerCamelCase : Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 189 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 100 ):
lowerCAmelCase = (n * (n + 1) // 2) ** 2
lowerCAmelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Any = {
'''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:
__UpperCamelCase : Tuple = ['''LayoutLMv2TokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[int] = ['''LayoutLMv2FeatureExtractor''']
__UpperCamelCase : Optional[int] = ['''LayoutLMv2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : 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
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 309 | 1 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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 CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __magic_name__ ( lowercase__):
def UpperCAmelCase__ ( self : Tuple ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowercase , '''embed_dim''' ) )
self.parent.assertTrue(hasattr(__lowercase , '''num_heads''' ) )
class __magic_name__ :
def __init__( self : Tuple , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Dict=13 , lowerCamelCase__ : str=64 , lowerCamelCase__ : str=3 , lowerCamelCase__ : Tuple=[16, 48, 96] , lowerCamelCase__ : Dict=[1, 3, 6] , lowerCamelCase__ : Union[str, Any]=[1, 2, 10] , lowerCamelCase__ : Optional[int]=[7, 3, 3] , lowerCamelCase__ : Tuple=[4, 2, 2] , lowerCamelCase__ : List[str]=[2, 1, 1] , lowerCamelCase__ : List[str]=[2, 2, 2] , lowerCamelCase__ : Optional[Any]=[False, False, True] , lowerCamelCase__ : Optional[Any]=[0.0, 0.0, 0.0] , lowerCamelCase__ : Tuple=0.02 , lowerCamelCase__ : Tuple=1E-1_2 , lowerCamelCase__ : List[str]=True , lowerCamelCase__ : Dict=True , lowerCamelCase__ : Optional[int]=2 , ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Any = parent
UpperCamelCase__ : List[Any] = batch_size
UpperCamelCase__ : Optional[int] = image_size
UpperCamelCase__ : Optional[int] = patch_sizes
UpperCamelCase__ : Optional[int] = patch_stride
UpperCamelCase__ : Optional[Any] = patch_padding
UpperCamelCase__ : Tuple = is_training
UpperCamelCase__ : Any = use_labels
UpperCamelCase__ : List[str] = num_labels
UpperCamelCase__ : int = num_channels
UpperCamelCase__ : Optional[Any] = embed_dim
UpperCamelCase__ : Optional[Any] = num_heads
UpperCamelCase__ : Optional[Any] = stride_kv
UpperCamelCase__ : Union[str, Any] = depth
UpperCamelCase__ : Optional[int] = cls_token
UpperCamelCase__ : Tuple = attention_drop_rate
UpperCamelCase__ : str = initializer_range
UpperCamelCase__ : Union[str, Any] = layer_norm_eps
def UpperCAmelCase__ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ : int = None
if self.use_labels:
UpperCamelCase__ : Dict = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase__ ( self : str ) -> Optional[int]:
'''simple docstring'''
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def UpperCAmelCase__ ( self : Optional[int] , lowerCamelCase__ : Any , lowerCamelCase__ : Dict , lowerCamelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = CvtModel(config=__lowercase )
model.to(__lowercase )
model.eval()
UpperCamelCase__ : int = model(__lowercase )
UpperCamelCase__ : Tuple = (self.image_size, self.image_size)
UpperCamelCase__ : Union[str, Any] = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCamelCase__ : Tuple = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCamelCase__ : Union[str, Any] = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def UpperCAmelCase__ ( self : str , lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Optional[Any] ) -> int:
'''simple docstring'''
UpperCamelCase__ : Dict = self.num_labels
UpperCamelCase__ : List[str] = CvtForImageClassification(__lowercase )
model.to(__lowercase )
model.eval()
UpperCamelCase__ : Any = model(__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ : Any = self.prepare_config_and_inputs()
UpperCamelCase__ : Union[str, Any] = config_and_inputs
UpperCamelCase__ : str = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowercase__ , lowercase__ , unittest.TestCase):
A: Union[str, Any] = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
A: List[str] = (
{'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
if is_torch_available()
else {}
)
A: int = False
A: str = False
A: Tuple = False
A: List[Any] = False
A: Optional[Any] = False
def UpperCAmelCase__ ( self : List[str] ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = CvtModelTester(self )
UpperCamelCase__ : Any = ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37 )
def UpperCAmelCase__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
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 UpperCAmelCase__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return
@unittest.skip(reason='''Cvt does not output attentions''' )
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def UpperCAmelCase__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def UpperCAmelCase__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : int ) -> str:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ : str = model_class(__lowercase )
UpperCamelCase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ : Optional[int] = [*signature.parameters.keys()]
UpperCamelCase__ : List[str] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __lowercase )
def UpperCAmelCase__ ( self : int ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def UpperCAmelCase__ ( self : str ) -> List[str]:
'''simple docstring'''
def check_hidden_states_output(lowerCamelCase__ : str , lowerCamelCase__ : Any , lowerCamelCase__ : Tuple ):
UpperCamelCase__ : int = model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
UpperCamelCase__ : Optional[int] = model(**self._prepare_for_class(__lowercase , __lowercase ) )
UpperCamelCase__ : Optional[int] = outputs.hidden_states
UpperCamelCase__ : Union[str, Any] = len(self.model_tester.depth )
self.assertEqual(len(__lowercase ) , __lowercase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ : Union[str, Any] = True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase__ : List[Any] = True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
def UpperCAmelCase__ ( self : int ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowercase )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ : Dict = CvtModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def _a ( ):
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase):
@cached_property
def UpperCAmelCase__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def UpperCAmelCase__ ( self : Dict ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__lowercase )
UpperCamelCase__ : Optional[int] = self.default_image_processor
UpperCamelCase__ : Dict = prepare_img()
UpperCamelCase__ : Any = image_processor(images=__lowercase , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
UpperCamelCase__ : str = model(**__lowercase )
# verify the logits
UpperCamelCase__ : str = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowercase )
UpperCamelCase__ : Optional[Any] = torch.tensor([0.9285, 0.9015, -0.3150] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1E-4 ) )
| 146 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class a ( lowercase__ ):
"""simple docstring"""
a : str = 'mvp'
a : Optional[Any] = ['past_key_values']
a : int = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Dict , __lowercase : Tuple=50267 , __lowercase : Optional[int]=1024 , __lowercase : Any=12 , __lowercase : List[Any]=4096 , __lowercase : Optional[Any]=16 , __lowercase : List[str]=12 , __lowercase : Optional[Any]=4096 , __lowercase : Tuple=16 , __lowercase : Union[str, Any]=0.0 , __lowercase : str=0.0 , __lowercase : Any="gelu" , __lowercase : Any=1024 , __lowercase : List[str]=0.1 , __lowercase : List[Any]=0.0 , __lowercase : int=0.0 , __lowercase : str=0.02 , __lowercase : Tuple=0.0 , __lowercase : Union[str, Any]=False , __lowercase : Dict=True , __lowercase : List[Any]=1 , __lowercase : Optional[Any]=0 , __lowercase : Union[str, Any]=2 , __lowercase : Optional[int]=True , __lowercase : Dict=2 , __lowercase : int=2 , __lowercase : Union[str, Any]=False , __lowercase : Union[str, Any]=100 , __lowercase : str=800 , **__lowercase : Union[str, Any] , ) -> Any:
__UpperCAmelCase : List[str] = vocab_size
__UpperCAmelCase : Union[str, Any] = max_position_embeddings
__UpperCAmelCase : Optional[Any] = d_model
__UpperCAmelCase : int = encoder_ffn_dim
__UpperCAmelCase : Tuple = encoder_layers
__UpperCAmelCase : List[str] = encoder_attention_heads
__UpperCAmelCase : Any = decoder_ffn_dim
__UpperCAmelCase : List[Any] = decoder_layers
__UpperCAmelCase : str = decoder_attention_heads
__UpperCAmelCase : Optional[int] = dropout
__UpperCAmelCase : Tuple = attention_dropout
__UpperCAmelCase : int = activation_dropout
__UpperCAmelCase : List[Any] = activation_function
__UpperCAmelCase : Tuple = init_std
__UpperCAmelCase : Any = encoder_layerdrop
__UpperCAmelCase : Union[str, Any] = decoder_layerdrop
__UpperCAmelCase : List[Any] = classifier_dropout
__UpperCAmelCase : Optional[int] = use_cache
__UpperCAmelCase : int = encoder_layers
__UpperCAmelCase : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
__UpperCAmelCase : Tuple = use_prompt
__UpperCAmelCase : str = prompt_length
__UpperCAmelCase : Union[str, Any] = prompt_mid_dim
super().__init__(
pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase , is_encoder_decoder=__lowercase , decoder_start_token_id=__lowercase , forced_eos_token_id=__lowercase , **__lowercase , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , __lowercase ):
__UpperCAmelCase : Optional[Any] = self.bos_token_id
warnings.warn(
f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
"""The config can simply be saved and uploaded again to be fixed.""" )
| 114 | 0 |
import datasets
from .evaluate import evaluate
lowercase : Any = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n"
lowercase : List[Any] = "\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"
lowercase : Optional[Any] = "\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair as given in the references (see below)\n - 'prediction_text': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair (see above),\n - 'answers': a Dict in the CUAD dataset format\n {\n 'text': list of possible texts for the answer, as a list of strings\n 'answer_start': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n 'exact_match': Exact match (the normalized answer exactly match the gold answer)\n 'f1': The F-score of predicted tokens versus the gold answer\n 'aupr': Area Under the Precision-Recall curve\n 'prec_at_80_recall': Precision at 80% recall\n 'prec_at_90_recall': Precision at 90% recall\nExamples:\n >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> cuad_metric = datasets.load_metric(\"cuad\")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __snake_case ( datasets.Metric ):
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) ,codebase_urls=["""https://www.atticusprojectai.org/cuad"""] ,reference_urls=["""https://www.atticusprojectai.org/cuad"""] ,)
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ):
'''simple docstring'''
lowercase : int = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
lowercase : Optional[int] = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
lowercase : List[Any] = evaluate(dataset=a_ ,predictions=a_ )
return score
| 359 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase : Tuple = """<<<<<<< This should probably be modified because it mentions: """
lowercase : Any = """=======
>>>>>>>
"""
lowercase : List[str] = [
"""TextEncoderConfig""",
"""ByteTextEncoder""",
"""SubwordTextEncoder""",
"""encoder_config""",
"""maybe_build_from_corpus""",
"""manual_dir""",
]
lowercase : Any = [
# (pattern, replacement)
# Order is important here for some replacements
(R"""tfds\.core""", R"""datasets"""),
(R"""tf\.io\.gfile\.GFile""", R"""open"""),
(R"""tf\.([\w\d]+)""", R"""datasets.Value('\1')"""),
(R"""tfds\.features\.Text\(\)""", R"""datasets.Value('string')"""),
(R"""tfds\.features\.Text\(""", R"""datasets.Value('string'),"""),
(R"""features\s*=\s*tfds.features.FeaturesDict\(""", R"""features=datasets.Features("""),
(R"""tfds\.features\.FeaturesDict\(""", R"""dict("""),
(R"""The TensorFlow Datasets Authors""", R"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
(R"""tfds\.""", R"""datasets."""),
(R"""dl_manager\.manual_dir""", R"""self.config.data_dir"""),
(R"""self\.builder_config""", R"""self.config"""),
]
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[Any]:
return ConvertCommand(args.tfds_path , args.datasets_directory )
class __snake_case ( lowerCAmelCase ):
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
lowercase : str = parser.add_parser(
"""convert""" ,help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.""" ,)
train_parser.add_argument(
"""--tfds_path""" ,type=snake_case ,required=snake_case ,help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.""" ,)
train_parser.add_argument(
"""--datasets_directory""" ,type=snake_case ,required=snake_case ,help="""Path to the HuggingFace Datasets folder.""" )
train_parser.set_defaults(func=snake_case )
def __init__( self ,snake_case ,snake_case ,*snake_case ):
'''simple docstring'''
lowercase : Optional[Any] = get_logger("""datasets-cli/converting""" )
lowercase : Optional[int] = tfds_path
lowercase : Dict = datasets_directory
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
if os.path.isdir(self._tfds_path ):
lowercase : List[str] = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
lowercase : Tuple = os.path.dirname(self._tfds_path )
else:
raise ValueError("""--tfds_path is neither a directory nor a file. Please check path.""" )
lowercase : Optional[int] = os.path.abspath(self._datasets_directory )
self._logger.info(f"Converting datasets from {abs_tfds_path} to {abs_datasets_path}" )
lowercase : List[Any] = []
lowercase : Optional[int] = []
lowercase : Dict = {}
if os.path.isdir(self._tfds_path ):
lowercase : int = os.listdir(snake_case )
else:
lowercase : List[Any] = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(f"Looking at file {f_name}" )
lowercase : List[Any] = os.path.join(snake_case ,snake_case )
lowercase : List[str] = os.path.join(snake_case ,snake_case )
if not os.path.isfile(snake_case ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("""Skipping file""" )
continue
with open(snake_case ,encoding="""utf-8""" ) as f:
lowercase : str = f.readlines()
lowercase : Union[str, Any] = []
lowercase : Optional[Any] = False
lowercase : Optional[Any] = False
lowercase : Optional[int] = []
for line in lines:
lowercase : int = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
lowercase : Union[str, Any] = """import datasets\n"""
elif "import tensorflow" in out_line:
# order is important here
lowercase : List[Any] = """"""
continue
elif "from absl import logging" in out_line:
lowercase : Optional[int] = """from datasets import logging\n"""
elif "getLogger" in out_line:
lowercase : Any = out_line.replace("""getLogger""" ,"""get_logger""" )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
lowercase : Optional[Any] = True
lowercase : Optional[Any] = list(filter(lambda snake_case : e in out_line ,snake_case ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(snake_case ) + """\n""" )
out_lines.append(snake_case )
out_lines.append(snake_case )
continue
else:
for pattern, replacement in TO_CONVERT:
lowercase : Union[str, Any] = re.sub(snake_case ,snake_case ,snake_case )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
lowercase : Dict = re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)""" ,snake_case )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(""",""" ) )
lowercase : Optional[int] = """from . import """ + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(f"Error converting {out_line.strip()}" )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
lowercase : Any = True
out_lines.append(snake_case )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
lowercase : Union[str, Any] = f_name.replace(""".py""" ,"""""" )
lowercase : Optional[Any] = os.path.join(snake_case ,snake_case )
lowercase : List[str] = os.path.join(snake_case ,snake_case )
os.makedirs(snake_case ,exist_ok=snake_case )
self._logger.info(f"Adding directory {output_dir}" )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(snake_case )
if needs_manual_update:
with_manual_update.append(snake_case )
with open(snake_case ,"""w""" ,encoding="""utf-8""" ) as f:
f.writelines(snake_case )
self._logger.info(f"Converted in {output_file}" )
for utils_file in utils_files:
try:
lowercase : Optional[int] = os.path.basename(snake_case )
lowercase : int = imports_to_builder_map[f_name.replace(""".py""" ,"""""" )]
self._logger.info(f"Moving {dest_folder} to {utils_file}" )
shutil.copy(snake_case ,snake_case )
except KeyError:
self._logger.error(f"Cannot find destination folder for {utils_file}. Please copy manually." )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
f"You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'." )
| 285 | 0 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
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 TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : int=13 , UpperCamelCase__ : Union[str, Any]=30 , UpperCamelCase__ : Optional[Any]=2 , UpperCamelCase__ : Optional[Any]=3 , UpperCamelCase__ : Any=True , UpperCamelCase__ : str=True , UpperCamelCase__ : Tuple=32 , UpperCamelCase__ : Optional[Any]=2 , UpperCamelCase__ : Optional[int]=4 , UpperCamelCase__ : List[str]=37 , UpperCamelCase__ : Tuple="gelu" , UpperCamelCase__ : Optional[int]=0.1 , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : int=10 , UpperCamelCase__ : List[str]=0.02 , UpperCamelCase__ : Tuple=3 , UpperCamelCase__ : List[str]=0.6 , UpperCamelCase__ : Dict=None , ) -> str:
"""simple docstring"""
__magic_name__ = parent
__magic_name__ = batch_size
__magic_name__ = image_size
__magic_name__ = patch_size
__magic_name__ = num_channels
__magic_name__ = is_training
__magic_name__ = use_labels
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = intermediate_size
__magic_name__ = hidden_act
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = type_sequence_label_size
__magic_name__ = initializer_range
__magic_name__ = mask_ratio
__magic_name__ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
__magic_name__ = (image_size // patch_size) ** 2
__magic_name__ = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _lowercase ( self : Dict ) -> Tuple:
"""simple docstring"""
__magic_name__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ = None
if self.use_labels:
__magic_name__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ = self.get_config()
return config, pixel_values, labels
def _lowercase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
return ViTMAEConfig(
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 , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_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=UpperCamelCase__ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _lowercase ( self : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int ) -> str:
"""simple docstring"""
__magic_name__ = TFViTMAEModel(config=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , training=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : Optional[int] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] ) -> Any:
"""simple docstring"""
__magic_name__ = TFViTMAEForPreTraining(UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , training=UpperCamelCase__ )
# expected sequence length = num_patches
__magic_name__ = (self.image_size // self.patch_size) ** 2
__magic_name__ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
__magic_name__ = 1
__magic_name__ = TFViTMAEForPreTraining(UpperCamelCase__ )
__magic_name__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__magic_name__ = model(UpperCamelCase__ , training=UpperCamelCase__ )
__magic_name__ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _lowercase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = self.prepare_config_and_inputs()
((__magic_name__) , (__magic_name__) , (__magic_name__)) = config_and_inputs
__magic_name__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ ( _A , _A , unittest.TestCase ):
'''simple docstring'''
a__ = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
a__ = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
def _lowercase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
__magic_name__ = TFViTMAEModelTester(self )
__magic_name__ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ , hidden_size=37 )
def _lowercase ( self : Optional[int] ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def _lowercase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
pass
def _lowercase ( self : Optional[int] ) -> Any:
"""simple docstring"""
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ = model_class(UpperCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
__magic_name__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase__ , tf.keras.layers.Layer ) )
def _lowercase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ = model_class(UpperCamelCase__ )
__magic_name__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ = [*signature.parameters.keys()]
__magic_name__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def _lowercase ( self : List[Any] ) -> str:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowercase ( self : Any ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__ )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
__magic_name__ = model_class(UpperCamelCase__ )
__magic_name__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , noise=UpperCamelCase__ )
__magic_name__ = copy.deepcopy(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
__magic_name__ = model(**UpperCamelCase__ , noise=UpperCamelCase__ )
__magic_name__ = outputs_dict[0].numpy()
__magic_name__ = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) , 1E-6 )
def _lowercase ( self : Dict ) -> Any:
"""simple docstring"""
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(UpperCamelCase__ : int ):
__magic_name__ = {}
for k, v in inputs_dict.items():
if tf.is_tensor(UpperCamelCase__ ):
__magic_name__ = v.numpy()
else:
__magic_name__ = np.array(UpperCamelCase__ )
return inputs_np_dict
for model_class in self.all_model_classes:
__magic_name__ = model_class(UpperCamelCase__ )
__magic_name__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = prepare_numpy_arrays(UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , noise=UpperCamelCase__ )
__magic_name__ = model(**UpperCamelCase__ , noise=UpperCamelCase__ )
self.assert_outputs_same(UpperCamelCase__ , UpperCamelCase__ )
def _lowercase ( self : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : Any , UpperCamelCase__ : Tuple ) -> List[Any]:
"""simple docstring"""
np.random.seed(2 )
__magic_name__ = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
__magic_name__ = tf.constant(UpperCamelCase__ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
__magic_name__ = tf_noise
super().check_pt_tf_models(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def _lowercase ( self : List[str] ) -> Dict:
"""simple docstring"""
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(UpperCamelCase__ )
if module_member_name.endswith("""MainLayer""" )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("""MainLayer""" )] == model_class.__name__[: -len("""Model""" )]
for module_member in (getattr(UpperCamelCase__ , UpperCamelCase__ ),)
if isinstance(UpperCamelCase__ , UpperCamelCase__ )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(UpperCamelCase__ , """_keras_serializable""" , UpperCamelCase__ )
}
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
__magic_name__ = tf.convert_to_tensor(UpperCamelCase__ )
inputs_dict.update({"""noise""": noise} )
for main_layer_class in tf_main_layer_classes:
__magic_name__ = main_layer_class(UpperCamelCase__ )
__magic_name__ = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
__magic_name__ = tf.keras.Model(UpperCamelCase__ , outputs=main_layer(UpperCamelCase__ ) )
__magic_name__ = model(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ = os.path.join(UpperCamelCase__ , """keras_model.h5""" )
model.save(UpperCamelCase__ )
__magic_name__ = tf.keras.models.load_model(
UpperCamelCase__ , custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(UpperCamelCase__ , tf.keras.Model )
__magic_name__ = model(UpperCamelCase__ )
self.assert_outputs_same(UpperCamelCase__ , UpperCamelCase__ )
@slow
def _lowercase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
__magic_name__ = model_class(UpperCamelCase__ )
__magic_name__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , noise=UpperCamelCase__ )
if model_class.__name__ == "TFViTMAEModel":
__magic_name__ = outputs.last_hidden_state.numpy()
__magic_name__ = 0
else:
__magic_name__ = outputs.logits.numpy()
__magic_name__ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase__ , saved_model=UpperCamelCase__ )
__magic_name__ = model_class.from_pretrained(UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , noise=UpperCamelCase__ )
if model_class.__name__ == "TFViTMAEModel":
__magic_name__ = after_outputs["""last_hidden_state"""].numpy()
__magic_name__ = 0
else:
__magic_name__ = after_outputs["""logits"""].numpy()
__magic_name__ = 0
__magic_name__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(UpperCamelCase__ , 1E-5 )
def _lowercase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
__magic_name__ = model_class(UpperCamelCase__ )
__magic_name__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , noise=UpperCamelCase__ )
__magic_name__ = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(UpperCamelCase__ )
__magic_name__ = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
__magic_name__ = model_class.from_config(model.config )
__magic_name__ = new_model(UpperCamelCase__ ) # Build model
new_model.set_weights(model.get_weights() )
__magic_name__ = new_model(UpperCamelCase__ , noise=UpperCamelCase__ )
self.assert_outputs_same(UpperCamelCase__ , UpperCamelCase__ )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def _lowercase ( self : Dict ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
pass
@slow
def _lowercase ( self : Any ) -> str:
"""simple docstring"""
__magic_name__ = TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(UpperCamelCase__ )
def a__ ( ):
'''simple docstring'''
__magic_name__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _lowercase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def _lowercase ( self : Dict ) -> int:
"""simple docstring"""
np.random.seed(2 )
__magic_name__ = TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" )
__magic_name__ = self.default_image_processor
__magic_name__ = prepare_img()
__magic_name__ = image_processor(images=UpperCamelCase__ , return_tensors="""tf""" )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
__magic_name__ = ViTMAEConfig()
__magic_name__ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(1, num_patches) )
# forward pass
__magic_name__ = model(**UpperCamelCase__ , noise=UpperCamelCase__ )
# verify the logits
__magic_name__ = tf.convert_to_tensor([1, 196, 768] )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
__magic_name__ = tf.convert_to_tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] , UpperCamelCase__ , atol=1E-4 )
| 88 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __UpperCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=lowerCAmelCase_ ).to(lowerCAmelCase_ )
_snake_case = AutoTokenizer.from_pretrained('google/mt5-small' )
_snake_case = tokenizer('Hello there' , return_tensors='pt' ).input_ids
_snake_case = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
_snake_case = model(input_ids.to(lowerCAmelCase_ ) , labels=labels.to(lowerCAmelCase_ ) ).loss
_snake_case = -(labels.shape[-1] * loss.item())
_snake_case = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 42 | 0 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a_ : Dict = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
booktitle = {},
year = {2002},
pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
author = \"Lin, Chin-Yew and
Och, Franz Josef\",
booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
month = \"aug 23{--}aug 27\",
year = \"2004\",
address = \"Geneva, Switzerland\",
publisher = \"COLING\",
url = \"https://www.aclweb.org/anthology/C04-1072\",
pages = \"501--507\",
}
"""
a_ : int = """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.
Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].
BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""
a_ : Optional[int] = """
Computes BLEU score of translated segments against one or more references.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
'bleu': bleu score,
'precisions': geometric mean of n-gram precisions,
'brevity_penalty': brevity penalty,
'length_ratio': ratio of lengths,
'translation_length': translation_length,
'reference_length': reference_length
Examples:
>>> predictions = [
... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample
... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample
... ]
>>> references = [
... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)
... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)
... ]
>>> bleu = datasets.load_metric(\"bleu\")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results[\"bleu\"])
1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def lowercase__ ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''', id='''token''' ), id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''', id='''token''' ), id='''sequence''' ), id='''references''' ),
} ), codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'''], reference_urls=[
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
], )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase=4, lowerCAmelCase=False ):
"""simple docstring"""
lowerCamelCase_ =compute_bleu(
reference_corpus=lowerCAmelCase, translation_corpus=lowerCAmelCase, max_order=lowerCAmelCase, smooth=lowerCAmelCase )
((lowerCamelCase_), (lowerCamelCase_), (lowerCamelCase_), (lowerCamelCase_), (lowerCamelCase_), (lowerCamelCase_)) =score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 362 |
'''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_ ( __snake_case : Any ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =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.''' )
lowerCamelCase_ =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.''' )
lowerCamelCase_ =components[:-1] + [test_fn.replace('''.py''' , '''''' )]
lowerCamelCase_ ='''.'''.join(__snake_case )
return test_module_path
def a_ ( __snake_case : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =get_module_path(__snake_case )
lowerCamelCase_ =importlib.import_module(__snake_case )
return test_module
def a_ ( __snake_case : Dict ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =get_test_module(__snake_case )
for attr in dir(__snake_case ):
if attr.endswith('''ModelTester''' ):
tester_classes.append(getattr(__snake_case , __snake_case ) )
# sort with class names
return sorted(__snake_case , key=lambda __snake_case : x.__name__ )
def a_ ( __snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =get_test_module(__snake_case )
for attr in dir(__snake_case ):
lowerCamelCase_ =getattr(__snake_case , __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).
lowerCamelCase_ =getattr(__snake_case , '''all_model_classes''' , [] )
if len(__snake_case ) > 0:
test_classes.append(__snake_case )
# sort with class names
return sorted(__snake_case , key=lambda __snake_case : x.__name__ )
def a_ ( __snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =get_test_classes(__snake_case )
lowerCamelCase_ =set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(__snake_case , key=lambda __snake_case : x.__name__ )
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
lowerCamelCase_ =test_class()
if hasattr(__snake_case , '''setUp''' ):
test.setUp()
lowerCamelCase_ =None
if hasattr(__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:
lowerCamelCase_ =test.model_tester.__class__
return model_tester
def a_ ( __snake_case : Dict , __snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =get_test_classes(__snake_case )
lowerCamelCase_ =[]
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(__snake_case )
# sort with class names
return sorted(__snake_case , key=lambda __snake_case : x.__name__ )
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ =get_test_classes_for_model(__snake_case , __snake_case )
lowerCamelCase_ =[]
for test_class in test_classes:
lowerCamelCase_ =get_model_tester_from_test_class(__snake_case )
if tester_class is not None:
tester_classes.append(__snake_case )
# sort with class names
return sorted(__snake_case , key=lambda __snake_case : x.__name__ )
def a_ ( __snake_case : Tuple ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =get_test_classes(__snake_case )
lowerCamelCase_ ={test_class: get_model_tester_from_test_class(__snake_case ) for test_class in test_classes}
return test_tester_mapping
def a_ ( __snake_case : Dict ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =get_model_classes(__snake_case )
lowerCamelCase_ ={
model_class: get_test_classes_for_model(__snake_case , __snake_case ) for model_class in model_classes
}
return model_test_mapping
def a_ ( __snake_case : Optional[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =get_model_classes(__snake_case )
lowerCamelCase_ ={
model_class: get_tester_classes_for_model(__snake_case , __snake_case ) for model_class in model_classes
}
return model_to_tester_mapping
def a_ ( __snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
if isinstance(__snake_case , __snake_case ):
return o
elif isinstance(__snake_case , __snake_case ):
return o.__name__
elif isinstance(__snake_case , (list, tuple) ):
return [to_json(__snake_case ) for x in o]
elif isinstance(__snake_case , __snake_case ):
return {to_json(__snake_case ): to_json(__snake_case ) for k, v in o.items()}
else:
return o
| 6 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
UpperCamelCase = TypeVar('''_T''')
class lowerCAmelCase_ ( Generic[_T] ):
'''simple docstring'''
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Iterable[_T] | None = None ) -> None:
'''simple docstring'''
A: list[_T] = list(iterable or [] )
A: list[_T] = []
def __len__( self : List[str] ) -> int:
'''simple docstring'''
return len(self._stacka ) + len(self._stacka )
def __repr__( self : int ) -> str:
'''simple docstring'''
return f"""Queue({tuple(self._stacka[::-1] + self._stacka )})"""
def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : _T ) -> None:
'''simple docstring'''
self._stacka.append(SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : List[str] ) -> _T:
'''simple docstring'''
A: str = self._stacka.pop
A: Optional[Any] = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError('''Queue is empty''' )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 319 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : torch.FloatTensor
UpperCamelCase_ : torch.FloatTensor
UpperCamelCase_ : Optional[torch.FloatTensor] = None
class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Tuple = 2
@register_to_config
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : float = 0.02 , SCREAMING_SNAKE_CASE_ : float = 1_00 , SCREAMING_SNAKE_CASE_ : float = 1.007 , SCREAMING_SNAKE_CASE_ : float = 80 , SCREAMING_SNAKE_CASE_ : float = 0.05 , SCREAMING_SNAKE_CASE_ : float = 50 , ) -> Optional[int]:
'''simple docstring'''
A: Union[str, Any] = sigma_max
# setable values
A: int = None
A: np.IntTensor = None
A: torch.FloatTensor = None # sigma(t_i)
def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Optional[int] = None ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, torch.device] = None ) -> Optional[Any]:
'''simple docstring'''
A: List[Any] = num_inference_steps
A: List[str] = np.arange(0 , self.num_inference_steps )[::-1].copy()
A: Any = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
A: str = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
A: Tuple = torch.tensor(SCREAMING_SNAKE_CASE_ , dtype=torch.floataa , device=SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : Optional[torch.Generator] = None ) -> Tuple[torch.FloatTensor, float]:
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
A: str = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
A: List[str] = 0
# sample eps ~ N(0, S_noise^2 * I)
A: Optional[Any] = self.config.s_noise * randn_tensor(sample.shape , generator=SCREAMING_SNAKE_CASE_ ).to(sample.device )
A: Optional[Any] = sigma + gamma * sigma
A: List[Any] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[KarrasVeOutput, Tuple]:
'''simple docstring'''
A: Union[str, Any] = sample_hat + sigma_hat * model_output
A: str = (sample_hat - pred_original_sample) / sigma_hat
A: Optional[int] = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=SCREAMING_SNAKE_CASE_ , derivative=SCREAMING_SNAKE_CASE_ , pred_original_sample=SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[KarrasVeOutput, Tuple]:
'''simple docstring'''
A: int = sample_prev + sigma_prev * model_output
A: List[Any] = (sample_prev - pred_original_sample) / sigma_prev
A: Dict = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=SCREAMING_SNAKE_CASE_ , derivative=SCREAMING_SNAKE_CASE_ , pred_original_sample=SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str ) -> Dict:
'''simple docstring'''
raise NotImplementedError()
| 319 | 1 |
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 a__ ( unittest.TestCase ):
def __init__( self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , ) -> Any:
'''simple docstring'''
a = size if size is not None else {"height": 18, "width": 18}
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 ) -> Dict:
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class a__ ( UpperCamelCase__ , unittest.TestCase ):
a : Dict = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''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 ) -> Union[str, Any]:
'''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": 18, "width": 18} )
a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
pass
def lowerCAmelCase_ ( self ) -> List[str]:
'''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=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 ) -> int:
'''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=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 ) -> Optional[Any]:
'''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=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 ) -> Optional[Any]:
'''simple docstring'''
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, 224, 224) )
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 = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # 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, 224, 224) )
| 351 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class a__ ( UpperCamelCase__ , unittest.TestCase ):
a : List[Any] = RoFormerTokenizer
a : Tuple = RoFormerTokenizerFast
a : Dict = True
a : Optional[Any] = True
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
def lowerCAmelCase_ ( self , **A ) -> Tuple:
'''simple docstring'''
return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **A )
def lowerCAmelCase_ ( self , **A ) -> Union[str, Any]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **A )
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
a = "永和服装饰品有限公司,今天天气非常好"
a = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
return input_text, output_text
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
a = self.get_tokenizer()
a , a = self.get_chinese_input_output_texts()
a = tokenizer.tokenize(A )
self.assertListEqual(A , output_text.split() )
a = tokens + [tokenizer.unk_token]
a = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
a = self.get_rust_tokenizer()
a , a = self.get_chinese_input_output_texts()
a = tokenizer.tokenize(A )
self.assertListEqual(A , output_text.split() )
a = tokens + [tokenizer.unk_token]
a = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
pass
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
| 180 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : List[str] = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Optional[Any] = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
lowerCAmelCase : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 13 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
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
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
UpperCAmelCase_ = get_tests_dir('fixtures')
UpperCAmelCase_ = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
UpperCAmelCase_ = get_tests_dir('fixtures/dummy-config.json')
class lowerCamelCase__( unittest.TestCase):
def lowerCAmelCase__ ( self: List[str] ):
__lowerCamelCase = 0
def lowerCAmelCase__ ( self: List[str] ):
__lowerCamelCase = AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: Union[str, Any] ):
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCamelCase = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(UpperCamelCase_ ).to_dict()
config_dict.pop("""feature_extractor_type""" )
__lowerCamelCase = WavaVecaFeatureExtractor(**UpperCamelCase_ )
# save in new folder
model_config.save_pretrained(UpperCamelCase_ )
config.save_pretrained(UpperCamelCase_ )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(UpperCamelCase_ )
# make sure private variable is not incorrectly saved
__lowerCamelCase = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: Tuple ):
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: int ):
with self.assertRaisesRegex(
UpperCamelCase_ , """bert-base is not a local folder and is not a valid model identifier""" ):
__lowerCamelCase = AutoFeatureExtractor.from_pretrained("""bert-base""" )
def lowerCAmelCase__ ( self: Tuple ):
with self.assertRaisesRegex(
UpperCamelCase_ , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(UpperCamelCase_ , revision="""aaaaaa""" )
def lowerCAmelCase__ ( self: Optional[Any] ):
with self.assertRaisesRegex(
UpperCamelCase_ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
__lowerCamelCase = AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model""" )
def lowerCAmelCase__ ( self: Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(UpperCamelCase_ ):
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(UpperCamelCase_ ):
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=UpperCamelCase_ )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=UpperCamelCase_ )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(UpperCamelCase_ )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(UpperCamelCase_ , trust_remote_code=UpperCamelCase_ )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
def lowerCAmelCase__ ( self: Any ):
try:
AutoConfig.register("""custom""" , UpperCamelCase_ )
AutoFeatureExtractor.register(UpperCamelCase_ , UpperCamelCase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCamelCase_ ):
AutoFeatureExtractor.register(UpperCamelCase_ , UpperCamelCase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
__lowerCamelCase = CustomFeatureExtractor.from_pretrained(UpperCamelCase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(UpperCamelCase_ )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def lowerCAmelCase__ ( self: Dict ):
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : str = True
try:
AutoConfig.register("""custom""" , UpperCamelCase_ )
AutoFeatureExtractor.register(UpperCamelCase_ , UpperCamelCase_ )
# If remote code is not set, the default is to use local
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=UpperCamelCase_ )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
__lowerCamelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=UpperCamelCase_ )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(not hasattr(UpperCamelCase_ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 12 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : torch.FloatTensor
_snake_case : torch.FloatTensor
_snake_case : Optional[torch.FloatTensor] = None
class lowerCamelCase (_snake_case , _snake_case ):
'''simple docstring'''
_snake_case : str = 2
@register_to_config
def __init__( self , _UpperCamelCase = 0.02 , _UpperCamelCase = 1_0_0 , _UpperCamelCase = 1.0_07 , _UpperCamelCase = 8_0 , _UpperCamelCase = 0.05 , _UpperCamelCase = 5_0 , ) -> Dict:
# standard deviation of the initial noise distribution
UpperCAmelCase_ : Optional[int] = sigma_max
# setable values
UpperCAmelCase_ : int = None
UpperCAmelCase_ : np.IntTensor = None
UpperCAmelCase_ : torch.FloatTensor = None # sigma(t_i)
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase = None ) -> torch.FloatTensor:
return sample
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase = None ) -> str:
UpperCAmelCase_ : Tuple = num_inference_steps
UpperCAmelCase_ : Optional[int] = np.arange(0 , self.num_inference_steps )[::-1].copy()
UpperCAmelCase_ : Union[str, Any] = torch.from_numpy(_UpperCamelCase ).to(_UpperCamelCase )
UpperCAmelCase_ : str = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
UpperCAmelCase_ : Optional[Any] = torch.tensor(_UpperCamelCase , dtype=torch.floataa , device=_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None ) -> Tuple[torch.FloatTensor, float]:
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase_ : str = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
UpperCAmelCase_ : Any = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase_ : int = self.config.s_noise * randn_tensor(sample.shape , generator=_UpperCamelCase ).to(sample.device )
UpperCAmelCase_ : Optional[Any] = sigma + gamma * sigma
UpperCAmelCase_ : List[Any] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = True , ) -> Union[KarrasVeOutput, Tuple]:
UpperCAmelCase_ : int = sample_hat + sigma_hat * model_output
UpperCAmelCase_ : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase_ : List[Any] = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=_UpperCamelCase , derivative=_UpperCamelCase , pred_original_sample=_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = True , ) -> Union[KarrasVeOutput, Tuple]:
UpperCAmelCase_ : int = sample_prev + sigma_prev * model_output
UpperCAmelCase_ : Union[str, Any] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase_ : List[Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=_UpperCamelCase , derivative=_UpperCamelCase , pred_original_sample=_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
raise NotImplementedError()
| 145 |
from collections import defaultdict
from math import ceil, sqrt
def lowercase__ ( __snake_case : int = 1_000_000 , __snake_case : int = 10 ):
'''simple docstring'''
UpperCAmelCase_ : defaultdict = defaultdict(__snake_case )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
UpperCAmelCase_ : Union[str, Any] = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
UpperCAmelCase_ : int = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(__snake_case , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(F'{solution() = }')
| 145 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 snake_case ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
snake_case__ = KandinskyVaaPipeline
snake_case__ = [
"image_embeds",
"negative_image_embeds",
]
snake_case__ = ["image_embeds", "negative_image_embeds"]
snake_case__ = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
snake_case__ = False
@property
def __lowerCAmelCase ( self : int ):
return 32
@property
def __lowerCAmelCase ( self : Optional[int] ):
return 32
@property
def __lowerCAmelCase ( self : Tuple ):
return self.time_input_dim
@property
def __lowerCAmelCase ( self : Dict ):
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self : Union[str, Any] ):
return 100
@property
def __lowerCAmelCase ( self : Union[str, Any] ):
torch.manual_seed(0 )
UpperCAmelCase__ = {
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': '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': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
UpperCAmelCase__ = UNetaDConditionModel(**lowerCamelCase__ )
return model
@property
def __lowerCAmelCase ( self : Any ):
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 __lowerCAmelCase ( self : Dict ):
torch.manual_seed(0 )
UpperCAmelCase__ = VQModel(**self.dummy_movq_kwargs )
return model
def __lowerCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase__ = self.dummy_unet
UpperCAmelCase__ = self.dummy_movq
UpperCAmelCase__ = DDIMScheduler(
num_train_timesteps=1_000 ,beta_schedule='linear' ,beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,steps_offset=1 ,prediction_type='epsilon' ,thresholding=lowerCamelCase__ ,)
UpperCAmelCase__ = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __lowerCAmelCase ( self : Optional[int] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Dict=0 ):
UpperCAmelCase__ = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
UpperCAmelCase__ = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
lowerCamelCase__ )
if str(lowerCamelCase__ ).startswith('mps' ):
UpperCAmelCase__ = torch.manual_seed(lowerCamelCase__ )
else:
UpperCAmelCase__ = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
UpperCAmelCase__ = {
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def __lowerCAmelCase ( self : List[Any] ):
UpperCAmelCase__ = 'cpu'
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = self.pipeline_class(**lowerCamelCase__ )
UpperCAmelCase__ = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCAmelCase__ = pipe(**self.get_dummy_inputs(lowerCamelCase__ ) )
UpperCAmelCase__ = output.images
UpperCAmelCase__ = pipe(
**self.get_dummy_inputs(lowerCamelCase__ ) ,return_dict=lowerCamelCase__ ,)[0]
UpperCAmelCase__ = image[0, -3:, -3:, -1]
UpperCAmelCase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase__ = np.array(
[0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6] )
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()}'''
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy' )
UpperCAmelCase__ = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' ,torch_dtype=torch.floataa )
pipe_prior.to(lowerCamelCase__ )
UpperCAmelCase__ = KandinskyVaaPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder' ,torch_dtype=torch.floataa )
UpperCAmelCase__ = pipeline.to(lowerCamelCase__ )
pipeline.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCAmelCase__ = 'red cat, 4k photo'
UpperCAmelCase__ = torch.Generator(device='cuda' ).manual_seed(0 )
UpperCAmelCase__ , UpperCAmelCase__ = pipe_prior(
lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=5 ,negative_prompt='' ,).to_tuple()
UpperCAmelCase__ = torch.Generator(device='cuda' ).manual_seed(0 )
UpperCAmelCase__ = pipeline(
image_embeds=lowerCamelCase__ ,negative_image_embeds=lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=100 ,output_type='np' ,)
UpperCAmelCase__ = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )
| 98 |
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ , lowercase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowercase= [False] * len(lowercase__ )
__lowercase= []
queue.append(lowercase__ )
__lowercase= True
while queue:
__lowercase= queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowercase__ )
__lowercase= True
__lowercase= u
return visited[t]
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> int:
'''simple docstring'''
__lowercase= [-1] * (len(lowercase__ ))
__lowercase= 0
while bfs(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
__lowercase= float('Inf' )
__lowercase= sink
while s != source:
# Find the minimum value in select path
__lowercase= min(lowercase__ , graph[parent[s]][s] )
__lowercase= parent[s]
max_flow += path_flow
__lowercase= sink
while v != source:
__lowercase= parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
__lowercase= parent[v]
return max_flow
lowerCAmelCase = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
lowerCAmelCase ,lowerCAmelCase = 0, 5
print(ford_fulkerson(graph, source, sink))
| 295 | 0 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCamelCase_ ( __magic_name__ ):
@require_torch
def _lowercase( self ) -> List[str]:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCAmelCase : List[Any] = """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
"""
UpperCAmelCase : Tuple = """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
"""
UpperCAmelCase : List[Any] = """
import socket
def offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")
socket.socket = offline_socket
"""
# Force fetching the files so that we can use the cache
UpperCAmelCase : Optional[int] = """hf-internal-testing/tiny-random-bert"""
BertConfig.from_pretrained(A )
BertModel.from_pretrained(A )
BertTokenizer.from_pretrained(A )
pipeline(task="""fill-mask""" , model=A )
# baseline - just load from_pretrained with normal network
UpperCAmelCase : Optional[Any] = [sys.executable, """-c""", """\n""".join([load, run, mock] )]
# should succeed
UpperCAmelCase : int = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase : List[Any] = """1"""
UpperCAmelCase : int = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def _lowercase( self ) -> Optional[Any]:
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCAmelCase : Optional[Any] = """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
"""
UpperCAmelCase : List[Any] = """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
"""
UpperCAmelCase : Tuple = """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")
socket.socket = offline_socket
"""
# Force fetching the files so that we can use the cache
UpperCAmelCase : Optional[Any] = """hf-internal-testing/tiny-random-bert"""
BertConfig.from_pretrained(A )
BertModel.from_pretrained(A )
BertTokenizer.from_pretrained(A )
pipeline(task="""fill-mask""" , model=A )
# baseline - just load from_pretrained with normal network
UpperCAmelCase : Any = [sys.executable, """-c""", """\n""".join([load, run, mock] )]
# should succeed
UpperCAmelCase : List[str] = self.get_env()
UpperCAmelCase : Optional[Any] = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def _lowercase( self ) -> str:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCAmelCase : int = """
from transformers import BertConfig, BertModel, BertTokenizer
"""
UpperCAmelCase : Tuple = """
mname = \"hf-internal-testing/tiny-random-bert-sharded\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print(\"success\")
"""
UpperCAmelCase : Dict = """
import socket
def offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")
socket.socket = offline_socket
"""
# baseline - just load from_pretrained with normal network
UpperCAmelCase : Tuple = [sys.executable, """-c""", """\n""".join([load, run] )]
# should succeed
UpperCAmelCase : Union[str, Any] = self.get_env()
UpperCAmelCase : Tuple = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
# next emulate no network
UpperCAmelCase : Optional[Any] = [sys.executable, """-c""", """\n""".join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase : Tuple = """1"""
UpperCAmelCase : Tuple = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def _lowercase( self ) -> Tuple:
UpperCAmelCase : List[Any] = """
from transformers import pipeline
"""
UpperCAmelCase : Optional[Any] = """
mname = \"hf-internal-testing/tiny-random-bert\"
pipe = pipeline(model=mname)
"""
UpperCAmelCase : Optional[Any] = """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")
socket.socket = offline_socket
"""
UpperCAmelCase : List[str] = self.get_env()
UpperCAmelCase : List[Any] = """1"""
UpperCAmelCase : int = [sys.executable, """-c""", """\n""".join([load, mock, run] )]
UpperCAmelCase : str = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
"""You cannot infer task automatically within `pipeline` when using offline mode""" , result.stderr.decode().replace("""\n""" , """""" ) , )
@require_torch
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[Any] = """
from transformers import AutoModel
"""
UpperCAmelCase : List[str] = """
mname = \"hf-internal-testing/test_dynamic_model\"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print(\"success\")
"""
# baseline - just load from_pretrained with normal network
UpperCAmelCase : Union[str, Any] = [sys.executable, """-c""", """\n""".join([load, run] )]
# should succeed
UpperCAmelCase : Union[str, Any] = self.get_env()
UpperCAmelCase : Optional[Any] = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase : Dict = """1"""
UpperCAmelCase : List[Any] = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case_ = inspect.getfile(accelerate.test_utils )
snake_case_ = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
snake_case_ = ["""accelerate""", """launch"""]
snake_case_ = Path.home() / """.cache/huggingface/accelerate"""
snake_case_ = """default_config.yaml"""
snake_case_ = config_folder / config_file
snake_case_ = config_folder / """_default_config.yaml"""
snake_case_ = Path('''tests/test_configs''' )
@classmethod
def lowercase_ ( cls ) -> Tuple:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowercase_ ( cls ) -> Union[str, Any]:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('**/*.yaml' ) ):
with self.subTest(config_file=_A ):
execute_subprocess_async(
self.base_cmd + ['--config_file', str(_A ), self.test_file_path] , env=os.environ.copy() )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['accelerate', 'test'] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case_ = """test-tpu"""
snake_case_ = """us-central1-a"""
snake_case_ = """ls"""
snake_case_ = ["""accelerate""", """tpu-config"""]
snake_case_ = """cd /usr/share"""
snake_case_ = """tests/test_samples/test_command_file.sh"""
snake_case_ = """Running gcloud compute tpus tpu-vm ssh"""
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd
+ ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug'] , return_stdout=_A , )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , _A , )
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/0_12_0.yaml',
'--command',
self.command,
'--tpu_zone',
self.tpu_zone,
'--tpu_name',
self.tpu_name,
'--debug',
] , return_stdout=_A , )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , _A , )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug'] , return_stdout=_A )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , _A , )
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug'] , return_stdout=_A , )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , _A , )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/latest.yaml',
'--command',
self.command,
'--command',
'echo \"Hello World\"',
'--debug',
] , return_stdout=_A , )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all""" , _A , )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd
+ ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug'] , return_stdout=_A , )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , _A , )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/0_12_0.yaml',
'--command_file',
self.command_file,
'--tpu_zone',
self.tpu_zone,
'--tpu_name',
self.tpu_name,
'--debug',
] , return_stdout=_A , )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , _A , )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug'] , return_stdout=_A , )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all""" , _A , )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/latest.yaml',
'--install_accelerate',
'--accelerate_version',
'12.0.0',
'--debug',
] , return_stdout=_A , )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all""" , _A , )
| 90 |
"""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):
def __snake_case ( self ) -> str:
'''simple docstring'''
_UpperCAmelCase : Tuple = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
_UpperCAmelCase : List[str] = dict(zip(_A , range(len(_A ) ) ) )
_UpperCAmelCase : List[Any] = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
_UpperCAmelCase : Dict = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 1_60_00,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
_UpperCAmelCase : int = 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
_UpperCAmelCase : List[Any] = """hf-internal-testing/ngram-beam-search-decoder"""
def __snake_case ( self , **_A ) -> Dict:
'''simple docstring'''
_UpperCAmelCase : str = self.add_kwargs_tokens_map.copy()
kwargs.update(_A )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **_A )
def __snake_case ( self , **_A ) -> Tuple:
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **_A )
def __snake_case ( self , **_A ) -> Union[str, Any]:
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **_A )
def __snake_case ( self ) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __snake_case ( self ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = self.get_tokenizer()
_UpperCAmelCase : Optional[Any] = self.get_feature_extractor()
_UpperCAmelCase : List[str] = self.get_decoder()
_UpperCAmelCase : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=_A , feature_extractor=_A , decoder=_A )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase : Any = 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 __snake_case ( self ) -> int:
'''simple docstring'''
_UpperCAmelCase : Union[str, 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
_UpperCAmelCase : Tuple = 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 __snake_case ( self ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : Any = 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 __snake_case ( self ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : str = self.get_feature_extractor()
_UpperCAmelCase : Optional[Any] = self.get_tokenizer()
_UpperCAmelCase : List[Any] = self.get_decoder()
_UpperCAmelCase : Dict = WavaVecaProcessorWithLM(tokenizer=_A , feature_extractor=_A , decoder=_A )
_UpperCAmelCase : List[Any] = floats_list((3, 10_00) )
_UpperCAmelCase : str = feature_extractor(_A , return_tensors="""np""" )
_UpperCAmelCase : int = 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 __snake_case ( self ) -> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = self.get_feature_extractor()
_UpperCAmelCase : Any = self.get_tokenizer()
_UpperCAmelCase : Dict = self.get_decoder()
_UpperCAmelCase : Any = WavaVecaProcessorWithLM(tokenizer=_A , feature_extractor=_A , decoder=_A )
_UpperCAmelCase : Optional[Any] = """This is a test string"""
_UpperCAmelCase : Optional[Any] = processor(text=_A )
_UpperCAmelCase : Dict = tokenizer(_A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __snake_case ( self , _A=(2, 10, 16) , _A=77 ) -> Union[str, Any]:
'''simple docstring'''
np.random.seed(_A )
return np.random.rand(*_A )
def __snake_case ( self ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Tuple = self.get_feature_extractor()
_UpperCAmelCase : Tuple = self.get_tokenizer()
_UpperCAmelCase : Optional[Any] = self.get_decoder()
_UpperCAmelCase : Any = WavaVecaProcessorWithLM(tokenizer=_A , feature_extractor=_A , decoder=_A )
_UpperCAmelCase : str = self._get_dummy_logits(shape=(10, 16) , seed=13 )
_UpperCAmelCase : Optional[int] = processor.decode(_A )
_UpperCAmelCase : str = 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 __snake_case ( self , _A ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Tuple = self.get_feature_extractor()
_UpperCAmelCase : str = self.get_tokenizer()
_UpperCAmelCase : Optional[Any] = self.get_decoder()
_UpperCAmelCase : str = WavaVecaProcessorWithLM(tokenizer=_A , feature_extractor=_A , decoder=_A )
_UpperCAmelCase : Union[str, 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:
_UpperCAmelCase : int = processor.batch_decode(_A )
else:
with get_context(_A ).Pool() as pool:
_UpperCAmelCase : Dict = processor.batch_decode(_A , _A )
_UpperCAmelCase : Tuple = list(_A )
with get_context("""fork""" ).Pool() as p:
_UpperCAmelCase : Tuple = decoder.decode_beams_batch(_A , _A )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[Any] = [], [], []
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 __snake_case ( self ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = self.get_feature_extractor()
_UpperCAmelCase : int = self.get_tokenizer()
_UpperCAmelCase : Tuple = self.get_decoder()
_UpperCAmelCase : Optional[int] = WavaVecaProcessorWithLM(tokenizer=_A , feature_extractor=_A , decoder=_A )
_UpperCAmelCase : Optional[int] = self._get_dummy_logits()
_UpperCAmelCase : List[str] = 15
_UpperCAmelCase : Dict = -20.0
_UpperCAmelCase : List[str] = -4.0
_UpperCAmelCase : Any = processor.batch_decode(
_A , beam_width=_A , beam_prune_logp=_A , token_min_logp=_A , )
_UpperCAmelCase : Any = decoded_processor_out.text
_UpperCAmelCase : Any = list(_A )
with get_context("""fork""" ).Pool() as pool:
_UpperCAmelCase : str = decoder.decode_beams_batch(
_A , _A , beam_width=_A , beam_prune_logp=_A , token_min_logp=_A , )
_UpperCAmelCase : Optional[Any] = [d[0][0] for d in decoded_decoder_out]
_UpperCAmelCase : List[str] = [d[0][2] for d in decoded_decoder_out]
_UpperCAmelCase : Tuple = [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, -13.9474] , _A , atol=1e-3 ) )
def __snake_case ( self ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : int = self.get_feature_extractor()
_UpperCAmelCase : List[Any] = self.get_tokenizer()
_UpperCAmelCase : Union[str, Any] = self.get_decoder()
_UpperCAmelCase : List[Any] = WavaVecaProcessorWithLM(tokenizer=_A , feature_extractor=_A , decoder=_A )
_UpperCAmelCase : Optional[int] = self._get_dummy_logits()
_UpperCAmelCase : Any = 2.0
_UpperCAmelCase : Union[str, Any] = 5.0
_UpperCAmelCase : List[Any] = -20.0
_UpperCAmelCase : str = True
_UpperCAmelCase : Union[str, Any] = processor.batch_decode(
_A , alpha=_A , beta=_A , unk_score_offset=_A , lm_score_boundary=_A , )
_UpperCAmelCase : Tuple = decoded_processor_out.text
_UpperCAmelCase : Tuple = list(_A )
decoder.reset_params(
alpha=_A , beta=_A , unk_score_offset=_A , lm_score_boundary=_A , )
with get_context("""fork""" ).Pool() as pool:
_UpperCAmelCase : Optional[Any] = decoder.decode_beams_batch(
_A , _A , )
_UpperCAmelCase : Optional[int] = [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 )
_UpperCAmelCase : List[str] = 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 __snake_case ( self ) -> str:
'''simple docstring'''
_UpperCAmelCase : int = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_UpperCAmelCase : Optional[Any] = processor.decoder.model_container[processor.decoder._model_key]
_UpperCAmelCase : Union[str, Any] = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
_UpperCAmelCase : Optional[Any] = os.listdir(_A )
_UpperCAmelCase : Optional[Any] = ["""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 __snake_case ( self ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = snapshot_download("""hf-internal-testing/processor_with_lm""" )
_UpperCAmelCase : Dict = WavaVecaProcessorWithLM.from_pretrained(_A )
_UpperCAmelCase : Any = processor.decoder.model_container[processor.decoder._model_key]
_UpperCAmelCase : Dict = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
_UpperCAmelCase : Optional[Any] = os.listdir(_A )
_UpperCAmelCase : Union[str, Any] = 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 __snake_case ( self ) -> Any:
'''simple docstring'''
_UpperCAmelCase : Dict = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_UpperCAmelCase : str = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_UpperCAmelCase : List[Any] = floats_list((3, 10_00) )
_UpperCAmelCase : str = processor_wavaveca(_A , return_tensors="""np""" )
_UpperCAmelCase : Any = 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 )
_UpperCAmelCase : Union[str, Any] = self._get_dummy_logits()
_UpperCAmelCase : Dict = processor_wavaveca.batch_decode(_A )
_UpperCAmelCase : Optional[Any] = processor_auto.batch_decode(_A )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def __snake_case ( self ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = self.get_feature_extractor()
_UpperCAmelCase : Optional[int] = self.get_tokenizer()
_UpperCAmelCase : Union[str, Any] = self.get_decoder()
_UpperCAmelCase : List[Any] = 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 __snake_case ( _A , _A ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Any = [d[key] for d in offsets]
return retrieved_list
def __snake_case ( self ) -> str:
'''simple docstring'''
_UpperCAmelCase : List[str] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_UpperCAmelCase : List[Any] = self._get_dummy_logits()[0]
_UpperCAmelCase : Tuple = 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 __snake_case ( self ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_UpperCAmelCase : Optional[Any] = self._get_dummy_logits()
_UpperCAmelCase : List[Any] = 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 __snake_case ( self ) -> str:
'''simple docstring'''
import torch
_UpperCAmelCase : List[str] = load_dataset("""common_voice""" , """en""" , split="""train""" , streaming=_A )
_UpperCAmelCase : List[Any] = ds.cast_column("""audio""" , datasets.Audio(sampling_rate=1_60_00 ) )
_UpperCAmelCase : List[Any] = iter(_A )
_UpperCAmelCase : Optional[Any] = next(_A )
_UpperCAmelCase : Optional[int] = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
_UpperCAmelCase : Dict = 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
_UpperCAmelCase : List[str] = processor(sample["""audio"""]["""array"""] , return_tensors="""pt""" ).input_values
with torch.no_grad():
_UpperCAmelCase : Optional[int] = model(_A ).logits.cpu().numpy()
_UpperCAmelCase : Union[str, Any] = processor.decode(logits[0] , output_word_offsets=_A )
_UpperCAmelCase : str = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
_UpperCAmelCase : Optional[int] = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
_UpperCAmelCase : List[Any] = """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
_UpperCAmelCase : List[str] = torch.tensor(self.get_from_offsets(_A , """start_time""" ) )
_UpperCAmelCase : Any = torch.tensor(self.get_from_offsets(_A , """end_time""" ) )
# fmt: off
_UpperCAmelCase : Optional[Any] = 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] )
_UpperCAmelCase : List[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(_A , _A , atol=0.01 ) )
self.assertTrue(torch.allclose(_A , _A , atol=0.01 ) )
| 246 | 0 |
import itertools
import math
def __snake_case ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __snake_case ( ):
__a = 2
while True:
if is_prime(_UpperCAmelCase ):
yield num
num += 1
def __snake_case ( _UpperCAmelCase = 10001 ):
return next(itertools.islice(prime_generator() , nth - 1 , _UpperCAmelCase ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 351 |
def __snake_case ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('''Input value must be an \'int\' type''' )
__a = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 131 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Tuple = AudioLDMPipeline
__snake_case : Tuple = TEXT_TO_AUDIO_PARAMS
__snake_case : Optional[int] = TEXT_TO_AUDIO_BATCH_PARAMS
__snake_case : Union[str, Any] = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
] )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=(32, 64) ,class_embed_type="""simple_projection""" ,projection_class_embeddings_input_dim=32 ,class_embeddings_concat=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=1 ,out_channels=1 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = ClapTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1e-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1000 ,projection_dim=32 ,)
SCREAMING_SNAKE_CASE = ClapTextModelWithProjection(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" ,model_max_length=77 )
SCREAMING_SNAKE_CASE = SpeechTaHifiGanConfig(
model_in_dim=8 ,sampling_rate=16000 ,upsample_initial_channel=16 ,upsample_rates=[2, 2] ,upsample_kernel_sizes=[4, 4] ,resblock_kernel_sizes=[3, 7] ,resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] ,normalize_before=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = SpeechTaHifiGan(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Optional[Any]=0 ) -> str:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) == 256
SCREAMING_SNAKE_CASE = audio[:10]
SCREAMING_SNAKE_CASE = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 3 * [inputs["""prompt"""]]
# forward
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 3 * [inputs.pop("""prompt""" )]
SCREAMING_SNAKE_CASE = audioldm_pipe.tokenizer(
lowerCamelCase__ ,padding="""max_length""" ,max_length=audioldm_pipe.tokenizer.model_max_length ,truncation=lowerCamelCase__ ,return_tensors="""pt""" ,)
SCREAMING_SNAKE_CASE = text_inputs["""input_ids"""].to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.text_encoder(
lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
SCREAMING_SNAKE_CASE = F.normalize(lowerCamelCase__ ,dim=-1 )
SCREAMING_SNAKE_CASE = prompt_embeds
# forward
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 3 * ["""this is a negative prompt"""]
SCREAMING_SNAKE_CASE = negative_prompt
SCREAMING_SNAKE_CASE = 3 * [inputs["""prompt"""]]
# forward
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 3 * [inputs.pop("""prompt""" )]
SCREAMING_SNAKE_CASE = []
for p in [prompt, negative_prompt]:
SCREAMING_SNAKE_CASE = audioldm_pipe.tokenizer(
lowerCamelCase__ ,padding="""max_length""" ,max_length=audioldm_pipe.tokenizer.model_max_length ,truncation=lowerCamelCase__ ,return_tensors="""pt""" ,)
SCREAMING_SNAKE_CASE = text_inputs["""input_ids"""].to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.text_encoder(
lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
SCREAMING_SNAKE_CASE = F.normalize(lowerCamelCase__ ,dim=-1 )
embeds.append(lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = embeds
# forward
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = PNDMScheduler(skip_prk_steps=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """egg cracking"""
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ ,negative_prompt=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) == 256
SCREAMING_SNAKE_CASE = audio[:10]
SCREAMING_SNAKE_CASE = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = PNDMScheduler(skip_prk_steps=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
SCREAMING_SNAKE_CASE = audioldm_pipe(lowerCamelCase__ ,num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = audioldm_pipe([prompt] * batch_size ,num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = audioldm_pipe(lowerCamelCase__ ,num_inference_steps=2 ,num_waveforms_per_prompt=lowerCamelCase__ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = audioldm_pipe(
[prompt] * batch_size ,num_inference_steps=2 ,num_waveforms_per_prompt=lowerCamelCase__ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.vocoder.config.sampling_rate
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe(audio_length_in_s=0.016 ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) / vocoder_sampling_rate == 0.016
SCREAMING_SNAKE_CASE = audioldm_pipe(audio_length_in_s=0.032 ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) / vocoder_sampling_rate == 0.032
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = ["""hey"""]
SCREAMING_SNAKE_CASE = audioldm_pipe(lowerCamelCase__ ,num_inference_steps=1 )
SCREAMING_SNAKE_CASE = output.audios.shape
assert audio_shape == (1, 256)
SCREAMING_SNAKE_CASE = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
SCREAMING_SNAKE_CASE = SpeechTaHifiGan(lowerCamelCase__ ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe(lowerCamelCase__ ,num_inference_steps=1 )
SCREAMING_SNAKE_CASE = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
self._test_inference_batch_single_identical(test_mean_pixel_difference=lowerCamelCase__ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__ )
@slow
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Any="cpu" ,lowerCamelCase__ : Optional[int]=torch.floataa ,lowerCamelCase__ : List[str]=0 ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = np.random.RandomState(lowerCamelCase__ ).standard_normal((1, 8, 128, 16) )
SCREAMING_SNAKE_CASE = torch.from_numpy(lowerCamelCase__ ).to(device=lowerCamelCase__ ,dtype=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 25
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ ).audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) == 81920
SCREAMING_SNAKE_CASE = audio[77230:77240]
SCREAMING_SNAKE_CASE = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
SCREAMING_SNAKE_CASE = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
SCREAMING_SNAKE_CASE = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ ).audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) == 81920
SCREAMING_SNAKE_CASE = audio[27780:27790]
SCREAMING_SNAKE_CASE = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
SCREAMING_SNAKE_CASE = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 296 |
from collections import defaultdict
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _SCREAMING_SNAKE_CASE , 2 ):
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) > 1:
continue
SCREAMING_SNAKE_CASE = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_SCREAMING_SNAKE_CASE , limit + 1 , _SCREAMING_SNAKE_CASE ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 | 1 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : Tuple , lowercase_ : Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
super().__init__()
snake_case_ = nn.ModuleList(lowercase_ )
def A_ ( self : Any , lowercase_ : torch.FloatTensor , lowercase_ : Union[torch.Tensor, float, int] , lowercase_ : torch.Tensor , lowercase_ : List[torch.tensor] , lowercase_ : List[float] , lowercase_ : Optional[torch.Tensor] = None , lowercase_ : Optional[torch.Tensor] = None , lowercase_ : Optional[torch.Tensor] = None , lowercase_ : Optional[Dict[str, Any]] = None , lowercase_ : bool = False , lowercase_ : bool = True , ):
for i, (image, scale, controlnet) in enumerate(zip(lowercase_ , lowercase_ , self.nets ) ):
snake_case_ ,snake_case_ = controlnet(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , )
# merge samples
if i == 0:
snake_case_ ,snake_case_ = down_samples, mid_sample
else:
snake_case_ = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(lowercase_ , lowercase_ )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def A_ ( self : Any , lowercase_ : Union[str, os.PathLike] , lowercase_ : bool = True , lowercase_ : Callable = None , lowercase_ : bool = False , lowercase_ : Optional[str] = None , ):
snake_case_ = 0
snake_case_ = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
lowercase_ , is_main_process=lowercase_ , save_function=lowercase_ , safe_serialization=lowercase_ , variant=lowercase_ , )
idx += 1
snake_case_ = model_path_to_save + F"_{idx}"
@classmethod
def A_ ( cls : Dict , lowercase_ : Optional[Union[str, os.PathLike]] , **lowercase_ : Any ):
snake_case_ = 0
snake_case_ = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
snake_case_ = pretrained_model_path
while os.path.isdir(lowercase_ ):
snake_case_ = ControlNetModel.from_pretrained(lowercase_ , **lowercase_ )
controlnets.append(lowercase_ )
idx += 1
snake_case_ = pretrained_model_path + F"_{idx}"
logger.info(F"{len(lowercase_ )} controlnets loaded from {pretrained_model_path}." )
if len(lowercase_ ) == 0:
raise ValueError(
F"No ControlNets found under {os.path.dirname(lowercase_ )}. Expected at least {pretrained_model_path + '_0'}." )
return cls(lowercase_ )
| 72 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class a ( _lowerCamelCase , unittest.TestCase ):
snake_case_ = FlaxAutoencoderKL
@property
def A_ ( self : List[Any] ):
snake_case_ = 4
snake_case_ = 3
snake_case_ = (32, 32)
snake_case_ = jax.random.PRNGKey(0 )
snake_case_ = jax.random.uniform(lowercase_ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def A_ ( self : Tuple ):
snake_case_ = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
| 72 | 1 |
from collections import defaultdict
from math import gcd
def __lowerCAmelCase ( a__ = 150_0000 ) -> int:
__a = defaultdict(a__ )
__a = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , a__ , 2 ):
if gcd(a__ , a__ ) > 1:
continue
__a = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(a__ , limit + 1 , a__ ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F"{solution() = }") | 6 |
import unittest
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 MobileNetVaImageProcessor
class __lowerCAmelCase ( unittest.TestCase):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=7 , lowerCAmelCase__=3 , lowerCAmelCase__=1_8 , lowerCAmelCase__=3_0 , lowerCAmelCase__=4_0_0 , lowerCAmelCase__=True , lowerCAmelCase__=None , lowerCAmelCase__=True , lowerCAmelCase__=None , ) -> Optional[int]:
'''simple docstring'''
a__ : str =size if size is not None else {"shortest_edge": 2_0}
a__ : Union[str, Any] =crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
a__ : Tuple =parent
a__ : Optional[int] =batch_size
a__ : Any =num_channels
a__ : List[str] =image_size
a__ : Dict =min_resolution
a__ : List[Any] =max_resolution
a__ : Dict =do_resize
a__ : Union[str, Any] =size
a__ : str =do_center_crop
a__ : List[str] =crop_size
def _lowercase ( self ) -> str:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __lowerCAmelCase ( UpperCamelCase__ , unittest.TestCase):
_lowercase : Optional[Any] = MobileNetVaImageProcessor if is_vision_available() else None
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
a__ : Optional[int] =MobileNetVaImageProcessingTester(self )
@property
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _lowercase ( self ) -> Any:
'''simple docstring'''
a__ : List[str] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "size" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_center_crop" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "crop_size" ) )
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : Any =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 2_0} )
self.assertEqual(image_processor.crop_size , {"height": 1_8, "width": 1_8} )
a__ : Dict =self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {"shortest_edge": 4_2} )
self.assertEqual(image_processor.crop_size , {"height": 8_4, "width": 8_4} )
def _lowercase ( self ) -> Any:
'''simple docstring'''
pass
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
a__ : Dict =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a__ : Optional[Any] =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__ : List[Any] =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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a__ : Dict =image_processing(lowerCAmelCase__ , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _lowercase ( self ) -> int:
'''simple docstring'''
a__ : str =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a__ : str =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__ : List[str] =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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a__ : Union[str, Any] =image_processing(lowerCAmelCase__ , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
a__ : Any =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a__ : int =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__ : Optional[Any] =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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
a__ : str =image_processing(lowerCAmelCase__ , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 95 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : dict ):
'''simple docstring'''
lowerCAmelCase : Any = set()
# edges = list of graph's edges
lowerCAmelCase : Optional[int] = get_edges(SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
lowerCAmelCase , lowerCAmelCase : Any = edges.pop()
chosen_vertices.add(SCREAMING_SNAKE_CASE )
chosen_vertices.add(SCREAMING_SNAKE_CASE )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(SCREAMING_SNAKE_CASE )
return chosen_vertices
def a__ ( SCREAMING_SNAKE_CASE : dict ):
'''simple docstring'''
lowerCAmelCase : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 133 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase__ = logging.getLogger()
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument("-f" )
lowerCAmelCase : Tuple = parser.parse_args()
return args.f
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = {}
lowerCAmelCase : List[str] = os.path.join(SCREAMING_SNAKE_CASE , "all_results.json" )
if os.path.exists(SCREAMING_SNAKE_CASE ):
with open(SCREAMING_SNAKE_CASE , "r" ) as f:
lowerCAmelCase : str = json.load(SCREAMING_SNAKE_CASE )
else:
raise ValueError(f"""can't find {path}""" )
return results
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Tuple = torch.cuda.is_available() and torch_device == "cuda"
return is_using_cuda and is_apex_available()
lowerCAmelCase__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCAmelCase : Any = tempfile.mkdtemp()
lowerCAmelCase : List[Any] = os.path.join(cls.tmpdir , "default_config.yml" )
write_basic_config(save_location=cls.configPath )
lowerCAmelCase : Optional[int] = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
lowerCAmelCase : int = f"""
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
lowerCAmelCase : Dict = get_results(snake_case__ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "glue_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[Any] = self.get_auto_remove_tmp_dir()
lowerCAmelCase : Tuple = f"""
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
lowerCAmelCase : List[str] = get_results(snake_case__ )
self.assertLess(result["perplexity"] , 100 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "clm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Union[str, Any] = self.get_auto_remove_tmp_dir()
lowerCAmelCase : Tuple = f"""
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
lowerCAmelCase : Any = get_results(snake_case__ )
self.assertLess(result["perplexity"] , 42 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "mlm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = 7 if get_gpu_count() > 1 else 2
lowerCAmelCase : Optional[Any] = self.get_auto_remove_tmp_dir()
lowerCAmelCase : int = f"""
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
lowerCAmelCase : str = get_results(snake_case__ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertLess(result["train_loss"] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "ner_no_trainer" ) ) )
@unittest.skip(reason="Fix me @muellerzr" )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
lowerCAmelCase : List[str] = f"""
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
lowerCAmelCase : Optional[Any] = get_results(snake_case__ )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result["eval_f1"] , 28 )
self.assertGreaterEqual(result["eval_exact"] , 28 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "qa_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[Any] = self.get_auto_remove_tmp_dir()
lowerCAmelCase : List[Any] = f"""
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
""".split()
run_command(self._launch_args + testargs )
lowerCAmelCase : Tuple = get_results(snake_case__ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "swag_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : str = self.get_auto_remove_tmp_dir()
lowerCAmelCase : Tuple = f"""
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
lowerCAmelCase : str = get_results(snake_case__ )
self.assertGreaterEqual(result["eval_rouge1"] , 10 )
self.assertGreaterEqual(result["eval_rouge2"] , 2 )
self.assertGreaterEqual(result["eval_rougeL"] , 7 )
self.assertGreaterEqual(result["eval_rougeLsum"] , 7 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "summarization_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Tuple = self.get_auto_remove_tmp_dir()
lowerCAmelCase : Tuple = f"""
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
lowerCAmelCase : int = get_results(snake_case__ )
self.assertGreaterEqual(result["eval_bleu"] , 30 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "translation_no_trainer" ) ) )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = logging.StreamHandler(sys.stdout )
logger.addHandler(snake_case__ )
lowerCAmelCase : Optional[Any] = self.get_auto_remove_tmp_dir()
lowerCAmelCase : Any = f"""
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
""".split()
run_command(self._launch_args + testargs )
lowerCAmelCase : int = get_results(snake_case__ )
self.assertGreaterEqual(result["eval_overall_accuracy"] , 0.10 )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : str = self.get_auto_remove_tmp_dir()
lowerCAmelCase : Union[str, Any] = f"""
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
lowerCAmelCase : Dict = get_results(snake_case__ )
# The base model scores a 25%
self.assertGreaterEqual(result["eval_accuracy"] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "step_1" ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case__ , "image_classification_no_trainer" ) ) )
| 133 | 1 |
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 SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase : Optional[int] = 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." )
lowerCAmelCase : str = 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." )
lowerCAmelCase : int = components[:-1] + [test_fn.replace('.py', '' )]
lowerCAmelCase : Optional[Any] = '.'.join(a__ )
return test_module_path
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[str]:
'''simple docstring'''
lowerCAmelCase : Optional[Any] = get_module_path(a__ )
lowerCAmelCase : int = importlib.import_module(a__ )
return test_module
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[str]:
'''simple docstring'''
lowerCAmelCase : Tuple = []
lowerCAmelCase : Optional[Any] = get_test_module(a__ )
for attr in dir(a__ ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(a__, a__ ) )
# sort with class names
return sorted(a__, key=lambda _UpperCAmelCase : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase : str = []
lowerCAmelCase : Optional[int] = get_test_module(a__ )
for attr in dir(a__ ):
lowerCAmelCase : Optional[int] = getattr(a__, a__ )
# (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).
lowerCAmelCase : Any = getattr(a__, 'all_model_classes', [] )
if len(a__ ) > 0:
test_classes.append(a__ )
# sort with class names
return sorted(a__, key=lambda _UpperCAmelCase : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase : Any = get_test_classes(a__ )
lowerCAmelCase : List[str] = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(a__, key=lambda _UpperCAmelCase : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase : int = test_class()
if hasattr(a__, 'setUp' ):
test.setUp()
lowerCAmelCase : Tuple = None
if hasattr(a__, 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
lowerCAmelCase : Optional[Any] = test.model_tester.__class__
return model_tester
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase : int = get_test_classes(a__ )
lowerCAmelCase : Union[str, Any] = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(a__ )
# sort with class names
return sorted(a__, key=lambda _UpperCAmelCase : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase : Dict = get_test_classes_for_model(a__, a__ )
lowerCAmelCase : Tuple = []
for test_class in test_classes:
lowerCAmelCase : Optional[Any] = get_model_tester_from_test_class(a__ )
if tester_class is not None:
tester_classes.append(a__ )
# sort with class names
return sorted(a__, key=lambda _UpperCAmelCase : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase : List[str] = get_test_classes(a__ )
lowerCAmelCase : str = {test_class: get_model_tester_from_test_class(a__ ) for test_class in test_classes}
return test_tester_mapping
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[str]:
'''simple docstring'''
lowerCAmelCase : Dict = get_model_classes(a__ )
lowerCAmelCase : Tuple = {
model_class: get_test_classes_for_model(a__, a__ ) for model_class in model_classes
}
return model_test_mapping
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase : int = get_model_classes(a__ )
lowerCAmelCase : Optional[int] = {
model_class: get_tester_classes_for_model(a__, a__ ) for model_class in model_classes
}
return model_to_tester_mapping
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
if isinstance(a__, a__ ):
return o
elif isinstance(a__, a__ ):
return o.__name__
elif isinstance(a__, (list, tuple) ):
return [to_json(a__ ) for x in o]
elif isinstance(a__, a__ ):
return {to_json(a__ ): to_json(a__ ) for k, v in o.items()}
else:
return o
| 138 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A( a ):
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '''ChineseCLIPImageProcessor'''
snake_case_ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , _snake_case=None , _snake_case=None , **_snake_case ) -> Tuple:
'''simple docstring'''
__a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _snake_case , )
__a = kwargs.pop('''feature_extractor''' )
__a = 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__(_snake_case , _snake_case )
__a = self.image_processor
def __call__( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
__a = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
if images is not None:
__a = self.image_processor(_snake_case , return_tensors=_snake_case , **_snake_case )
if text is not None and images is not None:
__a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_snake_case ) , tensor_type=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> str:
'''simple docstring'''
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> Dict:
'''simple docstring'''
return self.tokenizer.decode(*_snake_case , **_snake_case )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.tokenizer.model_input_names
__a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _snake_case , )
return self.image_processor_class | 6 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''yjernite/retribert-base-uncased''': 512,
}
__snake_case = {
'''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[Any] = VOCAB_FILES_NAMES
A_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
A_ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ : Dict = PRETRAINED_INIT_CONFIGURATION
A_ : Optional[int] = RetriBertTokenizer
A_ : int = ['input_ids', 'attention_mask']
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase="[UNK]" , __UpperCAmelCase="[SEP]" , __UpperCAmelCase="[PAD]" , __UpperCAmelCase="[CLS]" , __UpperCAmelCase="[MASK]" , __UpperCAmelCase=True , __UpperCAmelCase=None , **__UpperCAmelCase , ) -> List[Any]:
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
_a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
_a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
_a = do_lower_case
_a = strip_accents
_a = tokenize_chinese_chars
_a = normalizer_class(**__UpperCAmelCase )
_a = do_lower_case
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ) -> Optional[Any]:
_a = [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 _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ) -> List[int]:
_a = [self.sep_token_id]
_a = [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 _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ) -> Tuple[str]:
_a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase ) | 153 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class __lowerCamelCase :
'''simple docstring'''
@staticmethod
def _UpperCAmelCase ( *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
pass
def A_ ( _lowerCAmelCase : Image ):
"""simple docstring"""
_a = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
A_ : List[Any] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> str:
_a = DepthEstimationPipeline(model=__UpperCAmelCase , image_processor=__UpperCAmelCase )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Dict:
_a = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , __UpperCAmelCase )
import datasets
_a = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
_a = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , __UpperCAmelCase , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def _UpperCAmelCase ( self ) -> Tuple:
pass
@slow
@require_torch
def _UpperCAmelCase ( self ) -> List[str]:
_a = '''Intel/dpt-large'''
_a = pipeline('''depth-estimation''' , model=__UpperCAmelCase )
_a = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
_a = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.662 )
@require_torch
def _UpperCAmelCase ( self ) -> List[Any]:
# This is highly irregular to have no small tests.
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' ) | 153 | 1 |
from scipy.stats import pearsonr
import datasets
__lowerCamelCase : List[Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""
__lowerCamelCase : Optional[int] = """
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results['pearsonr'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
['p-value', 'pearsonr']
>>> print(round(results['pearsonr'], 2))
-0.74
>>> print(round(results['p-value'], 2))
0.15
"""
__lowerCamelCase : str = """
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def __UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
} ),reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"],)
def __UpperCamelCase ( self : List[str],_A : Optional[int],_A : Optional[Any],_A : Any=False ):
"""simple docstring"""
if return_pvalue:
SCREAMING_SNAKE_CASE_ : Tuple = pearsonr(__lowerCAmelCase,__lowerCAmelCase )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(__lowerCAmelCase,__lowerCAmelCase )[0] )}
| 18 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] =logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] ={
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class _A ( lowerCAmelCase ):
snake_case__ : Union[str, Any] = 'open-llama'
def __init__( self , __lowerCAmelCase=10_0000 , __lowerCAmelCase=4096 , __lowerCAmelCase=1_1008 , __lowerCAmelCase=32 , __lowerCAmelCase=32 , __lowerCAmelCase="silu" , __lowerCAmelCase=2048 , __lowerCAmelCase=0.0_2 , __lowerCAmelCase=1E-6 , __lowerCAmelCase=True , __lowerCAmelCase=0 , __lowerCAmelCase=1 , __lowerCAmelCase=2 , __lowerCAmelCase=False , __lowerCAmelCase=True , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=None , **__lowerCAmelCase , ):
"""simple docstring"""
lowercase = vocab_size
lowercase = max_position_embeddings
lowercase = hidden_size
lowercase = intermediate_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = initializer_range
lowercase = rms_norm_eps
lowercase = use_cache
lowercase = kwargs.pop(
"""use_memorry_efficient_attention""" , __lowerCAmelCase )
lowercase = hidden_dropout_prob
lowercase = attention_dropout_prob
lowercase = use_stable_embedding
lowercase = shared_input_output_embedding
lowercase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , tie_word_embeddings=__lowerCAmelCase , **__lowerCAmelCase , )
def A__ ( self ):
"""simple docstring"""
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}' )
lowercase = self.rope_scaling.get("""type""" , __lowerCAmelCase )
lowercase = 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}' )
| 197 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class __A ( _SCREAMING_SNAKE_CASE, unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = MvpTokenizer
__lowerCAmelCase = MvpTokenizerFast
__lowerCAmelCase = True
__lowerCAmelCase = filter_roberta_detectors
def SCREAMING_SNAKE_CASE ( self ) -> Any:
super().setUp()
a =[
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
a =dict(zip(__A , range(len(__A ) ) ) )
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(__A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__A ) )
def SCREAMING_SNAKE_CASE ( self , **__A ) -> Optional[Any]:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__A )
def SCREAMING_SNAKE_CASE ( self , **__A ) -> Any:
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__A )
def SCREAMING_SNAKE_CASE ( self , __A ) -> int:
return "lower newer", "lower newer"
@cached_property
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
return MvpTokenizer.from_pretrained('''RUCAIBox/mvp''' )
@cached_property
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp''' )
@require_torch
def SCREAMING_SNAKE_CASE ( self ) -> Any:
a =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
a =[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
a =tokenizer(__A , max_length=len(__A ) , padding=__A , return_tensors='''pt''' )
self.assertIsInstance(__A , __A )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
a =batch.input_ids.tolist()[0]
self.assertListEqual(__A , __A )
# Test that special tokens are reset
@require_torch
def SCREAMING_SNAKE_CASE ( self ) -> Any:
a =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
a =tokenizer(__A , padding=__A , return_tensors='''pt''' )
# check if input_ids are returned and no labels
self.assertIn('''input_ids''' , __A )
self.assertIn('''attention_mask''' , __A )
self.assertNotIn('''labels''' , __A )
self.assertNotIn('''decoder_attention_mask''' , __A )
@require_torch
def SCREAMING_SNAKE_CASE ( self ) -> str:
a =[
'''Summary of the text.''',
'''Another summary.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
a =tokenizer(text_target=__A , max_length=32 , padding='''max_length''' , return_tensors='''pt''' )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
@require_torch
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
a =tokenizer(
['''I am a small frog''' * 1024, '''I am a small frog'''] , padding=__A , truncation=__A , return_tensors='''pt''' )
self.assertIsInstance(__A , __A )
self.assertEqual(batch.input_ids.shape , (2, 1024) )
@require_torch
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
a =['''A long paragraph for summarization.''']
a =[
'''Summary of the text.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
a =tokenizer(__A , text_target=__A , return_tensors='''pt''' )
a =inputs['''input_ids''']
a =inputs['''labels''']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
pass
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
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(__A , **__A )
a =self.tokenizer_class.from_pretrained(__A , **__A )
a ='''A, <mask> AllenNLP sentence.'''
a =tokenizer_r.encode_plus(__A , add_special_tokens=__A , return_token_type_ids=__A )
a =tokenizer_p.encode_plus(__A , add_special_tokens=__A , return_token_type_ids=__A )
# 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'''] ) , )
a =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
a =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(
__A , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
__A , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] ) | 370 |
"""simple docstring"""
import os
import sys
import unittest
lowerCamelCase_ : Tuple = 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_ : Dict = os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
lowerCamelCase_ : Dict = os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class __A ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self ) -> Any:
a =get_test_to_tester_mapping(__A )
a =get_test_to_tester_mapping(__A )
a ={'''BertModelTest''': '''BertModelTester'''}
a ={
'''BlipModelTest''': '''BlipModelTester''',
'''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
'''BlipTextModelTest''': '''BlipTextModelTester''',
'''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
'''BlipVQAModelTest''': '''BlipVQAModelTester''',
'''BlipVisionModelTest''': '''BlipVisionModelTester''',
}
self.assertEqual(get_test_info.to_json(__A ) , __A )
self.assertEqual(get_test_info.to_json(__A ) , __A )
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
a =get_model_to_test_mapping(__A )
a =get_model_to_test_mapping(__A )
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(__A ) , __A )
self.assertEqual(get_test_info.to_json(__A ) , __A )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
a =get_model_to_tester_mapping(__A )
a =get_model_to_tester_mapping(__A )
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(__A ) , __A )
self.assertEqual(get_test_info.to_json(__A ) , __A ) | 215 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : List[str]=3 , lowerCamelCase : Dict=32 , lowerCamelCase : Optional[Any]=3 , lowerCamelCase : Dict=10 , lowerCamelCase : Optional[int]=[10, 20, 30, 40] , lowerCamelCase : Optional[int]=[1, 1, 2, 1] , lowerCamelCase : Optional[int]=True , lowerCamelCase : Optional[Any]=True , lowerCamelCase : str="relu" , lowerCamelCase : Dict=3 , lowerCamelCase : int=None , ) -> Any:
lowerCAmelCase_ : Union[str, Any] = parent
lowerCAmelCase_ : Optional[int] = batch_size
lowerCAmelCase_ : Optional[Any] = image_size
lowerCAmelCase_ : Optional[Any] = num_channels
lowerCAmelCase_ : List[str] = embeddings_size
lowerCAmelCase_ : str = hidden_sizes
lowerCAmelCase_ : Any = depths
lowerCAmelCase_ : Optional[int] = is_training
lowerCAmelCase_ : Union[str, Any] = use_labels
lowerCAmelCase_ : Union[str, Any] = hidden_act
lowerCAmelCase_ : int = num_labels
lowerCAmelCase_ : Optional[Any] = scope
lowerCAmelCase_ : Optional[Any] = len(lowerCamelCase )
def __lowercase ( self : Any ) -> List[str]:
lowerCAmelCase_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : List[Any] = None
if self.use_labels:
lowerCAmelCase_ : Optional[int] = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase_ : Tuple = self.get_config()
return config, pixel_values, labels
def __lowercase ( self : str ) -> List[Any]:
return ResNetConfig(
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 , image_size=self.image_size , )
def __lowercase ( self : List[Any] , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : int ) -> Tuple:
lowerCAmelCase_ : Dict = TFResNetModel(config=lowerCamelCase )
lowerCAmelCase_ : Any = model(lowerCamelCase )
# 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 __lowercase ( self : Any , lowerCamelCase : List[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[Any] ) -> Tuple:
lowerCAmelCase_ : Dict = self.num_labels
lowerCAmelCase_ : Dict = TFResNetForImageClassification(lowerCamelCase )
lowerCAmelCase_ : Optional[int] = model(lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self : Optional[Any] ) -> int:
lowerCAmelCase_ : Dict = self.prepare_config_and_inputs()
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : Dict = config_and_inputs
lowerCAmelCase_ : Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __snake_case ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,unittest.TestCase):
"""simple docstring"""
lowercase = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
lowercase = (
{'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
if is_tf_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def __lowercase ( self : List[Any] ) -> int:
lowerCAmelCase_ : List[Any] = TFResNetModelTester(self )
lowerCAmelCase_ : Dict = ConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase )
def __lowercase ( self : int ) -> Optional[Any]:
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 __lowercase ( self : Dict ) -> List[Any]:
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
def __lowercase ( self : Dict ) -> Tuple:
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
def __lowercase ( self : str ) -> int:
pass
def __lowercase ( self : List[str] ) -> Any:
lowerCAmelCase_, lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[Any] = model_class(lowerCamelCase )
lowerCAmelCase_ : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : Optional[Any] = [*signature.parameters.keys()]
lowerCAmelCase_ : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def __lowercase ( self : List[str] ) -> Dict:
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def __lowercase ( self : Optional[Any] ) -> Dict:
def check_hidden_states_output(lowerCamelCase : Any , lowerCamelCase : str , lowerCamelCase : Union[str, Any] ):
lowerCAmelCase_ : List[str] = model_class(lowerCamelCase )
lowerCAmelCase_ : Optional[Any] = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
lowerCAmelCase_ : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCAmelCase_ : Dict = self.model_tester.num_stages
self.assertEqual(len(lowerCamelCase ) , expected_num_stages + 1 )
# ResNet'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] , )
lowerCAmelCase_, lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ : Optional[int] = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowerCAmelCase_ : Optional[Any] = layer_type
lowerCAmelCase_ : Tuple = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : Any = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def __lowercase ( self : List[str] ) -> str:
lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )
@slow
def __lowercase ( self : Tuple ) -> Optional[Any]:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : str = TFResNetModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __snake_case ( unittest.TestCase):
"""simple docstring"""
@cached_property
def __lowercase ( self : List[str] ) -> Dict:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __lowercase ( self : Optional[Any] ) -> Optional[Any]:
lowerCAmelCase_ : Any = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCAmelCase_ : Any = self.default_image_processor
lowerCAmelCase_ : Union[str, Any] = prepare_img()
lowerCAmelCase_ : List[str] = image_processor(images=lowerCamelCase , return_tensors="""tf""" )
# forward pass
lowerCAmelCase_ : Tuple = model(**lowerCamelCase )
# verify the logits
lowerCAmelCase_ : Optional[int] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
lowerCAmelCase_ : Tuple = tf.constant([-11.1_069, -9.7_877, -8.3_777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowerCamelCase , atol=1E-4 ) )
| 120 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int = 1_00 ):
'''simple docstring'''
lowerCAmelCase_ : int = set()
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : str = n + 1 # maximum limit
for a in range(2 , A__ ):
for b in range(2 , A__ ):
lowerCAmelCase_ : str = a**b # calculates the current power
collect_powers.add(A__ ) # adds the result to the set
return len(A__ )
if __name__ == "__main__":
print("Number of terms ", solution(int(str(input()).strip())))
| 120 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_: Optional[int] =logging.get_logger(__name__)
def lowerCAmelCase_ ( snake_case_ : Tuple , snake_case_ : Union[str, Any] , snake_case_ : Any , snake_case_ : Any ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ = original_name.split("." )[0]
UpperCAmelCase_ = key.split("." )
UpperCAmelCase_ = int(key_list[key_list.index(snake_case_ ) - 2] )
UpperCAmelCase_ = int(key_list[key_list.index(snake_case_ ) - 1] )
UpperCAmelCase_ = orig_block_num - offset
UpperCAmelCase_ = key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" , f"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def lowerCAmelCase_ ( snake_case_ : str ) -> str:
'''simple docstring'''
UpperCAmelCase_ = OrderedDict()
UpperCAmelCase_ , UpperCAmelCase_ = 0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
UpperCAmelCase_ = key.replace("network" , "poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
UpperCAmelCase_ = key[: key.find("proj" )]
UpperCAmelCase_ = key.replace(snake_case_ , f"""patch_embeddings.{total_embed_found}.""" )
UpperCAmelCase_ = key.replace("proj" , "projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
UpperCAmelCase_ = "poolformer.encoder." + key
if "mlp.fc1" in key:
UpperCAmelCase_ = replace_key_with_offset(snake_case_ , snake_case_ , "mlp.fc1" , "output.conv1" )
if "mlp.fc2" in key:
UpperCAmelCase_ = replace_key_with_offset(snake_case_ , snake_case_ , "mlp.fc2" , "output.conv2" )
if "norm1" in key:
UpperCAmelCase_ = replace_key_with_offset(snake_case_ , snake_case_ , "norm1" , "before_norm" )
if "norm2" in key:
UpperCAmelCase_ = replace_key_with_offset(snake_case_ , snake_case_ , "norm2" , "after_norm" )
if "layer_scale_1" in key:
UpperCAmelCase_ = replace_key_with_offset(snake_case_ , snake_case_ , "layer_scale_1" , "layer_scale_1" )
if "layer_scale_2" in key:
UpperCAmelCase_ = replace_key_with_offset(snake_case_ , snake_case_ , "layer_scale_2" , "layer_scale_2" )
if "head" in key:
UpperCAmelCase_ = key.replace("head" , "classifier" )
UpperCAmelCase_ = value
return new_state_dict
def lowerCAmelCase_ ( ) -> Any:
'''simple docstring'''
UpperCAmelCase_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ = Image.open(requests.get(snake_case_ , stream=snake_case_ ).raw )
return image
@torch.no_grad()
def lowerCAmelCase_ ( snake_case_ : Optional[Any] , snake_case_ : Any , snake_case_ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ = PoolFormerConfig()
# set attributes based on model_name
UpperCAmelCase_ = "huggingface/label-files"
UpperCAmelCase_ = model_name[-3:]
UpperCAmelCase_ = 10_00
UpperCAmelCase_ = "imagenet-1k-id2label.json"
UpperCAmelCase_ = (1, 10_00)
# set config attributes
UpperCAmelCase_ = json.load(open(hf_hub_download(snake_case_ , snake_case_ , repo_type="dataset" ) , "r" ) )
UpperCAmelCase_ = {int(snake_case_ ): v for k, v in idalabel.items()}
UpperCAmelCase_ = idalabel
UpperCAmelCase_ = {v: k for k, v in idalabel.items()}
if size == "s12":
UpperCAmelCase_ = [2, 2, 6, 2]
UpperCAmelCase_ = [64, 1_28, 3_20, 5_12]
UpperCAmelCase_ = 4.0
UpperCAmelCase_ = 0.9
elif size == "s24":
UpperCAmelCase_ = [4, 4, 12, 4]
UpperCAmelCase_ = [64, 1_28, 3_20, 5_12]
UpperCAmelCase_ = 4.0
UpperCAmelCase_ = 0.9
elif size == "s36":
UpperCAmelCase_ = [6, 6, 18, 6]
UpperCAmelCase_ = [64, 1_28, 3_20, 5_12]
UpperCAmelCase_ = 4.0
UpperCAmelCase_ = 1E-6
UpperCAmelCase_ = 0.9
elif size == "m36":
UpperCAmelCase_ = [6, 6, 18, 6]
UpperCAmelCase_ = [96, 1_92, 3_84, 7_68]
UpperCAmelCase_ = 4.0
UpperCAmelCase_ = 1E-6
UpperCAmelCase_ = 0.95
elif size == "m48":
UpperCAmelCase_ = [8, 8, 24, 8]
UpperCAmelCase_ = [96, 1_92, 3_84, 7_68]
UpperCAmelCase_ = 4.0
UpperCAmelCase_ = 1E-6
UpperCAmelCase_ = 0.95
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor
UpperCAmelCase_ = PoolFormerImageProcessor(crop_pct=snake_case_ )
# Prepare image
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(images=snake_case_ , return_tensors="pt" ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
UpperCAmelCase_ = torch.load(snake_case_ , map_location=torch.device("cpu" ) )
# rename keys
UpperCAmelCase_ = rename_keys(snake_case_ )
# create HuggingFace model and load state dict
UpperCAmelCase_ = PoolFormerForImageClassification(snake_case_ )
model.load_state_dict(snake_case_ )
model.eval()
# Define image processor
UpperCAmelCase_ = PoolFormerImageProcessor(crop_pct=snake_case_ )
UpperCAmelCase_ = image_processor(images=prepare_img() , return_tensors="pt" ).pixel_values
# forward pass
UpperCAmelCase_ = model(snake_case_ )
UpperCAmelCase_ = outputs.logits
# define expected logit slices for different models
if size == "s12":
UpperCAmelCase_ = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
UpperCAmelCase_ = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
UpperCAmelCase_ = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
UpperCAmelCase_ = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
UpperCAmelCase_ = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , snake_case_ , atol=1E-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
model.save_pretrained(snake_case_ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(snake_case_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_: Optional[int] =argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
SCREAMING_SNAKE_CASE_: Optional[int] =parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 359 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
SCREAMING_SNAKE_CASE_: List[str] =get_logger()
SCREAMING_SNAKE_CASE_: Optional[dict] =None
class __A ( TensorFormatter[Mapping, """jax.Array""", Mapping] ):
def __init__(self : List[Any] , __a : Optional[int]=None , __a : Any=None , **__a : Dict ):
super().__init__(features=__a )
import jax
from jaxlib.xla_client import Device
if isinstance(__a , __a ):
raise ValueError(
f"""Expected {device} to be a `str` not {type(__a )}, as `jaxlib.xla_extension.Device` """
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
UpperCAmelCase_ = device if isinstance(__a , __a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCAmelCase_ = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"""Device with string identifier {self.device} not listed among the available """
f"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
f"""device: {str(jax.devices()[0] )}.""" )
UpperCAmelCase_ = str(jax.devices()[0] )
UpperCAmelCase_ = jnp_array_kwargs
@staticmethod
def _lowercase ():
import jax
return {str(__a ): device for device in jax.devices()}
def _lowercase (self : str , __a : Tuple ):
import jax
import jax.numpy as jnp
if isinstance(__a , __a ) and column:
if all(
isinstance(__a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__a , axis=0 )
return column
def _lowercase (self : Any , __a : Optional[int] ):
import jax
import jax.numpy as jnp
if isinstance(__a , (str, bytes, type(__a )) ):
return value
elif isinstance(__a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
UpperCAmelCase_ = {}
if isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
UpperCAmelCase_ = {"dtype": jnp.intaa}
else:
UpperCAmelCase_ = {"dtype": jnp.intaa}
elif isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
UpperCAmelCase_ = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__a , PIL.Image.Image ):
UpperCAmelCase_ = np.asarray(__a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCAmelCase_ = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(__a , **{**default_dtype, **self.jnp_array_kwargs} )
def _lowercase (self : int , __a : Any ):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__a , "__array__" ) and not isinstance(__a , jax.Array ):
UpperCAmelCase_ = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] )
elif isinstance(__a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] )
return self._tensorize(__a )
def _lowercase (self : Union[str, Any] , __a : dict ):
return map_nested(self._recursive_tensorize , __a , map_list=__a )
def _lowercase (self : str , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_row(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_row(__a )
return self.recursive_tensorize(__a )
def _lowercase (self : Tuple , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_column(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_column(__a , pa_table.column_names[0] )
UpperCAmelCase_ = self.recursive_tensorize(__a )
UpperCAmelCase_ = self._consolidate(__a )
return column
def _lowercase (self : str , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_batch(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_batch(__a )
UpperCAmelCase_ = self.recursive_tensorize(__a )
for column_name in batch:
UpperCAmelCase_ = self._consolidate(batch[column_name] )
return batch
| 106 | 0 |
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 __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=1_3 , __lowerCAmelCase=7 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=False , __lowerCAmelCase=True , __lowerCAmelCase=9_9 , __lowerCAmelCase=3_2 , __lowerCAmelCase=5 , __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 , ):
'''simple docstring'''
lowerCamelCase__ = parent
lowerCamelCase__ = batch_size
lowerCamelCase__ = seq_length
lowerCamelCase__ = is_training
lowerCamelCase__ = use_input_mask
lowerCamelCase__ = use_token_type_ids
lowerCamelCase__ = use_labels
lowerCamelCase__ = vocab_size
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_act
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = max_position_embeddings
lowerCamelCase__ = type_vocab_size
lowerCamelCase__ = type_sequence_label_size
lowerCamelCase__ = initializer_range
lowerCamelCase__ = num_labels
lowerCamelCase__ = num_choices
lowerCamelCase__ = scope
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
if self.use_token_type_ids:
lowerCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
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__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self ):
'''simple docstring'''
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=__lowerCAmelCase , initializer_range=self.initializer_range , use_stable_embedding=__lowerCAmelCase , )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = OpenLlamaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowerCamelCase__ = model(__lowerCAmelCase , attention_mask=__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 , __lowerCAmelCase , ):
'''simple docstring'''
lowerCamelCase__ = True
lowerCamelCase__ = OpenLlamaModel(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowerCamelCase__ = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=__lowerCAmelCase , )
lowerCamelCase__ = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , )
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 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
'''simple docstring'''
lowerCamelCase__ = OpenLlamaForCausalLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowerCamelCase__ = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
'''simple docstring'''
lowerCamelCase__ = True
lowerCamelCase__ = True
lowerCamelCase__ = OpenLlamaForCausalLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
# first forward pass
lowerCamelCase__ = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=__lowerCAmelCase , use_cache=__lowerCAmelCase , )
lowerCamelCase__ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCamelCase__ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowerCamelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCamelCase__ = torch.cat([input_mask, next_mask] , dim=-1 )
lowerCamelCase__ = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , )['''hidden_states'''][0]
lowerCamelCase__ = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=__lowerCAmelCase , past_key_values=__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , )['''hidden_states'''][0]
# select random slice
lowerCamelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCamelCase__ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase__ = 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(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) = config_and_inputs
lowerCamelCase__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
lowerCAmelCase_ = (OpenLlamaForCausalLM,) if is_torch_available() else ()
lowerCAmelCase_ = (
{
'''feature-extraction''': OpenLlamaModel,
'''text-classification''': OpenLlamaForSequenceClassification,
'''text-generation''': OpenLlamaForCausalLM,
'''zero-shot''': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = OpenLlamaModelTester(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 type in ["absolute", "relative_key", "relative_key_query"]:
lowerCamelCase__ = type
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = 3
lowerCamelCase__ = input_dict['''input_ids''']
lowerCamelCase__ = input_ids.ne(1 ).to(__lowerCAmelCase )
lowerCamelCase__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCamelCase__ = OpenLlamaForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowerCamelCase__ = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , labels=__lowerCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = 3
lowerCamelCase__ = '''single_label_classification'''
lowerCamelCase__ = input_dict['''input_ids''']
lowerCamelCase__ = input_ids.ne(1 ).to(__lowerCAmelCase )
lowerCamelCase__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCamelCase__ = OpenLlamaForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowerCamelCase__ = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , labels=__lowerCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = 3
lowerCamelCase__ = '''multi_label_classification'''
lowerCamelCase__ = input_dict['''input_ids''']
lowerCamelCase__ = input_ids.ne(1 ).to(__lowerCAmelCase )
lowerCamelCase__ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCamelCase__ = OpenLlamaForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowerCamelCase__ = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , labels=__lowerCAmelCase )
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 ):
'''simple docstring'''
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = ids_tensor([1, 1_0] , config.vocab_size )
lowerCamelCase__ = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ = OpenLlamaModel(__lowerCAmelCase )
original_model.to(__lowerCAmelCase )
original_model.eval()
lowerCamelCase__ = original_model(__lowerCAmelCase ).last_hidden_state
lowerCamelCase__ = original_model(__lowerCAmelCase ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ = {'''type''': scaling_type, '''factor''': 1_0.0}
lowerCamelCase__ = OpenLlamaModel(__lowerCAmelCase )
scaled_model.to(__lowerCAmelCase )
scaled_model.eval()
lowerCamelCase__ = scaled_model(__lowerCAmelCase ).last_hidden_state
lowerCamelCase__ = scaled_model(__lowerCAmelCase ).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(__lowerCAmelCase , __lowerCAmelCase , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-5 ) )
| 209 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class A (unittest.TestCase ):
'''simple docstring'''
def a_ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
A__ = ["""a""", """b""", """c"""]
# Defaults to last layer if both are None
A__ , A__ = get_aligned_output_features_output_indices(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ["""c"""] )
self.assertEqual(__lowerCAmelCase , [2] )
# Out indices set to match out features
A__ , A__ = get_aligned_output_features_output_indices(["""a""", """c"""] , __lowerCAmelCase , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(__lowerCAmelCase , [0, 2] )
# Out features set to match out indices
A__ , A__ = get_aligned_output_features_output_indices(__lowerCAmelCase , [0, 2] , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(__lowerCAmelCase , [0, 2] )
# Out features selected from negative indices
A__ , A__ = get_aligned_output_features_output_indices(__lowerCAmelCase , [-3, -1] , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(__lowerCAmelCase , [-3, -1] )
def a_ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , __lowerCAmelCase )
# Out features must be a list
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(("""a""", """b""") , (0, 1) , ["""a""", """b"""] )
# Out features must be a subset of stage names
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , ["""a"""] )
# Out indices must be a list or tuple
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(__lowerCAmelCase , 0 , ["""a""", """b"""] )
# Out indices must be a subset of stage names
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(__lowerCAmelCase , (0, 1) , ["""a"""] )
# Out features and out indices must be the same length
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0,) , ["""a""", """b""", """c"""] )
# Out features should match out indices
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 2) , ["""a""", """b""", """c"""] )
# Out features and out indices should be in order
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""b""", """a"""] , (0, 1) , ["""a""", """b"""] )
# Check passes with valid inputs
verify_out_features_out_indices(["""a""", """b""", """d"""] , (0, 1, -1) , ["""a""", """b""", """c""", """d"""] )
def a_ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
A__ = BackboneMixin()
A__ = ["""a""", """b""", """c"""]
A__ = ["""a""", """c"""]
A__ = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [0, 2] )
# Check out features and indices are updated correctly
A__ = ["""a""", """b"""]
self.assertEqual(backbone.out_features , ["""a""", """b"""] )
self.assertEqual(backbone.out_indices , [0, 1] )
A__ = [-3, -1]
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [-3, -1] )
| 274 | 0 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _lowerCAmelCase ( __snake_case : List[Any] , __snake_case : List[str]=None ) -> Dict:
__A : Union[str, Any] = None
if token is not None:
__A : Dict = {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}
__A : Optional[int] = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
__A : Tuple = requests.get(__snake_case , headers=__snake_case ).json()
__A : int = {}
try:
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
__A : int = math.ceil((result['total_count'] - 1_00) / 1_00 )
for i in range(__snake_case ):
__A : Any = requests.get(url + f'&page={i + 2}' , headers=__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 ( __snake_case : str , __snake_case : Optional[int]=None ) -> str:
__A : Optional[Any] = None
if token is not None:
__A : Optional[int] = {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}
__A : Dict = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
__A : Dict = requests.get(__snake_case , headers=__snake_case ).json()
__A : Union[str, Any] = {}
try:
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
__A : str = math.ceil((result['total_count'] - 1_00) / 1_00 )
for i in range(__snake_case ):
__A : List[str] = requests.get(url + f'&page={i + 2}' , headers=__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 ( __snake_case : Dict , __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : Optional[int] ) -> str:
__A : Any = None
if token is not None:
__A : List[Any] = {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}
__A : Any = requests.get(__snake_case , headers=__snake_case , allow_redirects=__snake_case )
__A : Optional[Any] = result.headers['Location']
__A : str = requests.get(__snake_case , allow_redirects=__snake_case )
__A : Union[str, Any] = os.path.join(__snake_case , f'{artifact_name}.zip' )
with open(__snake_case , 'wb' ) as fp:
fp.write(response.content )
def _lowerCAmelCase ( __snake_case : Union[str, Any] , __snake_case : str=None ) -> Any:
__A : Any = []
__A : Tuple = []
__A : Dict = None
with zipfile.ZipFile(__snake_case ) as z:
for filename in z.namelist():
if not os.path.isdir(__snake_case ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(__snake_case ) as f:
for line in f:
__A : str = line.decode('UTF-8' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
__A : int = line[: line.index(': ' )]
__A : Tuple = 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
__A : str = line[len('FAILED ' ) :]
failed_tests.append(__snake_case )
elif filename == "job_name.txt":
__A : Union[str, Any] = line
if len(__snake_case ) != len(__snake_case ):
raise ValueError(
f'`errors` and `failed_tests` should have the same number of elements. Got {len(__snake_case )} for `errors` '
f'and {len(__snake_case )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
' problem.' )
__A : Union[str, Any] = None
if job_name and job_links:
__A : int = job_links.get(__snake_case , __snake_case )
# A list with elements of the form (line of error, error, failed test)
__A : str = [x + [y] + [job_link] for x, y in zip(__snake_case , __snake_case )]
return result
def _lowerCAmelCase ( __snake_case : Union[str, Any] , __snake_case : Any=None ) -> Dict:
__A : Any = []
__A : Union[str, Any] = [os.path.join(__snake_case , __snake_case ) for p in os.listdir(__snake_case ) if p.endswith('.zip' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(__snake_case , job_links=__snake_case ) )
return errors
def _lowerCAmelCase ( __snake_case : Dict , __snake_case : Dict=None ) -> Tuple:
__A : Tuple = Counter()
counter.update([x[1] for x in logs] )
__A : List[str] = counter.most_common()
__A : int = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
__A : Any = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}
__A : List[str] = dict(sorted(r.items() , key=lambda __snake_case : item[1]["count"] , reverse=__snake_case ) )
return r
def _lowerCAmelCase ( __snake_case : Any ) -> Dict:
__A : Optional[Any] = test.split('::' )[0]
if test.startswith('tests/models/' ):
__A : List[str] = test.split('/' )[2]
else:
__A : List[Any] = None
return test
def _lowerCAmelCase ( __snake_case : str , __snake_case : Union[str, Any]=None ) -> Optional[Any]:
__A : Optional[Any] = [(x[0], x[1], get_model(x[2] )) for x in logs]
__A : int = [x for x in logs if x[2] is not None]
__A : Optional[Any] = {x[2] for x in logs}
__A : Union[str, Any] = {}
for test in tests:
__A : Optional[int] = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
__A : Dict = counter.most_common()
__A : Dict = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
__A : List[Any] = sum(error_counts.values() )
if n_errors > 0:
__A : str = {'count': n_errors, 'errors': error_counts}
__A : Dict = dict(sorted(r.items() , key=lambda __snake_case : item[1]["count"] , reverse=__snake_case ) )
return r
def _lowerCAmelCase ( __snake_case : Any ) -> Dict:
__A : str = '| no. | error | status |'
__A : Dict = '|-:|:-|:-|'
__A : Dict = [header, sep]
for error in reduced_by_error:
__A : int = reduced_by_error[error]['count']
__A : Any = f'| {count} | {error[:1_00]} | |'
lines.append(__snake_case )
return "\n".join(__snake_case )
def _lowerCAmelCase ( __snake_case : Union[str, Any] ) -> Any:
__A : Tuple = '| model | no. of errors | major error | count |'
__A : Optional[Any] = '|-:|-:|-:|-:|'
__A : Optional[Any] = [header, sep]
for model in reduced_by_model:
__A : Union[str, Any] = reduced_by_model[model]['count']
__A : Optional[Any] = list(reduced_by_model[model]['errors'].items() )[0]
__A : str = f'| {model} | {count} | {error[:60]} | {_count} |'
lines.append(__snake_case )
return "\n".join(__snake_case )
if __name__ == "__main__":
lowercase__ : Tuple = 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.''')
lowercase__ : List[str] = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
lowercase__ : str = get_job_links(args.workflow_run_id, token=args.token)
lowercase__ : Optional[Any] = {}
# 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:
lowercase__ : List[str] = k.find(''' / ''')
lowercase__ : Optional[Any] = k[index + len(''' / ''') :]
lowercase__ : List[Any] = 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)
lowercase__ : Optional[int] = 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)
lowercase__ : Optional[int] = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
lowercase__ : int = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
lowercase__ : 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)
lowercase__ : List[Any] = reduce_by_error(errors)
lowercase__ : Union[str, Any] = reduce_by_model(errors)
lowercase__ : Dict = make_github_table(reduced_by_error)
lowercase__ : List[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) | 350 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : str , __snake_case : str ) -> float:
def get_matched_characters(__snake_case : str , __snake_case : str ) -> str:
__A : Optional[int] = []
__A : Optional[Any] = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__A : str = int(max(0 , i - limit ) )
__A : str = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(__snake_case )
__A : Dict = f'{_stra[0:_stra.index(__snake_case )]} {_stra[_stra.index(__snake_case ) + 1:]}'
return "".join(__snake_case )
# matching characters
__A : Any = get_matched_characters(__snake_case , __snake_case )
__A : str = get_matched_characters(__snake_case , __snake_case )
__A : str = len(__snake_case )
# transposition
__A : Tuple = (
len([(ca, ca) for ca, ca in zip(__snake_case , __snake_case ) if ca != ca] ) // 2
)
if not match_count:
__A : int = 0.0
else:
__A : Union[str, Any] = (
1
/ 3
* (
match_count / len(__snake_case )
+ match_count / len(__snake_case )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__A : Tuple = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world''')) | 190 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase_ = {
'''vocab_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
},
'''added_tokens.json''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
},
'''merges_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
},
}
lowerCAmelCase_ = {
'''RUCAIBox/mvp''': 1_0_2_4,
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = VOCAB_FILES_NAMES
lowerCamelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ : List[Any] = ['''input_ids''', '''attention_mask''']
lowerCamelCase_ : Any = MvpTokenizer
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__="replace" , __magic_name__="<s>" , __magic_name__="</s>" , __magic_name__="</s>" , __magic_name__="<s>" , __magic_name__="<unk>" , __magic_name__="<pad>" , __magic_name__="<mask>" , __magic_name__=False , __magic_name__=True , **__magic_name__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
__magic_name__ , __magic_name__ , tokenizer_file=__magic_name__ , errors=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , add_prefix_space=__magic_name__ , trim_offsets=__magic_name__ , **__magic_name__ , )
snake_case_ : Union[str, Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __magic_name__ ) != add_prefix_space:
snake_case_ : List[Any] = getattr(__magic_name__ , pre_tok_state.pop('''type''' ) )
snake_case_ : Any = add_prefix_space
snake_case_ : List[str] = pre_tok_class(**__magic_name__ )
snake_case_ : Optional[Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
snake_case_ : List[str] = '''post_processor'''
snake_case_ : Union[str, Any] = getattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
if tokenizer_component_instance:
snake_case_ : Union[str, Any] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
snake_case_ : Optional[Any] = tuple(state['''sep'''] )
if "cls" in state:
snake_case_ : Tuple = tuple(state['''cls'''] )
snake_case_ : Union[str, Any] = False
if state.get('''add_prefix_space''' , __magic_name__ ) != add_prefix_space:
snake_case_ : Union[str, Any] = add_prefix_space
snake_case_ : Tuple = True
if state.get('''trim_offsets''' , __magic_name__ ) != trim_offsets:
snake_case_ : Dict = trim_offsets
snake_case_ : Union[str, Any] = True
if changes_to_apply:
snake_case_ : int = getattr(__magic_name__ , state.pop('''type''' ) )
snake_case_ : Optional[Any] = component_class(**__magic_name__ )
setattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase (self , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else value
snake_case_ : Optional[int] = value
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : List[str] = kwargs.get('''is_split_into_words''' , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Tuple = kwargs.get('''is_split_into_words''' , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> Tuple[str]:
'''simple docstring'''
snake_case_ : Any = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__=None ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> List[int]:
'''simple docstring'''
snake_case_ : str = [self.sep_token_id]
snake_case_ : Any = [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 + sep + token_ids_a + sep ) * [0]
| 279 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if dist[i][j] != float('''inf''' ):
print(int(dist[i][j] ) , end='''\t''' )
else:
print('''INF''' , end='''\t''' )
print()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : int = [[float('''inf''' ) for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
snake_case_ : Dict = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(_UpperCamelCase ):
# looping through rows of graph array
for i in range(_UpperCamelCase ):
# looping through columns of graph array
for j in range(_UpperCamelCase ):
if (
dist[i][k] != float('''inf''' )
and dist[k][j] != float('''inf''' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
snake_case_ : List[Any] = dist[i][k] + dist[k][j]
_print_dist(_UpperCamelCase , _UpperCamelCase )
return dist, v
if __name__ == "__main__":
lowerCAmelCase_ = int(input('''Enter number of vertices: '''))
lowerCAmelCase_ = int(input('''Enter number of edges: '''))
lowerCAmelCase_ = [[float('''inf''') for i in range(v)] for j in range(v)]
for i in range(v):
lowerCAmelCase_ = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print('''\nEdge ''', i + 1)
lowerCAmelCase_ = int(input('''Enter source:'''))
lowerCAmelCase_ = int(input('''Enter destination:'''))
lowerCAmelCase_ = float(input('''Enter weight:'''))
lowerCAmelCase_ = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 279 | 1 |
def _UpperCamelCase ( UpperCamelCase_ : list , UpperCamelCase_ : list , UpperCamelCase_ : int ) -> int:
"""simple docstring"""
if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
if any(p < 0 for p in profit ):
raise ValueError('Profit can not be negative.' )
if any(w < 0 for w in weight ):
raise ValueError('Weight can not be negative.' )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
lowerCAmelCase__ = [p / w for p, w in zip(UpperCamelCase_ , UpperCamelCase_ )]
# Creating a copy of the list and sorting profit/weight in ascending order
lowerCAmelCase__ = sorted(UpperCamelCase_ )
# declaring useful variables
lowerCAmelCase__ = len(UpperCamelCase_ )
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
lowerCAmelCase__ = sorted_profit_by_weight[length - i - 1]
lowerCAmelCase__ = profit_by_weight.index(UpperCamelCase_ )
lowerCAmelCase__ = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
"""Input profits, weights, and then max_weight (all positive ints) separated by """
"""spaces."""
)
__snake_case : Dict = [int(x) for x in input("""Input profits separated by spaces: """).split()]
__snake_case : List[Any] = [int(x) for x in input("""Input weights separated by spaces: """).split()]
__snake_case : List[Any] = int(input("""Max weight allowed: """))
# Function Call
calc_profit(profit, weight, max_weight)
| 122 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Dict = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
__snake_case : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 122 | 1 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _SCREAMING_SNAKE_CASE ( __snake_case : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def _SCREAMING_SNAKE_CASE ( __snake_case : np.ndarray , __snake_case : np.ndarray , __snake_case : np.ndarray ):
'''simple docstring'''
lowercase = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__snake_case , __snake_case )
# Predict target for test data
lowercase = xgb.predict(__snake_case )
lowercase = predictions.reshape(len(__snake_case ) , 1 )
return predictions
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = fetch_california_housing()
lowercase , lowercase = data_handling(__snake_case )
lowercase , lowercase , lowercase , lowercase = train_test_split(
__snake_case , __snake_case , test_size=0.25 , random_state=1 )
lowercase = xgboost(__snake_case , __snake_case , __snake_case )
# Error printing
print(f'Mean Absolute Error : {mean_absolute_error(__snake_case , __snake_case )}' )
print(f'Mean Square Error : {mean_squared_error(__snake_case , __snake_case )}' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 220 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] ):
'''simple docstring'''
lowercase = []
embed.append(
(
f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
f'stage{idx}.patch_embed.proj.weight',
) )
embed.append(
(
f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
f'stage{idx}.patch_embed.proj.bias',
) )
embed.append(
(
f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
f'stage{idx}.patch_embed.norm.weight',
) )
embed.append(
(
f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
f'stage{idx}.patch_embed.norm.bias',
) )
return embed
def _SCREAMING_SNAKE_CASE ( __snake_case : Union[str, Any] , __snake_case : List[str] ):
'''simple docstring'''
lowercase = []
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
f'stage{idx}.blocks.{cnt}.attn.proj.weight',
) )
attention_weights.append(
(
f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
f'stage{idx}.blocks.{cnt}.attn.proj.bias',
) )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight') )
attention_weights.append(
(f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias') )
return attention_weights
def _SCREAMING_SNAKE_CASE ( __snake_case : List[str] ):
'''simple docstring'''
lowercase = []
token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token') )
return token
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] , __snake_case : Dict , __snake_case : List[str] , __snake_case : Union[str, Any] ):
'''simple docstring'''
lowercase = 'imagenet-1k-id2label.json'
lowercase = 10_00
lowercase = 'huggingface/label-files'
lowercase = num_labels
lowercase = json.load(open(cached_download(hf_hub_url(__snake_case , __snake_case , repo_type='dataset' ) ) , 'r' ) )
lowercase = {int(__snake_case ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
lowercase = lowercase = CvtConfig(num_labels=__snake_case , idalabel=__snake_case , labelaid=__snake_case )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' , 1 )[-1][4:6] == "13":
lowercase = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' , 1 )[-1][4:6] == "21":
lowercase = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
lowercase = [2, 2, 20]
lowercase = [3, 12, 16]
lowercase = [1_92, 7_68, 10_24]
lowercase = CvtForImageClassification(__snake_case )
lowercase = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
lowercase = image_size
lowercase = torch.load(__snake_case , map_location=torch.device('cpu' ) )
lowercase = OrderedDict()
lowercase = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
lowercase = list_of_state_dict + cls_token(__snake_case )
lowercase = list_of_state_dict + embeddings(__snake_case )
for cnt in range(config.depth[idx] ):
lowercase = list_of_state_dict + attention(__snake_case , __snake_case )
lowercase = list_of_state_dict + final()
for gg in list_of_state_dict:
print(__snake_case )
for i in range(len(__snake_case ) ):
lowercase = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__snake_case )
model.save_pretrained(__snake_case )
image_processor.save_pretrained(__snake_case )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
_UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'--cvt_model',
default='cvt-w24',
type=str,
help='Name of the cvt model you\'d like to convert.',
)
parser.add_argument(
'--image_size',
default=3_8_4,
type=int,
help='Input Image Size',
)
parser.add_argument(
'--cvt_file_name',
default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
type=str,
help='Input Image Size',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_UpperCamelCase : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 220 | 1 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
raise Exception('''requires gluonnlp == 0.8.3''')
if version.parse(mx.__version__) != version.parse('''1.5.0'''):
raise Exception('''requires mxnet == 1.5.0''')
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = '''The Nymphenburg Palace is a beautiful palace in Munich!'''
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : str = {
'''attention_cell''': '''multi_head''',
'''num_layers''': 4,
'''units''': 1_024,
'''hidden_size''': 768,
'''max_length''': 512,
'''num_heads''': 8,
'''scaled''': True,
'''dropout''': 0.1,
'''use_residual''': True,
'''embed_size''': 1_024,
'''embed_dropout''': 0.1,
'''word_embed''': None,
'''layer_norm_eps''': 1E-5,
'''token_type_vocab_size''': 2,
}
snake_case_ : Union[str, Any] = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
snake_case_ : List[str] = BERTEncoder(
attention_cell=predefined_args['''attention_cell'''] , num_layers=predefined_args['''num_layers'''] , units=predefined_args['''units'''] , hidden_size=predefined_args['''hidden_size'''] , max_length=predefined_args['''max_length'''] , num_heads=predefined_args['''num_heads'''] , scaled=predefined_args['''scaled'''] , dropout=predefined_args['''dropout'''] , output_attention=_UpperCamelCase , output_all_encodings=_UpperCamelCase , use_residual=predefined_args['''use_residual'''] , activation=predefined_args.get('''activation''' , '''gelu''' ) , layer_norm_eps=predefined_args.get('''layer_norm_eps''' , _UpperCamelCase ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
snake_case_ : Dict = '''openwebtext_ccnews_stories_books_cased'''
# Specify download folder to Gluonnlp's vocab
snake_case_ : List[Any] = os.path.join(get_home_dir() , '''models''' )
snake_case_ : int = _load_vocab(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , cls=_UpperCamelCase )
snake_case_ : Any = nlp.model.BERTModel(
_UpperCamelCase , len(_UpperCamelCase ) , units=predefined_args['''units'''] , embed_size=predefined_args['''embed_size'''] , embed_dropout=predefined_args['''embed_dropout'''] , word_embed=predefined_args['''word_embed'''] , use_pooler=_UpperCamelCase , use_token_type_embed=_UpperCamelCase , token_type_vocab_size=predefined_args['''token_type_vocab_size'''] , use_classifier=_UpperCamelCase , use_decoder=_UpperCamelCase , )
original_bort.load_parameters(_UpperCamelCase , cast_dtype=_UpperCamelCase , ignore_extra=_UpperCamelCase )
snake_case_ : List[Any] = original_bort._collect_params_with_prefix()
# Build our config 🤗
snake_case_ : Dict = {
'''architectures''': ['''BertForMaskedLM'''],
'''attention_probs_dropout_prob''': predefined_args['''dropout'''],
'''hidden_act''': '''gelu''',
'''hidden_dropout_prob''': predefined_args['''dropout'''],
'''hidden_size''': predefined_args['''embed_size'''],
'''initializer_range''': 0.02,
'''intermediate_size''': predefined_args['''hidden_size'''],
'''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
'''max_position_embeddings''': predefined_args['''max_length'''],
'''model_type''': '''bort''',
'''num_attention_heads''': predefined_args['''num_heads'''],
'''num_hidden_layers''': predefined_args['''num_layers'''],
'''pad_token_id''': 1, # 2 = BERT, 1 = RoBERTa
'''type_vocab_size''': 1, # 2 = BERT, 1 = RoBERTa
'''vocab_size''': len(_UpperCamelCase ),
}
snake_case_ : Optional[int] = BertConfig.from_dict(_UpperCamelCase )
snake_case_ : Union[str, Any] = BertForMaskedLM(_UpperCamelCase )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(_UpperCamelCase ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Tuple = hf_param.shape
snake_case_ : Dict = to_torch(params[gluon_param] )
snake_case_ : Dict = gluon_param.shape
assert (
shape_hf == shape_gluon
), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''
return gluon_param
snake_case_ : Optional[Any] = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , '''word_embed.0.weight''' )
snake_case_ : List[Any] = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , '''encoder.position_weight''' )
snake_case_ : Union[str, Any] = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , '''encoder.layer_norm.beta''' )
snake_case_ : Optional[int] = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , '''encoder.layer_norm.gamma''' )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
snake_case_ : List[Any] = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
snake_case_ : BertLayer = hf_bort_model.bert.encoder.layer[i]
# self attention
snake_case_ : BertSelfAttention = layer.attention.self
snake_case_ : List[str] = check_and_map_params(
self_attn.key.bias.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''' )
snake_case_ : Union[str, Any] = check_and_map_params(
self_attn.key.weight.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''' )
snake_case_ : str = check_and_map_params(
self_attn.query.bias.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''' )
snake_case_ : Union[str, Any] = check_and_map_params(
self_attn.query.weight.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''' )
snake_case_ : str = check_and_map_params(
self_attn.value.bias.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''' )
snake_case_ : Optional[int] = check_and_map_params(
self_attn.value.weight.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''' )
# self attention output
snake_case_ : BertSelfOutput = layer.attention.output
snake_case_ : List[Any] = check_and_map_params(
self_output.dense.bias , f'''encoder.transformer_cells.{i}.proj.bias''' )
snake_case_ : Optional[int] = check_and_map_params(
self_output.dense.weight , f'''encoder.transformer_cells.{i}.proj.weight''' )
snake_case_ : Union[str, Any] = check_and_map_params(
self_output.LayerNorm.bias , f'''encoder.transformer_cells.{i}.layer_norm.beta''' )
snake_case_ : int = check_and_map_params(
self_output.LayerNorm.weight , f'''encoder.transformer_cells.{i}.layer_norm.gamma''' )
# intermediate
snake_case_ : BertIntermediate = layer.intermediate
snake_case_ : Optional[Any] = check_and_map_params(
intermediate.dense.bias , f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''' )
snake_case_ : Tuple = check_and_map_params(
intermediate.dense.weight , f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''' )
# output
snake_case_ : BertOutput = layer.output
snake_case_ : Union[str, Any] = check_and_map_params(
bert_output.dense.bias , f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
snake_case_ : str = check_and_map_params(
bert_output.dense.weight , f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
snake_case_ : Tuple = check_and_map_params(
bert_output.LayerNorm.bias , f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
snake_case_ : List[str] = check_and_map_params(
bert_output.LayerNorm.weight , f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma''' )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
snake_case_ : List[str] = RobertaTokenizer.from_pretrained('''roberta-base''' )
snake_case_ : List[Any] = tokenizer.encode_plus(_UpperCamelCase )['''input_ids''']
# Get gluon output
snake_case_ : Optional[int] = mx.nd.array([input_ids] )
snake_case_ : str = original_bort(inputs=_UpperCamelCase , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(_UpperCamelCase )
snake_case_ : Any = BertModel.from_pretrained(_UpperCamelCase )
hf_bort_model.eval()
snake_case_ : List[str] = tokenizer.encode_plus(_UpperCamelCase , return_tensors='''pt''' )
snake_case_ : Any = hf_bort_model(**_UpperCamelCase )[0]
snake_case_ : Dict = output_gluon[0].asnumpy()
snake_case_ : Tuple = output_hf[0].detach().numpy()
snake_case_ : Optional[int] = np.max(np.abs(hf_layer - gluon_layer ) ).item()
snake_case_ : Tuple = np.allclose(_UpperCamelCase , _UpperCamelCase , atol=1E-3 )
if success:
print('''✔️ Both model do output the same tensors''' )
else:
print('''❌ Both model do **NOT** output the same tensors''' )
print('''Absolute difference is:''' , _UpperCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowerCAmelCase_ = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 279 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
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
# 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/text-classification/requirements.txt''')
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
lowerCamelCase_ : Optional[int] = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
lowerCamelCase_ : bool = field(
default=_a, metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
lowerCamelCase_ : bool = field(
default=_a, metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
}, )
lowerCamelCase_ : Optional[int] = field(
default=_a, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
}, )
lowerCamelCase_ : Optional[int] = field(
default=_a, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
}, )
lowerCamelCase_ : Optional[int] = field(
default=_a, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
}, )
@dataclass
class __lowerCAmelCase :
lowerCamelCase_ : str = field(
default=_a, metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
lowerCamelCase_ : str = field(
default=_a, metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''} )
lowerCamelCase_ : Optional[str] = field(
default=_a, metadata={'''help''': '''Train language if it is different from the evaluation language.'''} )
lowerCamelCase_ : Optional[str] = field(
default=_a, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
lowerCamelCase_ : Optional[str] = field(
default=_a, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
lowerCamelCase_ : Optional[str] = field(
default=_a, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
lowerCamelCase_ : Optional[bool] = field(
default=_a, metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}, )
lowerCamelCase_ : bool = field(
default=_a, metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}, )
lowerCamelCase_ : str = field(
default='''main''', metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}, )
lowerCamelCase_ : bool = field(
default=_a, metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
}, )
lowerCamelCase_ : bool = field(
default=_a, metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}, )
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
snake_case_ , snake_case_ , snake_case_ : Tuple = 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_xnli''' , _UpperCamelCase )
# 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()
snake_case_ : List[Any] = training_args.get_process_log_level()
logger.setLevel(_UpperCamelCase )
datasets.utils.logging.set_verbosity(_UpperCamelCase )
transformers.utils.logging.set_verbosity(_UpperCamelCase )
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.
snake_case_ : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ : Optional[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:
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.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
snake_case_ : Union[str, Any] = load_dataset(
'''xnli''' , model_args.language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
snake_case_ : str = load_dataset(
'''xnli''' , model_args.train_language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ : Optional[int] = train_dataset.features['''label'''].names
if training_args.do_eval:
snake_case_ : Dict = load_dataset(
'''xnli''' , model_args.language , split='''validation''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ : Tuple = eval_dataset.features['''label'''].names
if training_args.do_predict:
snake_case_ : int = load_dataset(
'''xnli''' , model_args.language , split='''test''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ : Optional[int] = predict_dataset.features['''label'''].names
# Labels
snake_case_ : int = len(_UpperCamelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCamelCase , idalabel={str(_UpperCamelCase ): label for i, label in enumerate(_UpperCamelCase )} , labelaid={label: i for i, label in enumerate(_UpperCamelCase )} , finetuning_task='''xnli''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
snake_case_ : Dict = '''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
snake_case_ : str = False
def preprocess_function(_UpperCamelCase ):
# Tokenize the texts
return tokenizer(
examples['''premise'''] , examples['''hypothesis'''] , padding=_UpperCamelCase , max_length=data_args.max_seq_length , truncation=_UpperCamelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
snake_case_ : List[Any] = min(len(_UpperCamelCase ) , data_args.max_train_samples )
snake_case_ : int = train_dataset.select(range(_UpperCamelCase ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
snake_case_ : Optional[int] = train_dataset.map(
_UpperCamelCase , batched=_UpperCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on train dataset''' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(_UpperCamelCase ) ) , 3 ):
logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
snake_case_ : List[str] = min(len(_UpperCamelCase ) , data_args.max_eval_samples )
snake_case_ : List[str] = eval_dataset.select(range(_UpperCamelCase ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
snake_case_ : List[str] = eval_dataset.map(
_UpperCamelCase , batched=_UpperCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on validation dataset''' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
snake_case_ : Union[str, Any] = min(len(_UpperCamelCase ) , data_args.max_predict_samples )
snake_case_ : Dict = predict_dataset.select(range(_UpperCamelCase ) )
with training_args.main_process_first(desc='''prediction dataset map pre-processing''' ):
snake_case_ : List[str] = predict_dataset.map(
_UpperCamelCase , batched=_UpperCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on prediction dataset''' , )
# Get the metric function
snake_case_ : int = evaluate.load('''xnli''' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCamelCase ):
snake_case_ : List[str] = p.predictions[0] if isinstance(p.predictions , _UpperCamelCase ) else p.predictions
snake_case_ : Tuple = np.argmax(_UpperCamelCase , axis=1 )
return metric.compute(predictions=_UpperCamelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
snake_case_ : Optional[int] = default_data_collator
elif training_args.fpaa:
snake_case_ : Any = DataCollatorWithPadding(_UpperCamelCase , pad_to_multiple_of=8 )
else:
snake_case_ : Any = None
# Initialize our Trainer
snake_case_ : Any = Trainer(
model=_UpperCamelCase , args=_UpperCamelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCamelCase , tokenizer=_UpperCamelCase , data_collator=_UpperCamelCase , )
# Training
if training_args.do_train:
snake_case_ : int = None
if training_args.resume_from_checkpoint is not None:
snake_case_ : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ : Dict = last_checkpoint
snake_case_ : int = trainer.train(resume_from_checkpoint=_UpperCamelCase )
snake_case_ : Union[str, Any] = train_result.metrics
snake_case_ : Union[str, Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCamelCase )
)
snake_case_ : Dict = min(_UpperCamelCase , len(_UpperCamelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''' , _UpperCamelCase )
trainer.save_metrics('''train''' , _UpperCamelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
snake_case_ : Any = trainer.evaluate(eval_dataset=_UpperCamelCase )
snake_case_ : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCamelCase )
snake_case_ : str = min(_UpperCamelCase , len(_UpperCamelCase ) )
trainer.log_metrics('''eval''' , _UpperCamelCase )
trainer.save_metrics('''eval''' , _UpperCamelCase )
# Prediction
if training_args.do_predict:
logger.info('''*** Predict ***''' )
snake_case_ , snake_case_ , snake_case_ : Optional[int] = trainer.predict(_UpperCamelCase , metric_key_prefix='''predict''' )
snake_case_ : Union[str, Any] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCamelCase )
)
snake_case_ : Optional[int] = min(_UpperCamelCase , len(_UpperCamelCase ) )
trainer.log_metrics('''predict''' , _UpperCamelCase )
trainer.save_metrics('''predict''' , _UpperCamelCase )
snake_case_ : List[Any] = np.argmax(_UpperCamelCase , axis=1 )
snake_case_ : Optional[Any] = os.path.join(training_args.output_dir , '''predictions.txt''' )
if trainer.is_world_process_zero():
with open(_UpperCamelCase , '''w''' ) as writer:
writer.write('''index\tprediction\n''' )
for index, item in enumerate(_UpperCamelCase ):
snake_case_ : List[str] = label_list[item]
writer.write(f'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 279 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_UpperCAmelCase : Any = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_ )
class lowercase ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *snake_case , **snake_case ):
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def a ( self , snake_case=None ):
snake_case_ = {}
if top_k is not None:
snake_case_ = top_k
return {}, {}, postprocess_params
def __call__( self , snake_case , **snake_case ):
return super().__call__(__UpperCAmelCase , **__UpperCAmelCase )
def a ( self , snake_case ):
snake_case_ = load_image(__UpperCAmelCase )
snake_case_ = self.image_processor(images=__UpperCAmelCase , return_tensors=self.framework )
return model_inputs
def a ( self , snake_case ):
snake_case_ = self.model(**__UpperCAmelCase )
return model_outputs
def a ( self , snake_case , snake_case=5 ):
if top_k > self.model.config.num_labels:
snake_case_ = self.model.config.num_labels
if self.framework == "pt":
snake_case_ = model_outputs.logits.softmax(-1 )[0]
snake_case_ = probs.topk(__UpperCAmelCase )
elif self.framework == "tf":
snake_case_ = stable_softmax(model_outputs.logits , axis=-1 )[0]
snake_case_ = tf.math.top_k(__UpperCAmelCase , k=__UpperCAmelCase )
snake_case_ = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
snake_case_ = scores.tolist()
snake_case_ = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase , __UpperCAmelCase )]
| 285 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowerCAmelCase__ : str = set()
# Replace all the whitespace in our sentence
lowerCAmelCase__ : Tuple = input_str.replace(""" """ , """""" )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(UpperCamelCase ) == 26
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowerCAmelCase__ : Any = [False] * 26
for char in input_str:
if char.islower():
lowerCAmelCase__ : Optional[Any] = True
elif char.isupper():
lowerCAmelCase__ : Any = True
return all(UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def _SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
from timeit import timeit
lowerCAmelCase__ : Union[str, Any] = """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
print(timeit("""is_pangram()""" , setup=UpperCamelCase ) )
print(timeit("""is_pangram_faster()""" , setup=UpperCamelCase ) )
print(timeit("""is_pangram_fastest()""" , setup=UpperCamelCase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 37 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_UpperCamelCase = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_UpperCamelCase = typing.Union[np.floataa, int, float] # noqa: UP007
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(_snake_case ) - np.asarray(_snake_case )) ** 2 ) )
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(_snake_case , _snake_case ) ) ** (1 / 2)
if __name__ == "__main__":
def _a ( ):
"""simple docstring"""
from timeit import timeit
print("""Without Numpy""" )
print(
timeit(
"""euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""" , number=1_0000 , globals=globals() , ) )
print("""With Numpy""" )
print(
timeit(
"""euclidean_distance([1, 2, 3], [4, 5, 6])""" , number=1_0000 , globals=globals() , ) )
benchmark()
| 234 |
"""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 _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = WavaVecaForSequenceClassification.from_pretrained(_snake_case , config=_snake_case )
UpperCAmelCase = downstream_dict["""projector.weight"""]
UpperCAmelCase = downstream_dict["""projector.bias"""]
UpperCAmelCase = downstream_dict["""model.post_net.linear.weight"""]
UpperCAmelCase = downstream_dict["""model.post_net.linear.bias"""]
return model
def _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = WavaVecaForAudioFrameClassification.from_pretrained(_snake_case , config=_snake_case )
UpperCAmelCase = downstream_dict["""model.linear.weight"""]
UpperCAmelCase = downstream_dict["""model.linear.bias"""]
return model
def _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = WavaVecaForXVector.from_pretrained(_snake_case , config=_snake_case )
UpperCAmelCase = downstream_dict["""connector.weight"""]
UpperCAmelCase = downstream_dict["""connector.bias"""]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
UpperCAmelCase = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
UpperCAmelCase = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
UpperCAmelCase = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
UpperCAmelCase = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
UpperCAmelCase = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
UpperCAmelCase = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
UpperCAmelCase = downstream_dict["""objective.W"""]
return model
@torch.no_grad()
def _a ( _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = torch.load(_snake_case , map_location="""cpu""" )
UpperCAmelCase = checkpoint["""Downstream"""]
UpperCAmelCase = WavaVecaConfig.from_pretrained(_snake_case )
UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
_snake_case , return_attention_mask=_snake_case , do_normalize=_snake_case )
UpperCAmelCase = hf_config.architectures[0]
if arch.endswith("""ForSequenceClassification""" ):
UpperCAmelCase = convert_classification(_snake_case , _snake_case , _snake_case )
elif arch.endswith("""ForAudioFrameClassification""" ):
UpperCAmelCase = convert_diarization(_snake_case , _snake_case , _snake_case )
elif arch.endswith("""ForXVector""" ):
UpperCAmelCase = convert_xvector(_snake_case , _snake_case , _snake_case )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
UpperCAmelCase = checkpoint["""Featurizer"""]["""weights"""]
hf_feature_extractor.save_pretrained(_snake_case )
hf_model.save_pretrained(_snake_case )
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)
| 234 | 1 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None ):
'''simple docstring'''
if version.parse(hfh.__version__ ).release < version.parse("""0.11.0""" ).release:
# old versions of hfh don't url-encode the file path
_a : Dict = quote(UpperCamelCase__ )
return hfh.hf_hub_url(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" , revision=UpperCamelCase__ )
| 294 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_snake_case = logging.getLogger(__name__)
class UpperCamelCase ( snake_case_ ):
def __init__( self : Optional[Any] , UpperCAmelCase__ : Optional[int]=-1 ) -> Tuple:
# in NER datasets, the last column is usually reserved for NER label
_a : Optional[int] = label_idx
def _lowercase ( self : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[Split, str] ) -> List[InputExample]:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : Any = mode.value
_a : Optional[int] = os.path.join(UpperCAmelCase__ , f"""{mode}.txt""" )
_a : int = 1
_a : int = []
with open(UpperCAmelCase__ , encoding="""utf-8""" ) as f:
_a : str = []
_a : str = []
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
_a : List[str] = []
_a : str = []
else:
_a : List[Any] = 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 _lowercase ( self : Optional[Any] , UpperCAmelCase__ : TextIO , UpperCAmelCase__ : TextIO , UpperCAmelCase__ : List ) -> Union[str, Any]:
_a : List[str] = 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]:
_a : int = 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 _lowercase ( self : List[Any] , UpperCAmelCase__ : str ) -> List[str]:
if path:
with open(UpperCAmelCase__ , """r""" ) as f:
_a : List[Any] = f.read().splitlines()
if "O" not in labels:
_a : 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 ( snake_case_ ):
def __init__( self : Union[str, Any] ) -> List[str]:
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2 )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : str ) -> List[str]:
if path:
with open(UpperCAmelCase__ , """r""" ) as f:
_a : Optional[int] = f.read().splitlines()
if "O" not in labels:
_a : Optional[Any] = ["""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 ( snake_case_ ):
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[Split, str] ) -> List[InputExample]:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_a : List[Any] = mode.value
_a : Union[str, Any] = os.path.join(UpperCAmelCase__ , f"""{mode}.txt""" )
_a : List[str] = 1
_a : Optional[Any] = []
with open(UpperCAmelCase__ , encoding="""utf-8""" ) as f:
for sentence in parse_incr(UpperCAmelCase__ ):
_a : List[Any] = []
_a : Any = []
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 _lowercase ( self : Tuple , UpperCAmelCase__ : TextIO , UpperCAmelCase__ : TextIO , UpperCAmelCase__ : List ) -> Dict:
_a : Optional[Any] = 0
for sentence in parse_incr(UpperCAmelCase__ ):
_a : List[str] = preds_list[example_id]
_a : str = """"""
for token in sentence:
out += f"""{token['form']} ({token['upos']}|{s_p.pop(0 )}) """
out += "\n"
writer.write(UpperCAmelCase__ )
example_id += 1
def _lowercase ( self : List[str] , UpperCAmelCase__ : str ) -> List[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",
]
| 294 | 1 |
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list ):
if any(not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(_UpperCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(_UpperCAmelCase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 352 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCamelCase : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
__UpperCamelCase : str = [0, 25, 50]
__UpperCamelCase : int = [25, 50, 75]
__UpperCamelCase : str = fuzz.membership.trimf(X, abca)
__UpperCamelCase : Tuple = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
__UpperCamelCase : Dict = np.ones(75)
__UpperCamelCase : str = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
__UpperCamelCase : Optional[Any] = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
__UpperCamelCase : Dict = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
__UpperCamelCase : Dict = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
__UpperCamelCase : List[str] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
__UpperCamelCase : List[str] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
__UpperCamelCase : Tuple = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
__UpperCamelCase : Union[str, Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
__UpperCamelCase : Dict = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('''Young''')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('''Middle aged''')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('''union''')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('''intersection''')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('''complement_a''')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('''difference a/b''')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('''alg_sum''')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('''alg_product''')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('''bdd_sum''')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('''bdd_difference''')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 309 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCamelCase ( _lowerCamelCase : Optional[int] ):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def UpperCamelCase ( _lowerCamelCase : List[Any] , _lowerCamelCase : Tuple ):
A__ = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
A__ = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
A__ = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
A__ = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
A__ = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
A__ = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
A__ = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
A__ = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
A__ = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
A__ = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
A__ = key.replace("image_encoder.module" , "flava.image_model" )
A__ = key.replace("text_encoder.module" , "flava.text_model" )
A__ = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
A__ = key.replace("mm_encoder.module" , "flava.multimodal_model" )
A__ = key.replace("text_projection" , "flava.text_projection" )
A__ = key.replace("image_projection" , "flava.image_projection" )
A__ = value.float()
for key, value in codebook_state_dict.items():
A__ = value
return upgrade
@torch.no_grad()
def UpperCamelCase ( _lowerCamelCase : Any , _lowerCamelCase : Any , _lowerCamelCase : str , _lowerCamelCase : Any=None ):
if config_path is not None:
A__ = FlavaConfig.from_pretrained(_lowerCamelCase )
else:
A__ = FlavaConfig()
A__ = FlavaForPreTraining(_lowerCamelCase ).eval()
A__ = convert_dalle_checkpoint(_lowerCamelCase , _lowerCamelCase , save_checkpoint=_lowerCamelCase )
if os.path.exists(_lowerCamelCase ):
A__ = torch.load(_lowerCamelCase , map_location="cpu" )
else:
A__ = torch.hub.load_state_dict_from_url(_lowerCamelCase , map_location="cpu" )
A__ = upgrade_state_dict(_lowerCamelCase , _lowerCamelCase )
hf_model.load_state_dict(_lowerCamelCase )
A__ = hf_model.state_dict()
A__ = count_parameters(_lowerCamelCase )
A__ = count_parameters(_lowerCamelCase ) + count_parameters(_lowerCamelCase )
assert torch.allclose(_lowerCamelCase , _lowerCamelCase , atol=1e-3 )
hf_model.save_pretrained(_lowerCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] =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 flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__lowerCAmelCase : Any =parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 237 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowerCAmelCase : Any =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCAmelCase : Optional[int] =" def __init__(self, config):\n super().__init__()\n self.transform = BertPredictionHeadTransform(config)\n\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n self.decoder.bias = self.bias\n\n def forward(self, hidden_states):\n hidden_states = self.transform(hidden_states)\n hidden_states = self.decoder(hidden_states)\n return hidden_states\n"
class UpperCAmelCase ( unittest.TestCase ):
def UpperCAmelCase_ ( self :Tuple )-> Union[str, Any]:
A__ = tempfile.mkdtemp()
os.makedirs(os.path.join(self.transformer_dir , "models/bert/" ) )
A__ = self.transformer_dir
shutil.copy(
os.path.join(lowercase_ , "src/transformers/models/bert/modeling_bert.py" ) , os.path.join(self.transformer_dir , "models/bert/modeling_bert.py" ) , )
def UpperCAmelCase_ ( self :Optional[int] )-> Tuple:
A__ = "src/transformers"
shutil.rmtree(self.transformer_dir )
def UpperCAmelCase_ ( self :List[Any] , lowercase_ :List[str] , lowercase_ :Union[str, Any] , lowercase_ :int , lowercase_ :Tuple=None )-> Optional[Any]:
A__ = comment + F"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
A__ = comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
A__ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 )
A__ = black.format_str(lowercase_ , mode=lowercase_ )
A__ = os.path.join(self.transformer_dir , "new_code.py" )
with open(lowercase_ , "w" , newline="\n" ) as f:
f.write(lowercase_ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(lowercase_ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=lowercase_ )
with open(lowercase_ , "r" ) as f:
self.assertTrue(f.read() , lowercase_ )
def UpperCAmelCase_ ( self :str )-> Optional[Any]:
A__ = check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead" )
self.assertEqual(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self :List[str] )-> Optional[int]:
# Base copy consistency
self.check_copy_consistency(
"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead" , "BertLMPredictionHead" , REFERENCE_CODE + "\n" , )
# With no empty line at the end
self.check_copy_consistency(
"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead" , "BertLMPredictionHead" , lowercase_ , )
# Copy consistency with rename
self.check_copy_consistency(
"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel" , "TestModelLMPredictionHead" , re.sub("Bert" , "TestModel" , lowercase_ ) , )
# Copy consistency with a really long name
A__ = "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
self.check_copy_consistency(
F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}" , F"{long_class_name}LMPredictionHead" , re.sub("Bert" , lowercase_ , lowercase_ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel" , "TestModelLMPredictionHead" , lowercase_ , overwrite_result=re.sub("Bert" , "TestModel" , lowercase_ ) , )
def UpperCAmelCase_ ( self :Dict )-> Any:
A__ = check_copies.LOCALIZED_READMES["README_zh-hans.md"]
A__ = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
" Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
" Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
" Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
" **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
" released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
" lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
" method has been applied to compress GPT2 into"
" [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
" [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
" Multilingual BERT into"
" [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
" version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
" (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
" as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
" Luong, Quoc V. Le, Christopher D. Manning."
)
A__ = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
A__ = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
" **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
" [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
" lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
" method has been applied to compress GPT2 into"
" [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
" [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
" Multilingual BERT into"
" [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
" version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
" Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
" than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
" Christopher D. Manning 发布。\n"
)
A__, A__ = check_copies.convert_to_localized_md(
lowercase_ , lowercase_ , localized_readme["format_model_list"] )
self.assertFalse(lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
A__, A__ = check_copies.convert_to_localized_md(
lowercase_ , lowercase_ , localized_readme["format_model_list"] )
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(lowercase_ )
A__ = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
" Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
" Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
" Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
)
A__ = (
"1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
" the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
A__ = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
A__, A__ = check_copies.convert_to_localized_md(
lowercase_ , lowercase_ , localized_readme["format_model_list"] )
# Check if the model link is synchronized.
self.assertEqual(lowercase_ , lowercase_ )
| 237 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ) ->Tuple:
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
SCREAMING_SNAKE_CASE : Optional[int] = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 128,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 142,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def __lowerCAmelCase ( self ) ->Tuple:
SCREAMING_SNAKE_CASE : Optional[int] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_ ) , x.transpose() ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def __lowerCAmelCase ( self ) ->str:
SCREAMING_SNAKE_CASE : List[str] = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : str = torch.tensor(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_ ) , transpose(SCREAMING_SNAKE_CASE_ ).numpy() ) )
SCREAMING_SNAKE_CASE : int = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE : Tuple = torch.tensor(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE_ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def __lowerCAmelCase ( self ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : Dict = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : Tuple = tf.constant(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_ ) , transpose(SCREAMING_SNAKE_CASE_ ).numpy() ) )
SCREAMING_SNAKE_CASE : Optional[Any] = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE : Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE_ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def __lowerCAmelCase ( self ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : Optional[int] = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : str = jnp.array(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_ ) , np.asarray(transpose(SCREAMING_SNAKE_CASE_ ) ) ) )
SCREAMING_SNAKE_CASE : Optional[int] = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE : str = jnp.array(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_ , axes=(1, 2, 0) ) , np.asarray(transpose(SCREAMING_SNAKE_CASE_ , axes=(1, 2, 0) ) ) ) )
def __lowerCAmelCase ( self ) ->str:
SCREAMING_SNAKE_CASE : str = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_ , (4, 3) ) , np.reshape(SCREAMING_SNAKE_CASE_ , (4, 3) ) ) )
SCREAMING_SNAKE_CASE : List[str] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_ , (12, 5) ) , np.reshape(SCREAMING_SNAKE_CASE_ , (12, 5) ) ) )
@require_torch
def __lowerCAmelCase ( self ) ->Dict:
SCREAMING_SNAKE_CASE : str = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : int = torch.tensor(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE_ , (4, 3) ).numpy() ) )
SCREAMING_SNAKE_CASE : List[str] = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE : str = torch.tensor(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE_ , (12, 5) ).numpy() ) )
@require_tf
def __lowerCAmelCase ( self ) ->Optional[int]:
SCREAMING_SNAKE_CASE : Tuple = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : Tuple = tf.constant(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE_ , (4, 3) ).numpy() ) )
SCREAMING_SNAKE_CASE : Dict = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE : List[Any] = tf.constant(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE_ , (12, 5) ).numpy() ) )
@require_flax
def __lowerCAmelCase ( self ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : str = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : Dict = jnp.array(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_ , (4, 3) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE_ , (4, 3) ) ) ) )
SCREAMING_SNAKE_CASE : Any = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE : Dict = jnp.array(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_ , (12, 5) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE_ , (12, 5) ) ) ) )
def __lowerCAmelCase ( self ) ->Tuple:
SCREAMING_SNAKE_CASE : Tuple = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_ ) , np.squeeze(SCREAMING_SNAKE_CASE_ ) ) )
SCREAMING_SNAKE_CASE : Tuple = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_ , axis=2 ) , np.squeeze(SCREAMING_SNAKE_CASE_ , axis=2 ) ) )
@require_torch
def __lowerCAmelCase ( self ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : Any = np.random.randn(1 , 3 , 4 )
SCREAMING_SNAKE_CASE : Tuple = torch.tensor(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_ ) , squeeze(SCREAMING_SNAKE_CASE_ ).numpy() ) )
SCREAMING_SNAKE_CASE : Any = np.random.randn(1 , 4 , 1 , 5 )
SCREAMING_SNAKE_CASE : int = torch.tensor(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE_ , axis=2 ).numpy() ) )
@require_tf
def __lowerCAmelCase ( self ) ->Dict:
SCREAMING_SNAKE_CASE : Any = np.random.randn(1 , 3 , 4 )
SCREAMING_SNAKE_CASE : int = tf.constant(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_ ) , squeeze(SCREAMING_SNAKE_CASE_ ).numpy() ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.random.randn(1 , 4 , 1 , 5 )
SCREAMING_SNAKE_CASE : int = tf.constant(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE_ , axis=2 ).numpy() ) )
@require_flax
def __lowerCAmelCase ( self ) ->str:
SCREAMING_SNAKE_CASE : Optional[int] = np.random.randn(1 , 3 , 4 )
SCREAMING_SNAKE_CASE : int = jnp.array(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_ ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE_ ) ) ) )
SCREAMING_SNAKE_CASE : Tuple = np.random.randn(1 , 4 , 1 , 5 )
SCREAMING_SNAKE_CASE : Dict = jnp.array(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_ , axis=2 ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE_ , axis=2 ) ) ) )
def __lowerCAmelCase ( self ) ->Dict:
SCREAMING_SNAKE_CASE : Union[str, Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE_ , axis=1 ) , np.expand_dims(SCREAMING_SNAKE_CASE_ , axis=1 ) ) )
@require_torch
def __lowerCAmelCase ( self ) ->List[str]:
SCREAMING_SNAKE_CASE : List[Any] = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : Dict = torch.tensor(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE_ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE_ , axis=1 ).numpy() ) )
@require_tf
def __lowerCAmelCase ( self ) ->Dict:
SCREAMING_SNAKE_CASE : Optional[Any] = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : List[Any] = tf.constant(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE_ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE_ , axis=1 ).numpy() ) )
@require_flax
def __lowerCAmelCase ( self ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : Optional[Any] = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE : Optional[int] = jnp.array(SCREAMING_SNAKE_CASE_ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE_ , axis=1 ) , np.asarray(expand_dims(SCREAMING_SNAKE_CASE_ , axis=1 ) ) ) )
| 365 |
import math
a__ : List[str] = 10
a__ : Optional[int] = 7
a__ : int = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCAmelCase_( a__ = 20 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = math.comb(a__ , a__ )
SCREAMING_SNAKE_CASE : Dict = math.comb(NUM_BALLS - BALLS_PER_COLOUR , a__ )
SCREAMING_SNAKE_CASE : Any = NUM_COLOURS * (1 - missing_colour / total)
return F"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 19 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
UpperCamelCase__ : List[str] = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-classification''',
'''language-modeling''',
'''summarization''',
'''token-classification''',
'''question-answering''',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase__ : Union[str, Any] = logging.getLogger()
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
parser.add_argument("""-f""" )
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
return args.f
def lowerCAmelCase_ ( _lowerCamelCase: List[Any] , _lowerCamelCase: Optional[Any]="eval" ):
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , F"{split}_results.json" )
if os.path.exists(_lowerCamelCase ):
with open(_lowerCamelCase , """r""" ) as f:
return json.load(_lowerCamelCase )
raise ValueError(F"can't find {path}" )
UpperCamelCase__ : List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self.get_auto_remove_tmp_dir()
__SCREAMING_SNAKE_CASE : Any = F"\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n ".split()
with patch.object(lowerCAmelCase__ , """argv""" , lowerCAmelCase__ ):
run_flax_glue.main()
__SCREAMING_SNAKE_CASE : Tuple = get_results(lowerCAmelCase__ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.75 )
@slow
def UpperCamelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_auto_remove_tmp_dir()
__SCREAMING_SNAKE_CASE : Dict = F"\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowerCAmelCase__ , """argv""" , lowerCAmelCase__ ):
run_clm_flax.main()
__SCREAMING_SNAKE_CASE : List[Any] = get_results(lowerCAmelCase__ )
self.assertLess(result["""eval_perplexity"""] , 1_0_0 )
@slow
def UpperCamelCase__ ( self : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self.get_auto_remove_tmp_dir()
__SCREAMING_SNAKE_CASE : Union[str, Any] = F"\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n ".split()
with patch.object(lowerCAmelCase__ , """argv""" , lowerCAmelCase__ ):
run_summarization_flax.main()
__SCREAMING_SNAKE_CASE : Optional[Any] = get_results(lowerCAmelCase__ , split="""test""" )
self.assertGreaterEqual(result["""test_rouge1"""] , 1_0 )
self.assertGreaterEqual(result["""test_rouge2"""] , 2 )
self.assertGreaterEqual(result["""test_rougeL"""] , 7 )
self.assertGreaterEqual(result["""test_rougeLsum"""] , 7 )
@slow
def UpperCamelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = self.get_auto_remove_tmp_dir()
__SCREAMING_SNAKE_CASE : int = F"\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n ".split()
with patch.object(lowerCAmelCase__ , """argv""" , lowerCAmelCase__ ):
run_mlm_flax.main()
__SCREAMING_SNAKE_CASE : Optional[int] = get_results(lowerCAmelCase__ )
self.assertLess(result["""eval_perplexity"""] , 4_2 )
@slow
def UpperCamelCase__ ( self : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = self.get_auto_remove_tmp_dir()
__SCREAMING_SNAKE_CASE : Optional[int] = F"\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowerCAmelCase__ , """argv""" , lowerCAmelCase__ ):
run_ta_mlm_flax.main()
__SCREAMING_SNAKE_CASE : int = get_results(lowerCAmelCase__ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.42 )
@slow
def UpperCamelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = 7 if get_gpu_count() > 1 else 2
__SCREAMING_SNAKE_CASE : int = self.get_auto_remove_tmp_dir()
__SCREAMING_SNAKE_CASE : Optional[Any] = F"\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n ".split()
with patch.object(lowerCAmelCase__ , """argv""" , lowerCAmelCase__ ):
run_flax_ner.main()
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_results(lowerCAmelCase__ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.75 )
self.assertGreaterEqual(result["""eval_f1"""] , 0.3 )
@slow
def UpperCamelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_auto_remove_tmp_dir()
__SCREAMING_SNAKE_CASE : Tuple = F"\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n ".split()
with patch.object(lowerCAmelCase__ , """argv""" , lowerCAmelCase__ ):
run_qa.main()
__SCREAMING_SNAKE_CASE : Optional[int] = get_results(lowerCAmelCase__ )
self.assertGreaterEqual(result["""eval_f1"""] , 3_0 )
self.assertGreaterEqual(result["""eval_exact"""] , 3_0 ) | 112 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCamelCase__ : List[Any] = {
'''E''': 1_2.7_0,
'''T''': 9.0_6,
'''A''': 8.1_7,
'''O''': 7.5_1,
'''I''': 6.9_7,
'''N''': 6.7_5,
'''S''': 6.3_3,
'''H''': 6.0_9,
'''R''': 5.9_9,
'''D''': 4.2_5,
'''L''': 4.0_3,
'''C''': 2.7_8,
'''U''': 2.7_6,
'''M''': 2.4_1,
'''W''': 2.3_6,
'''F''': 2.2_3,
'''G''': 2.0_2,
'''Y''': 1.9_7,
'''P''': 1.9_3,
'''B''': 1.2_9,
'''V''': 0.9_8,
'''K''': 0.7_7,
'''J''': 0.1_5,
'''X''': 0.1_5,
'''Q''': 0.1_0,
'''Z''': 0.0_7,
}
UpperCamelCase__ : Optional[Any] = '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
UpperCamelCase__ : Dict = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def lowerCAmelCase_ ( _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : int = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def lowerCAmelCase_ ( _lowerCamelCase: tuple ):
return x[0]
def lowerCAmelCase_ ( _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : Dict = get_letter_count(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : dict[int, list[str]] = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : dict[int, str] = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = """""".join(freq_to_letter[freq] )
__SCREAMING_SNAKE_CASE : str = list(freq_to_letter_str.items() )
freq_pairs.sort(key=_lowerCamelCase , reverse=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : list[str] = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(_lowerCamelCase )
def lowerCAmelCase_ ( _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : List[Any] = get_frequency_order(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod() | 112 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : Union[str, Any] = ["image_processor", "tokenizer"]
a__ : List[str] = "AutoImageProcessor"
a__ : Tuple = "AutoTokenizer"
def __init__( self : int , _lowercase : Tuple=None , _lowercase : List[Any]=None , **_lowercase : int ):
__UpperCAmelCase = 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 , )
__UpperCAmelCase = kwargs.pop('''feature_extractor''' )
__UpperCAmelCase = 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 )
__UpperCAmelCase = self.image_processor
__UpperCAmelCase = False
def __call__( self : Dict , *_lowercase : Optional[Any] , **_lowercase : Tuple ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*_lowercase , **_lowercase )
__UpperCAmelCase = kwargs.pop('''images''' , _lowercase )
__UpperCAmelCase = kwargs.pop('''text''' , _lowercase )
if len(_lowercase ) > 0:
__UpperCAmelCase = args[0]
__UpperCAmelCase = args[1:]
if images is None and text is None:
raise ValueError('''You need to specify either an `images` or `text` input to process.''' )
if images is not None:
__UpperCAmelCase = self.image_processor(_lowercase , *_lowercase , **_lowercase )
if text is not None:
__UpperCAmelCase = self.tokenizer(_lowercase , **_lowercase )
if text is None:
return inputs
elif images is None:
return encodings
else:
__UpperCAmelCase = encodings['''input_ids''']
return inputs
def a ( self : Optional[int] , *_lowercase : Optional[Any] , **_lowercase : Any ):
return self.tokenizer.batch_decode(*_lowercase , **_lowercase )
def a ( self : Tuple , *_lowercase : List[str] , **_lowercase : Tuple ):
return self.tokenizer.decode(*_lowercase , **_lowercase )
@contextmanager
def a ( self : str ):
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your images inputs, or in a separate call.''' )
__UpperCAmelCase = True
__UpperCAmelCase = self.tokenizer
yield
__UpperCAmelCase = self.image_processor
__UpperCAmelCase = False
def a ( self : Optional[int] , _lowercase : Optional[int] , _lowercase : Dict=False , _lowercase : List[str]=None ):
if added_vocab is None:
__UpperCAmelCase = self.tokenizer.get_added_vocab()
__UpperCAmelCase = {}
while tokens:
__UpperCAmelCase = re.search(r'''<s_(.*?)>''' , _lowercase , re.IGNORECASE )
if start_token is None:
break
__UpperCAmelCase = start_token.group(1 )
__UpperCAmelCase = re.search(rF'''</s_{key}>''' , _lowercase , re.IGNORECASE )
__UpperCAmelCase = start_token.group()
if end_token is None:
__UpperCAmelCase = tokens.replace(_lowercase , '''''' )
else:
__UpperCAmelCase = end_token.group()
__UpperCAmelCase = re.escape(_lowercase )
__UpperCAmelCase = re.escape(_lowercase )
__UpperCAmelCase = re.search(F'''{start_token_escaped}(.*?){end_token_escaped}''' , _lowercase , re.IGNORECASE )
if content is not None:
__UpperCAmelCase = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
__UpperCAmelCase = self.tokenajson(_lowercase , is_inner_value=_lowercase , added_vocab=_lowercase )
if value:
if len(_lowercase ) == 1:
__UpperCAmelCase = value[0]
__UpperCAmelCase = value
else: # leaf nodes
__UpperCAmelCase = []
for leaf in content.split(r'''<sep/>''' ):
__UpperCAmelCase = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
__UpperCAmelCase = leaf[1:-2] # for categorical special tokens
output[key].append(_lowercase )
if len(output[key] ) == 1:
__UpperCAmelCase = output[key][0]
__UpperCAmelCase = tokens[tokens.find(_lowercase ) + len(_lowercase ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=_lowercase , added_vocab=_lowercase )
if len(_lowercase ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def a ( self : Tuple ):
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 : Tuple ):
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , _lowercase , )
return self.image_processor
| 86 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
_lowercase : List[Any] = logging.get_logger(__name__)
@add_end_docstrings(_lowerCAmelCase )
class _UpperCAmelCase ( _lowerCAmelCase ):
def __init__( self : str , *_lowercase : Tuple , **_lowercase : List[Any] ):
super().__init__(*_lowercase , **_lowercase )
self.check_model_type(_lowercase )
def a ( self : int , _lowercase : Dict=None , _lowercase : List[Any]=None , _lowercase : int=None , **_lowercase : Dict ):
__UpperCAmelCase , __UpperCAmelCase = {}, {}
if padding is not None:
__UpperCAmelCase = padding
if truncation is not None:
__UpperCAmelCase = truncation
if top_k is not None:
__UpperCAmelCase = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : List[str] , _lowercase : Union["Image.Image", str] , _lowercase : str = None , **_lowercase : Optional[Any] ):
if isinstance(_lowercase , (Image.Image, str) ) and isinstance(_lowercase , _lowercase ):
__UpperCAmelCase = {'''image''': image, '''question''': question}
else:
__UpperCAmelCase = image
__UpperCAmelCase = super().__call__(_lowercase , **_lowercase )
return results
def a ( self : Union[str, Any] , _lowercase : List[str] , _lowercase : Any=False , _lowercase : Union[str, Any]=False ):
__UpperCAmelCase = load_image(inputs['''image'''] )
__UpperCAmelCase = self.tokenizer(
inputs['''question'''] , return_tensors=self.framework , padding=_lowercase , truncation=_lowercase )
__UpperCAmelCase = self.image_processor(images=_lowercase , return_tensors=self.framework )
model_inputs.update(_lowercase )
return model_inputs
def a ( self : Optional[Any] , _lowercase : str ):
__UpperCAmelCase = self.model(**_lowercase )
return model_outputs
def a ( self : str , _lowercase : Optional[int] , _lowercase : Any=5 ):
if top_k > self.model.config.num_labels:
__UpperCAmelCase = self.model.config.num_labels
if self.framework == "pt":
__UpperCAmelCase = model_outputs.logits.sigmoid()[0]
__UpperCAmelCase , __UpperCAmelCase = probs.topk(_lowercase )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
__UpperCAmelCase = scores.tolist()
__UpperCAmelCase = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_lowercase , _lowercase )]
| 86 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
__snake_case : List[str] = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
__snake_case : Any = {
'''allenai/longformer-base-4096''': 4_096,
'''allenai/longformer-large-4096''': 4_096,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 4_096,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 4_096,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _lowercase ( ) -> Any:
__lowerCAmelCase : Optional[Any] = (
list(range(ord("!" ) ,ord("~" ) + 1 ) ) + list(range(ord("¡" ) ,ord("¬" ) + 1 ) ) + list(range(ord("®" ) ,ord("ÿ" ) + 1 ) )
)
__lowerCAmelCase : int = bs[:]
__lowerCAmelCase : Optional[Any] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__snake_case )
cs.append(2**8 + n )
n += 1
__lowerCAmelCase : Optional[int] = [chr(__snake_case ) for n in cs]
return dict(zip(__snake_case ,__snake_case ) )
def _lowercase ( __snake_case ) -> Optional[Any]:
__lowerCAmelCase : str = set()
__lowerCAmelCase : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowerCAmelCase : Optional[Any] = char
return pairs
class A__ ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self: Dict , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Dict="replace" , _SCREAMING_SNAKE_CASE: Dict="<s>" , _SCREAMING_SNAKE_CASE: Union[str, Any]="</s>" , _SCREAMING_SNAKE_CASE: Any="</s>" , _SCREAMING_SNAKE_CASE: str="<s>" , _SCREAMING_SNAKE_CASE: int="<unk>" , _SCREAMING_SNAKE_CASE: str="<pad>" , _SCREAMING_SNAKE_CASE: Any="<mask>" , _SCREAMING_SNAKE_CASE: Tuple=False , **_SCREAMING_SNAKE_CASE: Tuple , ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : str = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else bos_token
__lowerCAmelCase : List[str] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else eos_token
__lowerCAmelCase : str = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else sep_token
__lowerCAmelCase : Union[str, Any] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else cls_token
__lowerCAmelCase : str = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else unk_token
__lowerCAmelCase : Optional[Any] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowerCAmelCase : Dict = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else mask_token
super().__init__(
errors=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , **_UpperCAmelCase , )
with open(_UpperCAmelCase , encoding="utf-8") as vocab_handle:
__lowerCAmelCase : Union[str, Any] = json.load(_UpperCAmelCase)
__lowerCAmelCase : str = {v: k for k, v in self.encoder.items()}
__lowerCAmelCase : Union[str, Any] = errors # how to handle errors in decoding
__lowerCAmelCase : Any = bytes_to_unicode()
__lowerCAmelCase : Any = {v: k for k, v in self.byte_encoder.items()}
with open(_UpperCAmelCase , encoding="utf-8") as merges_handle:
__lowerCAmelCase : Optional[int] = merges_handle.read().split("\n")[1:-1]
__lowerCAmelCase : Tuple = [tuple(merge.split()) for merge in bpe_merges]
__lowerCAmelCase : Dict = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase))))
__lowerCAmelCase : Optional[int] = {}
__lowerCAmelCase : Optional[int] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowerCAmelCase : Any = re.compile(r"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")
@property
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Dict:
"""simple docstring"""
return len(self.encoder)
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> List[Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder)
def _SCREAMING_SNAKE_CASE ( self: List[Any] , _SCREAMING_SNAKE_CASE: Tuple) -> Union[str, Any]:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__lowerCAmelCase : Union[str, Any] = tuple(_UpperCAmelCase)
__lowerCAmelCase : Union[str, Any] = get_pairs(_UpperCAmelCase)
if not pairs:
return token
while True:
__lowerCAmelCase : Any = min(_UpperCAmelCase , key=lambda _SCREAMING_SNAKE_CASE: self.bpe_ranks.get(_UpperCAmelCase , float("inf")))
if bigram not in self.bpe_ranks:
break
__lowerCAmelCase : Tuple = bigram
__lowerCAmelCase : Dict = []
__lowerCAmelCase : List[str] = 0
while i < len(_UpperCAmelCase):
try:
__lowerCAmelCase : List[str] = word.index(_UpperCAmelCase , _UpperCAmelCase)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
__lowerCAmelCase : Optional[Any] = j
if word[i] == first and i < len(_UpperCAmelCase) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
__lowerCAmelCase : Optional[Any] = tuple(_UpperCAmelCase)
__lowerCAmelCase : Tuple = new_word
if len(_UpperCAmelCase) == 1:
break
else:
__lowerCAmelCase : Optional[int] = get_pairs(_UpperCAmelCase)
__lowerCAmelCase : List[Any] = ' '.join(_UpperCAmelCase)
__lowerCAmelCase : int = word
return word
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: Tuple) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[str] = []
for token in re.findall(self.pat , _UpperCAmelCase):
__lowerCAmelCase : Any = ''.join(
self.byte_encoder[b] for b in token.encode("utf-8")) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase).split(" "))
return bpe_tokens
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: List[str]) -> int:
"""simple docstring"""
return self.encoder.get(_UpperCAmelCase , self.encoder.get(self.unk_token))
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: Any) -> Tuple:
"""simple docstring"""
return self.decoder.get(_UpperCAmelCase)
def _SCREAMING_SNAKE_CASE ( self: Any , _SCREAMING_SNAKE_CASE: List[Any]) -> int:
"""simple docstring"""
__lowerCAmelCase : Dict = ''.join(_UpperCAmelCase)
__lowerCAmelCase : Any = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8" , errors=self.errors)
return text
def _SCREAMING_SNAKE_CASE ( self: Optional[int] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: List[Any] = None) -> Union[str, Any]:
"""simple docstring"""
if not os.path.isdir(_UpperCAmelCase):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
return
__lowerCAmelCase : str = os.path.join(
_UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
__lowerCAmelCase : List[Any] = os.path.join(
_UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])
with open(_UpperCAmelCase , "w" , encoding="utf-8") as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_UpperCAmelCase , ensure_ascii=_UpperCAmelCase) + "\n")
__lowerCAmelCase : int = 0
with open(_UpperCAmelCase , "w" , encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _SCREAMING_SNAKE_CASE: kv[1]):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!")
__lowerCAmelCase : List[str] = token_index
writer.write(" ".join(_UpperCAmelCase) + "\n")
index += 1
return vocab_file, merge_file
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: List[Any] = None) -> Union[str, Any]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowerCAmelCase : List[Any] = [self.cls_token_id]
__lowerCAmelCase : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Dict = None , _SCREAMING_SNAKE_CASE: Any = False) -> int:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase)
if token_ids_a is None:
return [1] + ([0] * len(_UpperCAmelCase)) + [1]
return [1] + ([0] * len(_UpperCAmelCase)) + [1, 1] + ([0] * len(_UpperCAmelCase)) + [1]
def _SCREAMING_SNAKE_CASE ( self: int , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[Any] = None) -> Any:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = [self.sep_token_id]
__lowerCAmelCase : Tuple = [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 + sep + token_ids_a + sep) * [0]
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[Any]=False , **_SCREAMING_SNAKE_CASE: List[str]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[str] = kwargs.pop("add_prefix_space" , self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase) > 0 and not text[0].isspace()):
__lowerCAmelCase : Optional[int] = ' ' + text
return (text, kwargs) | 269 |
'''simple docstring'''
import random
def _lowerCAmelCase ( __snake_case : int , __snake_case : float , __snake_case : bool = False ) -> dict:
__A : dict = {i: [] for i in range(__snake_case )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(__snake_case )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(__snake_case ):
for j in range(i + 1 , __snake_case ):
if random.random() < probability:
graph[i].append(__snake_case )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(__snake_case )
return graph
def _lowerCAmelCase ( __snake_case : int ) -> dict:
return {
i: [j for j in range(__snake_case ) if i != j] for i in range(__snake_case )
}
if __name__ == "__main__":
import doctest
doctest.testmod() | 190 | 0 |
"""simple docstring"""
from __future__ import annotations
class __a :
def __init__( self , a__=None ):
_lowerCamelCase = data
_lowerCamelCase = None
def __repr__( self ):
_lowerCamelCase = []
_lowerCamelCase = self
while temp:
string_rep.append(F'{temp.data}' )
_lowerCamelCase = temp.next
return "->".join(a__ )
def SCREAMING_SNAKE_CASE_ ( snake_case : list )-> Optional[int]:
if not elements_list:
raise Exception('The Elements List is empty' )
_lowerCamelCase = _lowerCamelCase = Node(elements_list[0] )
for i in range(1 , len(snake_case ) ):
_lowerCamelCase = Node(elements_list[i] )
_lowerCamelCase = current.next
return head
def SCREAMING_SNAKE_CASE_ ( snake_case : Node )-> None:
if head_node is not None and isinstance(snake_case , snake_case ):
print_reverse(head_node.next )
print(head_node.data )
def SCREAMING_SNAKE_CASE_ ( )-> Union[str, Any]:
from doctest import testmod
testmod()
_lowerCamelCase = make_linked_list([14, 52, 14, 12, 43] )
print('Linked List:' )
print(snake_case )
print('Elements in Reverse:' )
print_reverse(snake_case )
if __name__ == "__main__":
main()
| 360 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
A_ : Any =logging.get_logger(__name__)
A_ : Dict =Dict[str, Any]
A_ : Dict =List[Prediction]
@add_end_docstrings(lowerCAmelCase__ )
class __a ( lowerCAmelCase__ ):
def __init__( self , *a__ , **a__ ):
super().__init__(*a__ , **a__ )
if self.framework == "tf":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
requires_backends(self , 'vision' )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def snake_case_ ( self , **a__ ):
_lowerCamelCase = {}
if "threshold" in kwargs:
_lowerCamelCase = kwargs['threshold']
return {}, {}, postprocess_kwargs
def __call__( self , *a__ , **a__ ):
return super().__call__(*a__ , **a__ )
def snake_case_ ( self , a__ ):
_lowerCamelCase = load_image(a__ )
_lowerCamelCase = torch.IntTensor([[image.height, image.width]] )
_lowerCamelCase = self.image_processor(images=[image] , return_tensors='pt' )
if self.tokenizer is not None:
_lowerCamelCase = self.tokenizer(text=inputs['words'] , boxes=inputs['boxes'] , return_tensors='pt' )
_lowerCamelCase = target_size
return inputs
def snake_case_ ( self , a__ ):
_lowerCamelCase = model_inputs.pop('target_size' )
_lowerCamelCase = self.model(**a__ )
_lowerCamelCase = outputs.__class__({'target_size': target_size, **outputs} )
if self.tokenizer is not None:
_lowerCamelCase = model_inputs['bbox']
return model_outputs
def snake_case_ ( self , a__ , a__=0.9 ):
_lowerCamelCase = model_outputs['target_size']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
_lowerCamelCase , _lowerCamelCase = target_size[0].tolist()
def unnormalize(a__ ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 10_00),
(height * bbox[1] / 10_00),
(width * bbox[2] / 10_00),
(height * bbox[3] / 10_00),
] ) )
_lowerCamelCase , _lowerCamelCase = model_outputs['logits'].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
_lowerCamelCase = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
_lowerCamelCase = [unnormalize(a__ ) for bbox in model_outputs['bbox'].squeeze(0 )]
_lowerCamelCase = ['score', 'label', 'box']
_lowerCamelCase = [dict(zip(a__ , a__ ) ) for vals in zip(scores.tolist() , a__ , a__ ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
_lowerCamelCase = self.image_processor.post_process_object_detection(a__ , a__ , a__ )
_lowerCamelCase = raw_annotations[0]
_lowerCamelCase = raw_annotation['scores']
_lowerCamelCase = raw_annotation['labels']
_lowerCamelCase = raw_annotation['boxes']
_lowerCamelCase = scores.tolist()
_lowerCamelCase = [self.model.config.idalabel[label.item()] for label in labels]
_lowerCamelCase = [self._get_bounding_box(a__ ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
_lowerCamelCase = ['score', 'label', 'box']
_lowerCamelCase = [
dict(zip(a__ , a__ ) )
for vals in zip(raw_annotation['scores'] , raw_annotation['labels'] , raw_annotation['boxes'] )
]
return annotation
def snake_case_ ( self , a__ ):
if self.framework != "pt":
raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.' )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = box.int().tolist()
_lowerCamelCase = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 80 | 0 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
a_ : Any = re.compile(R"""\s+""")
def a_ ( __snake_case : Tuple ) -> List[str]:
"""simple docstring"""
return {"hash": hashlib.mda(re.sub(__snake_case , '''''' , example['''content'''] ).encode('''utf-8''' ) ).hexdigest()}
def a_ ( __snake_case : Any ) -> str:
"""simple docstring"""
lowerCamelCase_ =[len(__snake_case ) for line in example['''content'''].splitlines()]
return {"line_mean": np.mean(__snake_case ), "line_max": max(__snake_case )}
def a_ ( __snake_case : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =np.mean([c.isalnum() for c in example['''content''']] )
return {"alpha_frac": alpha_frac}
def a_ ( __snake_case : str , __snake_case : int ) -> Optional[Any]:
"""simple docstring"""
if example["hash"] in uniques:
uniques.remove(example['''hash'''] )
return True
else:
return False
def a_ ( __snake_case : Dict , __snake_case : List[Any]=5 ) -> int:
"""simple docstring"""
lowerCamelCase_ =['''auto-generated''', '''autogenerated''', '''automatically generated''']
lowerCamelCase_ =example['''content'''].splitlines()
for _, line in zip(range(__snake_case ) , __snake_case ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def a_ ( __snake_case : List[Any] , __snake_case : str=5 , __snake_case : List[Any]=0.0_5 ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =['''unit tests''', '''test file''', '''configuration file''']
lowerCamelCase_ =example['''content'''].splitlines()
lowerCamelCase_ =0
lowerCamelCase_ =0
# first test
for _, line in zip(range(__snake_case ) , __snake_case ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
lowerCamelCase_ =example['''content'''].count('''\n''' )
lowerCamelCase_ =int(coeff * nlines )
for line in lines:
count_config += line.lower().count('''config''' )
count_test += line.lower().count('''test''' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def a_ ( __snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =['''def ''', '''class ''', '''for ''', '''while ''']
lowerCamelCase_ =example['''content'''].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def a_ ( __snake_case : str , __snake_case : Optional[int]=4 ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =example['''content'''].splitlines()
lowerCamelCase_ =0
for line in lines:
counter += line.lower().count('''=''' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def a_ ( __snake_case : Dict ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =tokenizer(example['''content'''] , truncation=__snake_case )['''input_ids''']
lowerCamelCase_ =len(example['''content'''] ) / len(__snake_case )
return {"ratio": ratio}
def a_ ( __snake_case : Any ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ ={}
results.update(get_hash(__snake_case ) )
results.update(line_stats(__snake_case ) )
results.update(alpha_stats(__snake_case ) )
results.update(char_token_ratio(__snake_case ) )
results.update(is_autogenerated(__snake_case ) )
results.update(is_config_or_test(__snake_case ) )
results.update(has_no_keywords(__snake_case ) )
results.update(has_few_assignments(__snake_case ) )
return results
def a_ ( __snake_case : List[str] , __snake_case : int , __snake_case : List[str] ) -> Dict:
"""simple docstring"""
if not check_uniques(__snake_case , __snake_case ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def a_ ( __snake_case : List[str] ) -> List[str]:
"""simple docstring"""
with open(__snake_case , '''rb''' ) as f_in:
with gzip.open(str(__snake_case ) + '''.gz''' , '''wb''' , compresslevel=6 ) as f_out:
shutil.copyfileobj(__snake_case , __snake_case )
os.unlink(__snake_case )
# Settings
a_ : List[Any] = HfArgumentParser(PreprocessingArguments)
a_ : int = parser.parse_args()
if args.num_workers is None:
a_ : List[str] = multiprocessing.cpu_count()
a_ : Optional[Any] = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
a_ : Any = time.time()
a_ : str = load_dataset(args.dataset_name, split="""train""")
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
a_ : Tuple = time.time()
a_ : Any = ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
a_ : List[Any] = set(ds.unique("""hash"""))
a_ : str = len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
a_ : Tuple = time.time()
a_ : int = ds.filter(filter, fn_kwargs={"""uniques""": uniques, """args""": args})
print(F"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(F"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
a_ : Union[str, Any] = time.time()
a_ , a_ : List[str] = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(F"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
a_ : str = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / """duplicate_clusters.json""", """w""") as f:
json.dump(duplicate_clusters, f)
a_ : Optional[Any] = output_dir / """data"""
data_dir.mkdir(exist_ok=True)
a_ : Any = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
a_ : int = str(data_dir / F"""file-{file_number+1:012}.json""")
a_ : Optional[Any] = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"""Time to save dataset: {time.time()-t_start:.2f}""")
| 75 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
a_ : Optional[Any] = 5
a_ : str = 10
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : int =SpeechaTextTokenizer
lowercase : int =False
lowercase : List[str] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =sp.SentencePieceProcessor()
spm_model.Load(lowerCAmelCase )
lowerCamelCase_ =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowerCAmelCase ) )]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =Path(self.tmpdirname )
save_json(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''<pad>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<s>''' )
self.assertEqual(vocab_keys[1], '''<pad>''' )
self.assertEqual(vocab_keys[-1], '''j''' )
self.assertEqual(len(lowerCAmelCase ), 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [289, 50, 14, 174, 386], )
lowerCamelCase_ =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.'''], )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ =tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.'''], )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 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], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 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, 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]], '''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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=lowerCAmelCase, model_name='''facebook/s2t-small-mustc-en-de-st''', revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''', )
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Tuple ='valhalla/s2t_mustc_multilinguial_medium'
lowercase : Dict ='C\'est trop cool'
lowercase : str ='Esto es genial'
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''], 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''], 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''], 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''], 11 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 10_000 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertIn(lowerCAmelCase, self.tokenizer.all_special_ids )
lowerCamelCase_ =[ES_CODE, 4, 1_601, 47, 7_647, 2]
lowerCamelCase_ =self.tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
lowerCamelCase_ =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0], lowerCAmelCase )
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE] )
lowerCamelCase_ ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE] )
| 75 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase_ = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 332 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
snake_case = 0
# the area corresponding to the grid that gives the product closest to target
snake_case = 0
# an estimate of b, using the quadratic formula
snake_case = 42
# the largest integer less than b_estimate
snake_case = 42
# the largest integer less than b_estimate
snake_case = 42
# the triangle number corresponding to b_floor
snake_case = 42
# the triangle number corresponding to b_ceil
snake_case = 42
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
snake_case = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
snake_case = floor(A )
snake_case = ceil(A )
snake_case = triangle_numbers[b_floor]
snake_case = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
snake_case = triangle_b_first_guess * triangle_a
snake_case = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
snake_case = triangle_b_second_guess * triangle_a
snake_case = idx_a * b_ceil
return area
if __name__ == "__main__":
print(f"{solution() = }")
| 332 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
def __lowerCamelCase ( __snake_case : str ) -> bytes:
"""simple docstring"""
A__ : str ="""https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
A__ : Dict =requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return requests.get(__snake_case ).content
if __name__ == "__main__":
__snake_case : Optional[Any] = input('Enter Video/IGTV url: ').strip()
__snake_case : Dict = F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
with open(file_name, 'wb') as fp:
fp.write(download_video(url))
print(F"""Done. Video saved to disk as {file_name}.""")
| 134 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( __snake_case : List[str], __snake_case : Union[str, Any], __snake_case : Dict ) -> Dict:
"""simple docstring"""
return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]
def __lowerCamelCase ( __snake_case : str, __snake_case : int, __snake_case : Dict, __snake_case : int="attention" ) -> str:
"""simple docstring"""
A__ : Union[str, Any] =np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :] )
A__ : str =k_tmp.reshape(k_tmp.shape[0], k_tmp.shape[1] * k_tmp.shape[2] )
A__ : List[Any] =np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :] )
A__ : Optional[int] =o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1], o_tmp.shape[2] )
A__ : Dict =np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :] )
A__ : Dict =q_tmp.reshape(q_tmp.shape[0], q_tmp.shape[1] * q_tmp.shape[2] )
A__ : Union[str, Any] =np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :] )
A__ : List[str] =v_tmp.reshape(v_tmp.shape[0], v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def __lowerCamelCase ( __snake_case : Dict, __snake_case : Any, __snake_case : Tuple, __snake_case : Optional[Any]=False ) -> Any:
"""simple docstring"""
if split_mlp_wi:
A__ : Any =params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
A__ : int =params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
A__ : Optional[Any] =(wi_a, wi_a)
else:
A__ : Optional[int] =params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]
A__ : int =params[f"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
return wi, wo
def __lowerCamelCase ( __snake_case : Optional[Any], __snake_case : str, __snake_case : Any, __snake_case : int ) -> List[Any]:
"""simple docstring"""
return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]
def __lowerCamelCase ( __snake_case : dict, *, __snake_case : int, __snake_case : bool, __snake_case : bool = False ) -> Union[str, Any]:
"""simple docstring"""
A__ : Optional[int] =traverse_util.flatten_dict(variables["""target"""] )
A__ : int ={"""/""".join(__snake_case ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
A__ : List[Any] ="""encoder/encoder/mlp/wi_0/kernel""" in old
print("""Split MLP:""", __snake_case )
A__ : Optional[int] =collections.OrderedDict()
# Shared embeddings.
A__ : List[Any] =old["""token_embedder/embedding"""]
# Encoder.
for i in range(__snake_case ):
# Block i, layer 0 (Self Attention).
A__ : Optional[Any] =tax_layer_norm_lookup(__snake_case, __snake_case, """encoder""", """pre_attention_layer_norm""" )
A__ , A__ , A__ , A__ : Optional[int] =tax_attention_lookup(__snake_case, __snake_case, """encoder""", """attention""" )
A__ : List[str] =layer_norm
A__ : Dict =k.T
A__ : Optional[int] =o.T
A__ : str =q.T
A__ : Any =v.T
# Block i, layer 1 (MLP).
A__ : List[Any] =tax_layer_norm_lookup(__snake_case, __snake_case, """encoder""", """pre_mlp_layer_norm""" )
A__ , A__ : int =tax_mlp_lookup(__snake_case, __snake_case, """encoder""", __snake_case )
A__ : Optional[int] =layer_norm
if split_mlp_wi:
A__ : List[str] =wi[0].T
A__ : List[str] =wi[1].T
else:
A__ : Optional[int] =wi.T
A__ : Optional[Any] =wo.T
if scalable_attention:
# convert the rel_embedding of each layer
A__ : int =tax_relpos_bias_lookup(
__snake_case, __snake_case, """encoder""" ).T
A__ : Optional[int] =old["""encoder/encoder_norm/scale"""]
if not scalable_attention:
A__ : List[Any] =tax_relpos_bias_lookup(
__snake_case, 0, """encoder""" ).T
A__ : Tuple =tax_relpos_bias_lookup(
__snake_case, 0, """decoder""" ).T
if not is_encoder_only:
# Decoder.
for i in range(__snake_case ):
# Block i, layer 0 (Self Attention).
A__ : List[str] =tax_layer_norm_lookup(__snake_case, __snake_case, """decoder""", """pre_self_attention_layer_norm""" )
A__ , A__ , A__ , A__ : List[str] =tax_attention_lookup(__snake_case, __snake_case, """decoder""", """self_attention""" )
A__ : str =layer_norm
A__ : List[str] =k.T
A__ : int =o.T
A__ : Tuple =q.T
A__ : Optional[Any] =v.T
# Block i, layer 1 (Cross Attention).
A__ : int =tax_layer_norm_lookup(__snake_case, __snake_case, """decoder""", """pre_cross_attention_layer_norm""" )
A__ , A__ , A__ , A__ : Optional[Any] =tax_attention_lookup(__snake_case, __snake_case, """decoder""", """encoder_decoder_attention""" )
A__ : str =layer_norm
A__ : Union[str, Any] =k.T
A__ : str =o.T
A__ : Any =q.T
A__ : str =v.T
# Block i, layer 2 (MLP).
A__ : str =tax_layer_norm_lookup(__snake_case, __snake_case, """decoder""", """pre_mlp_layer_norm""" )
A__ , A__ : Optional[int] =tax_mlp_lookup(__snake_case, __snake_case, """decoder""", __snake_case )
A__ : Dict =layer_norm
if split_mlp_wi:
A__ : List[Any] =wi[0].T
A__ : Union[str, Any] =wi[1].T
else:
A__ : Optional[int] =wi.T
A__ : str =wo.T
if scalable_attention:
# convert the rel_embedding of each layer
A__ : str =tax_relpos_bias_lookup(__snake_case, __snake_case, """decoder""" ).T
A__ : str =old["""decoder/decoder_norm/scale"""]
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
A__ : Tuple =old["""decoder/logits_dense/kernel"""].T
return new
def __lowerCamelCase ( __snake_case : Dict, __snake_case : bool ) -> Optional[Any]:
"""simple docstring"""
A__ : Any =collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
A__ : Union[str, Any] =state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
A__ : List[str] =state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
A__ : Optional[Any] =state_dict["""shared.weight"""]
return state_dict
def __lowerCamelCase ( __snake_case : str, __snake_case : str, __snake_case : Optional[Any], __snake_case : int, __snake_case : Optional[int] ) -> Optional[int]:
"""simple docstring"""
A__ : str =checkpoints.load_tax_checkpoint(__snake_case )
A__ : Optional[Any] =convert_tax_to_pytorch(
__snake_case, num_layers=config.num_layers, is_encoder_only=__snake_case, scalable_attention=__snake_case )
A__ : str =make_state_dict(__snake_case, __snake_case )
model.load_state_dict(__snake_case, strict=__snake_case )
def __lowerCamelCase ( __snake_case : Optional[int], __snake_case : Dict, __snake_case : Optional[int], __snake_case : bool = False, __snake_case : bool = False, ) -> Dict:
"""simple docstring"""
A__ : Tuple =MTaConfig.from_json_file(__snake_case )
print(f"Building PyTorch model from configuration: {config}" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
A__ : List[Any] =UMTaEncoderModel(__snake_case )
else:
A__ : int =UMTaForConditionalGeneration(__snake_case )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(__snake_case )
# Verify that we can load the checkpoint.
model.from_pretrained(__snake_case )
print("""Done""" )
if __name__ == "__main__":
__snake_case : str = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
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.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
parser.add_argument(
'--scalable_attention',
action='store_true',
help='Whether the model uses scaled attention (umt5 model)',
default=False,
)
__snake_case : Optional[Any] = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 134 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowerCamelCase : Optional[Any] = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__lowerCamelCase : Union[str, Any] = parser.parse_args()
__lowerCamelCase : Any = '''cpu'''
__lowerCamelCase : Optional[int] = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__lowerCamelCase : Union[str, Any] = '''path-to-your-trained-model'''
__lowerCamelCase : Optional[int] = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__lowerCamelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__lowerCamelCase : Dict = pipe.to(device)
# to channels last
__lowerCamelCase : Any = pipe.unet.to(memory_format=torch.channels_last)
__lowerCamelCase : str = pipe.vae.to(memory_format=torch.channels_last)
__lowerCamelCase : int = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__lowerCamelCase : str = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__lowerCamelCase : List[Any] = torch.randn(2, 4, 64, 64)
__lowerCamelCase : Dict = torch.rand(1) * 999
__lowerCamelCase : List[Any] = torch.randn(2, 77, 768)
__lowerCamelCase : List[str] = (sample, timestep, encoder_hidden_status)
try:
__lowerCamelCase : Dict = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__lowerCamelCase : int = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCamelCase : int = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCamelCase : str = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__lowerCamelCase : int = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__lowerCamelCase : str = 666
__lowerCamelCase : int = torch.Generator(device).manual_seed(seed)
__lowerCamelCase : int = {'''generator''': generator}
if args.steps is not None:
__lowerCamelCase : List[str] = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__lowerCamelCase : List[str] = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 204 | from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = ["input_features", "attention_mask"]
def __init__( self : int , _lowercase : Dict=80 , _lowercase : Union[str, Any]=1_60_00 , _lowercase : str=0.0 , _lowercase : int=10 , _lowercase : str=25 , _lowercase : Union[str, Any]="hamming_window" , _lowercase : Dict=3_27_68.0 , _lowercase : Optional[int]=0.97 , _lowercase : Optional[int]=1.0 , _lowercase : int=True , _lowercase : Union[str, Any]=True , _lowercase : Optional[int]=False , **_lowercase : Union[str, Any] , ):
"""simple docstring"""
super().__init__(feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase )
SCREAMING_SNAKE_CASE__ = feature_size
SCREAMING_SNAKE_CASE__ = sampling_rate
SCREAMING_SNAKE_CASE__ = padding_value
SCREAMING_SNAKE_CASE__ = hop_length
SCREAMING_SNAKE_CASE__ = win_length
SCREAMING_SNAKE_CASE__ = frame_signal_scale
SCREAMING_SNAKE_CASE__ = preemphasis_coeff
SCREAMING_SNAKE_CASE__ = mel_floor
SCREAMING_SNAKE_CASE__ = normalize_means
SCREAMING_SNAKE_CASE__ = normalize_vars
SCREAMING_SNAKE_CASE__ = win_function
SCREAMING_SNAKE_CASE__ = return_attention_mask
SCREAMING_SNAKE_CASE__ = win_length * sampling_rate // 10_00
SCREAMING_SNAKE_CASE__ = hop_length * sampling_rate // 10_00
SCREAMING_SNAKE_CASE__ = optimal_fft_length(self.sample_size )
SCREAMING_SNAKE_CASE__ = (self.n_fft // 2) + 1
def __a ( self : str , _lowercase : np.array ):
"""simple docstring"""
if self.win_function == "hamming_window":
SCREAMING_SNAKE_CASE__ = window_function(window_length=self.sample_size , name=self.win_function , periodic=_lowercase )
else:
SCREAMING_SNAKE_CASE__ = window_function(window_length=self.sample_size , name=self.win_function )
SCREAMING_SNAKE_CASE__ = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
SCREAMING_SNAKE_CASE__ = spectrogram(
one_waveform * self.frame_signal_scale , window=_lowercase , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=_lowercase , preemphasis=self.preemphasis_coeff , mel_filters=_lowercase , mel_floor=self.mel_floor , log_mel="""log""" , )
return msfc_features.T
def __a ( self : Tuple , _lowercase : Union[str, Any] , _lowercase : Optional[int] , _lowercase : int ):
"""simple docstring"""
if self.normalize_means:
SCREAMING_SNAKE_CASE__ = x[:input_length].mean(axis=0 )
SCREAMING_SNAKE_CASE__ = np.subtract(_lowercase , _lowercase )
if self.normalize_vars:
SCREAMING_SNAKE_CASE__ = x[:input_length].std(axis=0 )
SCREAMING_SNAKE_CASE__ = np.divide(_lowercase , _lowercase )
if input_length < x.shape[0]:
SCREAMING_SNAKE_CASE__ = padding_value
# make sure array is in float32
SCREAMING_SNAKE_CASE__ = x.astype(np.floataa )
return x
def __a ( self : int , _lowercase : List[np.ndarray] , _lowercase : Optional[np.ndarray] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(_lowercase , _lowercase , self.padding_value ) for x, n in zip(_lowercase , _lowercase )]
def __call__( self : Dict , _lowercase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _lowercase : Union[bool, str, PaddingStrategy] = False , _lowercase : Optional[int] = None , _lowercase : bool = False , _lowercase : Optional[int] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Optional[int] = None , **_lowercase : List[str] , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
f""" {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.""" )
SCREAMING_SNAKE_CASE__ = isinstance(_lowercase , 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}""" )
SCREAMING_SNAKE_CASE__ = is_batched_numpy or (
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
SCREAMING_SNAKE_CASE__ = [np.asarray(_lowercase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
SCREAMING_SNAKE_CASE__ = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
SCREAMING_SNAKE_CASE__ = [raw_speech]
# extract fbank features
SCREAMING_SNAKE_CASE__ = [self._extract_mfsc_features(_lowercase ) for one_waveform in raw_speech]
# convert into correct format for padding
SCREAMING_SNAKE_CASE__ = BatchFeature({"""input_features""": features} )
SCREAMING_SNAKE_CASE__ = self.pad(
_lowercase , padding=_lowercase , max_length=_lowercase , truncation=_lowercase , pad_to_multiple_of=_lowercase , return_attention_mask=_lowercase , **_lowercase , )
# make sure list is in array format
SCREAMING_SNAKE_CASE__ = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] , _lowercase ):
SCREAMING_SNAKE_CASE__ = [np.asarray(_lowercase , dtype=np.floataa ) for feature in input_features]
SCREAMING_SNAKE_CASE__ = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
SCREAMING_SNAKE_CASE__ = [np.asarray(_lowercase , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
SCREAMING_SNAKE_CASE__ = (
np.array(_lowercase , dtype=np.intaa )
if self._get_padding_strategies(_lowercase , max_length=_lowercase ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
SCREAMING_SNAKE_CASE__ = self.normalize(
padded_inputs["""input_features"""] , attention_mask=_lowercase )
if return_tensors is not None:
SCREAMING_SNAKE_CASE__ = padded_inputs.convert_to_tensors(_lowercase )
return padded_inputs
| 204 | 1 |
'''simple docstring'''
from math import ceil
def a_ ( _lowerCAmelCase = 1001 ) -> int:
__lowerCamelCase : Tuple = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
__lowerCamelCase : Dict = 2 * i + 1
__lowerCamelCase : int = 2 * i
__lowerCamelCase : Optional[int] = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
_UpperCamelCase = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number')
| 208 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = ['SpeechEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = ['FlaxSpeechEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 208 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a__ = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__( self , **_a ) -> Optional[int]:
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
_a : List[str] = deprecated_arg[3:]
_a : int = not kwargs.pop(_a )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
_a : Dict = kwargs.pop('''tpu_name''' , self.tpu_name )
_a : Dict = kwargs.pop('''device_idx''' , self.device_idx )
_a : Dict = kwargs.pop('''eager_mode''' , self.eager_mode )
_a : List[str] = kwargs.pop('''use_xla''' , self.use_xla )
super().__init__(**_a )
UpperCAmelCase__ : str = field(
default=__lowercase , metadata={"help": "Name of TPU"} , )
UpperCAmelCase__ : int = field(
default=0 , metadata={"help": "CPU / GPU device index. Defaults to 0."} , )
UpperCAmelCase__ : bool = field(default=__lowercase , metadata={"help": "Benchmark models in eager model."} )
UpperCAmelCase__ : bool = field(
default=__lowercase , metadata={
"help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
} , )
@cached_property
def __lowercase ( self ) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
requires_backends(self , ['''tf'''] )
_a : int = None
if self.tpu:
try:
if self.tpu_name:
_a : List[str] = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
_a : Tuple = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
_a : Optional[int] = None
return tpu
@cached_property
def __lowercase ( self ) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
requires_backends(self , ['''tf'''] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
_a : Dict = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , '''GPU''' )
_a : Optional[Any] = tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , '''GPU''' ) # disable GPU
_a : Any = tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}""" )
return strategy
@property
def __lowercase ( self ) -> bool:
requires_backends(self , ['''tf'''] )
return self._setup_tpu is not None
@property
def __lowercase ( self ) -> "tf.distribute.Strategy":
requires_backends(self , ['''tf'''] )
return self._setup_strategy
@property
def __lowercase ( self ) -> Any:
requires_backends(self , ['''tf'''] )
return tf.config.list_physical_devices('''GPU''' )
@property
def __lowercase ( self ) -> int:
requires_backends(self , ['''tf'''] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __lowercase ( self ) -> bool:
return self.n_gpu > 0
| 15 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : Dict = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : int = 2 * i + 1
_a : str = 2 * i
_a : Any = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
a__ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number''')
| 15 | 1 |
"""simple docstring"""
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
A_ = logging.get_logger(__name__)
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["input_features"]
def __init__( self: List[Any], a_: Tuple=80, a_: List[str]=16_000, a_: Tuple=160, a_: Optional[int]=30, a_: List[str]=400, a_: Union[str, Any]=0.0, a_: Union[str, Any]=False, **a_: Dict, ):
'''simple docstring'''
super().__init__(
feature_size=a_, sampling_rate=a_, padding_value=a_, return_attention_mask=a_, **a_, )
_snake_case : Union[str, Any] = n_fft
_snake_case : Optional[Any] = hop_length
_snake_case : Optional[int] = chunk_length
_snake_case : str = chunk_length * sampling_rate
_snake_case : Dict = self.n_samples // hop_length
_snake_case : List[str] = sampling_rate
_snake_case : Optional[int] = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2, num_mel_filters=a_, min_frequency=0.0, max_frequency=8_000.0, sampling_rate=a_, norm="""slaney""", mel_scale="""slaney""", )
def UpperCamelCase_ ( self: List[Any], a_: np.array ):
'''simple docstring'''
_snake_case : str = spectrogram(
a_, 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""", )
_snake_case : Dict = log_spec[:, :-1]
_snake_case : List[Any] = np.maximum(a_, log_spec.max() - 8.0 )
_snake_case : 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 UpperCamelCase_ ( a_: List[np.ndarray], a_: List[np.ndarray], a_: float = 0.0 ):
'''simple docstring'''
if attention_mask is not None:
_snake_case : str = np.array(a_, np.intaa )
_snake_case : str = []
for vector, length in zip(a_, attention_mask.sum(-1 ) ):
_snake_case : List[str] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
_snake_case : Any = padding_value
normed_input_values.append(a_ )
else:
_snake_case : Dict = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __call__( self: Optional[int], a_: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], a_: bool = True, a_: Optional[int] = None, a_: Optional[Union[str, TensorType]] = None, a_: Optional[bool] = None, a_: Optional[str] = "max_length", a_: Optional[int] = None, a_: Optional[int] = None, a_: Optional[bool] = None, **a_: Any, ):
'''simple docstring'''
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.""" )
_snake_case : str = isinstance(a_, 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}" )
_snake_case : Optional[Any] = is_batched_numpy or (
isinstance(a_, (list, tuple) ) and (isinstance(raw_speech[0], (np.ndarray, tuple, list) ))
)
if is_batched:
_snake_case : Union[str, Any] = [np.asarray([speech], dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(a_, np.ndarray ):
_snake_case : Optional[Any] = np.asarray(a_, dtype=np.floataa )
elif isinstance(a_, np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_snake_case : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_snake_case : List[str] = [np.asarray([raw_speech] ).T]
_snake_case : Optional[int] = BatchFeature({"""input_features""": raw_speech} )
# convert into correct format for padding
_snake_case : Optional[Any] = self.pad(
a_, padding=a_, max_length=max_length if max_length else self.n_samples, truncation=a_, pad_to_multiple_of=a_, return_attention_mask=return_attention_mask or do_normalize, )
# zero-mean and unit-variance normalization
if do_normalize:
_snake_case : Dict = self.zero_mean_unit_var_norm(
padded_inputs["""input_features"""], attention_mask=padded_inputs["""attention_mask"""], padding_value=self.padding_value, )
_snake_case : Tuple = np.stack(padded_inputs["""input_features"""], axis=0 )
# make sure list is in array format
_snake_case : Optional[int] = padded_inputs.get("""input_features""" ).transpose(2, 0, 1 )
_snake_case : Dict = [self._np_extract_fbank_features(a_ ) for waveform in input_features[0]]
if isinstance(input_features[0], a_ ):
_snake_case : List[str] = [np.asarray(a_, dtype=np.floataa ) for feature in input_features]
else:
_snake_case : List[Any] = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
_snake_case : Any = padded_inputs["""attention_mask"""][:, :: self.hop_length]
if return_tensors is not None:
_snake_case : List[str] = padded_inputs.convert_to_tensors(a_ )
return padded_inputs
def UpperCamelCase_ ( self: Optional[Any] ):
'''simple docstring'''
_snake_case : Union[str, Any] = copy.deepcopy(self.__dict__ )
_snake_case : Union[str, Any] = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 64 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
A_ = logging.get_logger(__name__)
class lowercase:
'''simple docstring'''
lowercase__ = 42
lowercase__ = None
@staticmethod
def UpperCamelCase_ ( ):
'''simple docstring'''
raise NotImplementedError
def UpperCamelCase_ ( self: Tuple, a_: int, a_: int, a_: str, **a_: Dict ):
'''simple docstring'''
raise NotImplementedError
def UpperCamelCase_ ( self: Union[str, Any], a_: List[str] ):
'''simple docstring'''
raise NotImplementedError
def UpperCamelCase_ ( self: Union[str, Any] ):
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}." )
@classmethod
def UpperCamelCase_ ( cls: Tuple ):
'''simple docstring'''
return f"`pip install {cls.pip_package or cls.name}`"
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "optuna"
@staticmethod
def UpperCamelCase_ ( ):
'''simple docstring'''
return is_optuna_available()
def UpperCamelCase_ ( self: Union[str, Any], a_: List[Any], a_: int, a_: str, **a_: List[str] ):
'''simple docstring'''
return run_hp_search_optuna(a_, a_, a_, **a_ )
def UpperCamelCase_ ( self: Optional[Any], a_: Any ):
'''simple docstring'''
return default_hp_space_optuna(a_ )
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "ray"
lowercase__ = "'ray[tune]'"
@staticmethod
def UpperCamelCase_ ( ):
'''simple docstring'''
return is_ray_available()
def UpperCamelCase_ ( self: int, a_: Optional[Any], a_: int, a_: str, **a_: List[Any] ):
'''simple docstring'''
return run_hp_search_ray(a_, a_, a_, **a_ )
def UpperCamelCase_ ( self: str, a_: Tuple ):
'''simple docstring'''
return default_hp_space_ray(a_ )
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "sigopt"
@staticmethod
def UpperCamelCase_ ( ):
'''simple docstring'''
return is_sigopt_available()
def UpperCamelCase_ ( self: Dict, a_: str, a_: int, a_: str, **a_: int ):
'''simple docstring'''
return run_hp_search_sigopt(a_, a_, a_, **a_ )
def UpperCamelCase_ ( self: str, a_: List[str] ):
'''simple docstring'''
return default_hp_space_sigopt(a_ )
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "wandb"
@staticmethod
def UpperCamelCase_ ( ):
'''simple docstring'''
return is_wandb_available()
def UpperCamelCase_ ( self: Optional[Any], a_: str, a_: int, a_: str, **a_: Union[str, Any] ):
'''simple docstring'''
return run_hp_search_wandb(a_, a_, a_, **a_ )
def UpperCamelCase_ ( self: str, a_: Any ):
'''simple docstring'''
return default_hp_space_wandb(a_ )
A_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(snake_case__ ) > 0:
_snake_case : Any = available_backends[0].name
if len(snake_case__ ) > 1:
logger.info(
F"{len(snake_case__ )} hyperparameter search backends available. Using {name} as the default." )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
F" - To install {backend.name} run {backend.pip_install()}"
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 64 | 1 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
lowercase = version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def lowerCamelCase_ ( UpperCamelCase__ : Dict, UpperCamelCase__ : tuple, UpperCamelCase__ : Path, UpperCamelCase__ : Any, UpperCamelCase__ : str, UpperCamelCase__ : Any, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[Any]=False, ):
'''simple docstring'''
output_path.parent.mkdir(parents=UpperCamelCase__, exist_ok=UpperCamelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
UpperCamelCase__, UpperCamelCase__, f=output_path.as_posix(), input_names=UpperCamelCase__, output_names=UpperCamelCase__, dynamic_axes=UpperCamelCase__, do_constant_folding=UpperCamelCase__, use_external_data_format=UpperCamelCase__, enable_onnx_checker=UpperCamelCase__, opset_version=UpperCamelCase__, )
else:
export(
UpperCamelCase__, UpperCamelCase__, f=output_path.as_posix(), input_names=UpperCamelCase__, output_names=UpperCamelCase__, dynamic_axes=UpperCamelCase__, do_constant_folding=UpperCamelCase__, opset_version=UpperCamelCase__, )
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : str, UpperCamelCase__ : str, UpperCamelCase__ : int, UpperCamelCase__ : bool = False ):
'''simple docstring'''
UpperCamelCase__ = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
UpperCamelCase__ = '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
else:
UpperCamelCase__ = '''cpu'''
UpperCamelCase__ = StableDiffusionPipeline.from_pretrained(UpperCamelCase__, torch_dtype=UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCamelCase__ = Path(UpperCamelCase__ )
# TEXT ENCODER
UpperCamelCase__ = pipeline.text_encoder.config.max_position_embeddings
UpperCamelCase__ = pipeline.text_encoder.config.hidden_size
UpperCamelCase__ = pipeline.tokenizer(
'''A sample prompt''', padding='''max_length''', max_length=pipeline.tokenizer.model_max_length, truncation=UpperCamelCase__, return_tensors='''pt''', )
onnx_export(
pipeline.text_encoder, model_args=(text_input.input_ids.to(device=UpperCamelCase__, dtype=torch.intaa )), output_path=output_path / '''text_encoder''' / '''model.onnx''', ordered_input_names=['''input_ids'''], output_names=['''last_hidden_state''', '''pooler_output'''], dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''sequence'''},
}, opset=UpperCamelCase__, )
del pipeline.text_encoder
# UNET
UpperCamelCase__ = pipeline.unet.config.in_channels
UpperCamelCase__ = pipeline.unet.config.sample_size
UpperCamelCase__ = output_path / '''unet''' / '''model.onnx'''
onnx_export(
pipeline.unet, model_args=(
torch.randn(2, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ).to(device=UpperCamelCase__, dtype=UpperCamelCase__ ),
torch.randn(2 ).to(device=UpperCamelCase__, dtype=UpperCamelCase__ ),
torch.randn(2, UpperCamelCase__, UpperCamelCase__ ).to(device=UpperCamelCase__, dtype=UpperCamelCase__ ),
False,
), output_path=UpperCamelCase__, ordered_input_names=['''sample''', '''timestep''', '''encoder_hidden_states''', '''return_dict'''], output_names=['''out_sample'''], dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''timestep''': {0: '''batch'''},
'''encoder_hidden_states''': {0: '''batch''', 1: '''sequence'''},
}, opset=UpperCamelCase__, use_external_data_format=UpperCamelCase__, )
UpperCamelCase__ = str(unet_path.absolute().as_posix() )
UpperCamelCase__ = os.path.dirname(UpperCamelCase__ )
UpperCamelCase__ = onnx.load(UpperCamelCase__ )
# clean up existing tensor files
shutil.rmtree(UpperCamelCase__ )
os.mkdir(UpperCamelCase__ )
# collate external tensor files into one
onnx.save_model(
UpperCamelCase__, UpperCamelCase__, save_as_external_data=UpperCamelCase__, all_tensors_to_one_file=UpperCamelCase__, location='''weights.pb''', convert_attribute=UpperCamelCase__, )
del pipeline.unet
# VAE ENCODER
UpperCamelCase__ = pipeline.vae
UpperCamelCase__ = vae_encoder.config.in_channels
UpperCamelCase__ = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
UpperCamelCase__ = lambda UpperCamelCase__, UpperCamelCase__ : vae_encoder.encode(UpperCamelCase__, UpperCamelCase__ )[0].sample()
onnx_export(
UpperCamelCase__, model_args=(
torch.randn(1, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ).to(device=UpperCamelCase__, dtype=UpperCamelCase__ ),
False,
), output_path=output_path / '''vae_encoder''' / '''model.onnx''', ordered_input_names=['''sample''', '''return_dict'''], output_names=['''latent_sample'''], dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
}, opset=UpperCamelCase__, )
# VAE DECODER
UpperCamelCase__ = pipeline.vae
UpperCamelCase__ = vae_decoder.config.latent_channels
UpperCamelCase__ = vae_decoder.config.out_channels
# forward only through the decoder part
UpperCamelCase__ = vae_encoder.decode
onnx_export(
UpperCamelCase__, model_args=(
torch.randn(1, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ).to(device=UpperCamelCase__, dtype=UpperCamelCase__ ),
False,
), output_path=output_path / '''vae_decoder''' / '''model.onnx''', ordered_input_names=['''latent_sample''', '''return_dict'''], output_names=['''sample'''], dynamic_axes={
'''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
}, opset=UpperCamelCase__, )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
UpperCamelCase__ = pipeline.safety_checker
UpperCamelCase__ = safety_checker.config.vision_config.num_channels
UpperCamelCase__ = safety_checker.config.vision_config.image_size
UpperCamelCase__ = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker, model_args=(
torch.randn(
1, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, ).to(device=UpperCamelCase__, dtype=UpperCamelCase__ ),
torch.randn(1, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ).to(device=UpperCamelCase__, dtype=UpperCamelCase__ ),
), output_path=output_path / '''safety_checker''' / '''model.onnx''', ordered_input_names=['''clip_input''', '''images'''], output_names=['''out_images''', '''has_nsfw_concepts'''], dynamic_axes={
'''clip_input''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''images''': {0: '''batch''', 1: '''height''', 2: '''width''', 3: '''channels'''},
}, opset=UpperCamelCase__, )
del pipeline.safety_checker
UpperCamelCase__ = OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker''' )
UpperCamelCase__ = pipeline.feature_extractor
else:
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_encoder''' ), vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_decoder''' ), text_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''text_encoder''' ), tokenizer=pipeline.tokenizer, unet=OnnxRuntimeModel.from_pretrained(output_path / '''unet''' ), scheduler=pipeline.scheduler, safety_checker=UpperCamelCase__, feature_extractor=UpperCamelCase__, requires_safety_checker=safety_checker is not None, )
onnx_pipeline.save_pretrained(UpperCamelCase__ )
print('''ONNX pipeline saved to''', UpperCamelCase__ )
del pipeline
del onnx_pipeline
UpperCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase__, provider='''CPUExecutionProvider''' )
print('''ONNX pipeline is loadable''' )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument(
"""--model_path""",
type=str,
required=True,
help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
)
parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--opset""",
default=1_4,
type=int,
help="""The version of the ONNX operator set to use.""",
)
parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")
lowercase = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 35 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase = logging.get_logger(__name__)
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : Any , *_a : Optional[Any] , **_a : Any ):
warnings.warn(
'''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use CLIPImageProcessor instead.''' , _a , )
super().__init__(*_a , **_a )
| 35 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __A :
"""simple docstring"""
def __init__( self ) -> Tuple:
a ={}
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A=1 ) -> Union[str, Any]:
if self.graph.get(__A ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
a =[[w, v]]
if not self.graph.get(__A ):
a =[]
def SCREAMING_SNAKE_CASE ( self ) -> int:
return list(self.graph )
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> Optional[Any]:
if self.graph.get(__A ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__A )
def SCREAMING_SNAKE_CASE ( self , __A=-2 , __A=-1 ) -> List[Any]:
if s == d:
return []
a =[]
a =[]
if s == -2:
a =list(self.graph )[0]
stack.append(__A )
visited.append(__A )
a =s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
a =s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__A )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
a =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__A ) != 0:
a =stack[len(__A ) - 1]
else:
a =ss
# check if se have reached the starting point
if len(__A ) == 0:
return visited
def SCREAMING_SNAKE_CASE ( self , __A=-1 ) -> int:
if c == -1:
a =floor(random() * 1_0000 ) + 10
for i in range(__A ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
a =floor(random() * c ) + 1
if n != i:
self.add_pair(__A , __A , 1 )
def SCREAMING_SNAKE_CASE ( self , __A=-2 ) -> List[str]:
a =deque()
a =[]
if s == -2:
a =list(self.graph )[0]
d.append(__A )
visited.append(__A )
while d:
a =d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def SCREAMING_SNAKE_CASE ( self , __A ) -> Optional[Any]:
a =0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def SCREAMING_SNAKE_CASE ( self , __A ) -> Tuple:
return len(self.graph[u] )
def SCREAMING_SNAKE_CASE ( self , __A=-2 ) -> List[Any]:
a =[]
a =[]
if s == -2:
a =list(self.graph )[0]
stack.append(__A )
visited.append(__A )
a =s
a =[]
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
a =s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
a =node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(__A ) != 0:
a =stack[len(__A ) - 1]
else:
a =ss
# check if se have reached the starting point
if len(__A ) == 0:
return sorted_nodes
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
a =[]
a =[]
a =list(self.graph )[0]
stack.append(__A )
visited.append(__A )
a =-2
a =[]
a =s
a =False
a =set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
a =s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
a =len(__A ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
a =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
a =True
if len(__A ) != 0:
a =stack[len(__A ) - 1]
else:
a =False
indirect_parents.append(__A )
a =s
a =ss
# check if se have reached the starting point
if len(__A ) == 0:
return list(__A )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
a =[]
a =[]
a =list(self.graph )[0]
stack.append(__A )
visited.append(__A )
a =-2
a =[]
a =s
a =False
a =set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
a =s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
a =len(__A ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
a =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
a =True
if len(__A ) != 0:
a =stack[len(__A ) - 1]
else:
a =False
indirect_parents.append(__A )
a =s
a =ss
# check if se have reached the starting point
if len(__A ) == 0:
return False
def SCREAMING_SNAKE_CASE ( self , __A=-2 , __A=-1 ) -> List[str]:
a =time()
self.dfs(__A , __A )
a =time()
return end - begin
def SCREAMING_SNAKE_CASE ( self , __A=-2 ) -> int:
a =time()
self.bfs(__A )
a =time()
return end - begin
class __A :
"""simple docstring"""
def __init__( self ) -> List[str]:
a ={}
def SCREAMING_SNAKE_CASE ( self , __A , __A , __A=1 ) -> Dict:
# check if the u exists
if self.graph.get(__A ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
a =[[w, v]]
# add the other way
if self.graph.get(__A ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
a =[[w, u]]
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> Any:
if self.graph.get(__A ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__A )
# the other way round
if self.graph.get(__A ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(__A )
def SCREAMING_SNAKE_CASE ( self , __A=-2 , __A=-1 ) -> int:
if s == d:
return []
a =[]
a =[]
if s == -2:
a =list(self.graph )[0]
stack.append(__A )
visited.append(__A )
a =s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
a =s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__A )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
a =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__A ) != 0:
a =stack[len(__A ) - 1]
else:
a =ss
# check if se have reached the starting point
if len(__A ) == 0:
return visited
def SCREAMING_SNAKE_CASE ( self , __A=-1 ) -> List[Any]:
if c == -1:
a =floor(random() * 1_0000 ) + 10
for i in range(__A ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
a =floor(random() * c ) + 1
if n != i:
self.add_pair(__A , __A , 1 )
def SCREAMING_SNAKE_CASE ( self , __A=-2 ) -> str:
a =deque()
a =[]
if s == -2:
a =list(self.graph )[0]
d.append(__A )
visited.append(__A )
while d:
a =d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def SCREAMING_SNAKE_CASE ( self , __A ) -> str:
return len(self.graph[u] )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
a =[]
a =[]
a =list(self.graph )[0]
stack.append(__A )
visited.append(__A )
a =-2
a =[]
a =s
a =False
a =set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
a =s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
a =len(__A ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
a =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
a =True
if len(__A ) != 0:
a =stack[len(__A ) - 1]
else:
a =False
indirect_parents.append(__A )
a =s
a =ss
# check if se have reached the starting point
if len(__A ) == 0:
return list(__A )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
a =[]
a =[]
a =list(self.graph )[0]
stack.append(__A )
visited.append(__A )
a =-2
a =[]
a =s
a =False
a =set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
a =s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
a =len(__A ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
a =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
a =True
if len(__A ) != 0:
a =stack[len(__A ) - 1]
else:
a =False
indirect_parents.append(__A )
a =s
a =ss
# check if se have reached the starting point
if len(__A ) == 0:
return False
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
return list(self.graph )
def SCREAMING_SNAKE_CASE ( self , __A=-2 , __A=-1 ) -> Optional[int]:
a =time()
self.dfs(__A , __A )
a =time()
return end - begin
def SCREAMING_SNAKE_CASE ( self , __A=-2 ) -> Dict:
a =time()
self.bfs(__A )
a =time()
return end - begin | 81 |
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 _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , ) -> Optional[int]:
lowerCamelCase_ = parent
lowerCamelCase_ = 13
lowerCamelCase_ = 7
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = 99
lowerCamelCase_ = 32
lowerCamelCase_ = 2
lowerCamelCase_ = 4
lowerCamelCase_ = 37
lowerCamelCase_ = "gelu"
lowerCamelCase_ = 0.1
lowerCamelCase_ = 0.1
lowerCamelCase_ = 512
lowerCamelCase_ = 16
lowerCamelCase_ = 2
lowerCamelCase_ = 0.0_2
lowerCamelCase_ = 3
lowerCamelCase_ = 4
lowerCamelCase_ = None
def SCREAMING_SNAKE_CASE_( self ) -> Any:
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 SCREAMING_SNAKE_CASE_( self ) -> List[str]:
(
(
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 SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Any:
lowerCamelCase_ = TFEsmModel(config=lowercase )
lowerCamelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
lowerCamelCase_ = model(lowercase )
lowerCamelCase_ = [input_ids, input_mask]
lowerCamelCase_ = model(lowercase )
lowerCamelCase_ = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> Tuple:
lowerCamelCase_ = True
lowerCamelCase_ = TFEsmModel(config=lowercase )
lowerCamelCase_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
lowerCamelCase_ = model(lowercase )
lowerCamelCase_ = [input_ids, input_mask]
lowerCamelCase_ = model(lowercase , encoder_hidden_states=lowercase )
# Also check the case where encoder outputs are not passed
lowerCamelCase_ = model(lowercase , attention_mask=lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
lowerCamelCase_ = TFEsmForMaskedLM(config=lowercase )
lowerCamelCase_ = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = TFEsmForTokenClassification(config=lowercase )
lowerCamelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
lowerCamelCase_ = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
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 _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , unittest.TestCase ):
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 SCREAMING_SNAKE_CASE_( self ) -> List[str]:
lowerCamelCase_ = TFEsmModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=lowercase , hidden_size=37 )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase )
@slow
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFEsmModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@unittest.skip("Protein models do not support embedding resizing." )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
pass
@unittest.skip("Protein models do not support embedding resizing." )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
pass
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(lowercase )
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(lowercase , lowercase )
for k, v in name.items():
assert isinstance(lowercase , tf.Variable )
else:
lowerCamelCase_ = model.get_output_embeddings()
assert x is None
lowerCamelCase_ = model.get_bias()
assert name is None
@require_tf
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
lowerCamelCase_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase_ = model(lowercase )[0]
lowerCamelCase_ = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , lowercase )
# compare the actual values for a slice.
lowerCamelCase_ = tf.constant(
[
[
[8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
[-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
[-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-2 ) )
@slow
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
lowerCamelCase_ = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowerCamelCase_ = model(lowercase )[0]
# compare the actual values for a slice.
lowerCamelCase_ = tf.constant(
[
[
[0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
[0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
[0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 19 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = """transfo-xl"""
_snake_case = ["""mems"""]
_snake_case = {
"""n_token""": """vocab_size""",
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , A=2_6_7_7_3_5 , A=[2_0_0_0_0, 4_0_0_0_0, 2_0_0_0_0_0] , A=1_0_2_4 , A=1_0_2_4 , A=1_6 , A=6_4 , A=4_0_9_6 , A=4 , A=False , A=1_8 , A=1_6_0_0 , A=1_0_0_0 , A=True , A=True , A=0 , A=-1 , A=True , A=0.1 , A=0.0 , A=True , A="normal" , A=0.01 , A=0.01 , A=0.02 , A=1e-5 , A=0 , **A , ) -> Optional[int]:
snake_case : Optional[Any] = vocab_size
snake_case : Tuple = []
self.cutoffs.extend(A )
if proj_share_all_but_first:
snake_case : Union[str, Any] = [False] + [True] * len(self.cutoffs )
else:
snake_case : Optional[int] = [False] + [False] * len(self.cutoffs )
snake_case : Union[str, Any] = d_model
snake_case : Optional[Any] = d_embed
snake_case : int = d_head
snake_case : str = d_inner
snake_case : int = div_val
snake_case : Optional[Any] = pre_lnorm
snake_case : Any = n_layer
snake_case : Optional[int] = n_head
snake_case : Tuple = mem_len
snake_case : int = same_length
snake_case : int = attn_type
snake_case : Optional[int] = clamp_len
snake_case : Optional[Any] = sample_softmax
snake_case : str = adaptive
snake_case : List[Any] = dropout
snake_case : Optional[Any] = dropatt
snake_case : List[str] = untie_r
snake_case : List[str] = init
snake_case : Any = init_range
snake_case : Union[str, Any] = proj_init_std
snake_case : Optional[int] = init_std
snake_case : Tuple = layer_norm_epsilon
super().__init__(eos_token_id=A , **A )
@property
def UpperCAmelCase ( self ) -> Dict:
# Message copied from Transformer-XL documentation
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def UpperCAmelCase ( self , A ) -> Tuple:
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 176 |
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,
)
lowerCamelCase : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCamelCase : Dict = '\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)["depth"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline("depth-estimation")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to("cuda")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to("cuda")\n\n\n >>> img = load_image(\n ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/cat.png"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n >>> prompt = "A robot, 4k photo"\n >>> 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"\n\n >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save("robot_cat.png")\n ```\n'
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase=8 ) -> Optional[int]:
snake_case : Dict = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
snake_case : int = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
def __init__( self , A , A , A , ) -> int:
super().__init__()
self.register_modules(
unet=A , scheduler=A , movq=A , )
snake_case : Tuple = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase ( self , A , A , A , A , A , A ) -> str:
if latents is None:
snake_case : List[str] = randn_tensor(A , generator=A , device=A , dtype=A )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
snake_case : Union[str, Any] = latents.to(A )
snake_case : Optional[Any] = latents * scheduler.init_noise_sigma
return latents
def UpperCAmelCase ( self , A=0 ) -> List[str]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
snake_case : Dict = torch.device(f"""cuda:{gpu_id}""" )
snake_case : Tuple = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(A , A )
def UpperCAmelCase ( self , A=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.""" )
snake_case : int = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=A )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
snake_case : Tuple = None
for cpu_offloaded_model in [self.unet, self.movq]:
snake_case , snake_case : Tuple = cpu_offload_with_hook(A , A , prev_module_hook=A )
# We'll offload the last model manually.
snake_case : Tuple = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase ( self ) -> str:
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(A , """_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(A )
def __call__( self , A , A , A , A = 5_1_2 , A = 5_1_2 , A = 1_0_0 , A = 4.0 , A = 1 , A = None , A = None , A = "pil" , A = True , ) -> Any:
snake_case : int = self._execution_device
snake_case : Tuple = guidance_scale > 1.0
if isinstance(A , A ):
snake_case : str = torch.cat(A , dim=0 )
if isinstance(A , A ):
snake_case : int = torch.cat(A , dim=0 )
if isinstance(A , A ):
snake_case : Tuple = torch.cat(A , dim=0 )
snake_case : List[Any] = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
snake_case : Optional[int] = image_embeds.repeat_interleave(A , dim=0 )
snake_case : Dict = negative_image_embeds.repeat_interleave(A , dim=0 )
snake_case : Optional[Any] = hint.repeat_interleave(A , dim=0 )
snake_case : List[str] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=A )
snake_case : Optional[Any] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=A )
self.scheduler.set_timesteps(A , device=A )
snake_case : Tuple = self.scheduler.timesteps
snake_case : Tuple = self.movq.config.latent_channels
snake_case , snake_case : Optional[int] = downscale_height_and_width(A , A , self.movq_scale_factor )
# create initial latent
snake_case : str = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , A , A , A , self.scheduler , )
for i, t in enumerate(self.progress_bar(A ) ):
# expand the latents if we are doing classifier free guidance
snake_case : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
snake_case : Any = {"""image_embeds""": image_embeds, """hint""": hint}
snake_case : List[Any] = self.unet(
sample=A , timestep=A , encoder_hidden_states=A , added_cond_kwargs=A , return_dict=A , )[0]
if do_classifier_free_guidance:
snake_case , snake_case : Union[str, Any] = noise_pred.split(latents.shape[1] , dim=1 )
snake_case , snake_case : Tuple = noise_pred.chunk(2 )
snake_case , snake_case : Optional[Any] = variance_pred.chunk(2 )
snake_case : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
snake_case : 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"]
):
snake_case , snake_case : int = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
snake_case : Dict = self.scheduler.step(
A , A , A , generator=A , )[0]
# post-processing
snake_case : Tuple = self.movq.decode(A , force_not_quantize=A )["""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"]:
snake_case : Optional[int] = image * 0.5 + 0.5
snake_case : Union[str, Any] = image.clamp(0 , 1 )
snake_case : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case : Tuple = self.numpy_to_pil(A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A )
| 176 | 1 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
if num < 0:
return False
__lowerCAmelCase = num
__lowerCAmelCase = 0
while num > 0:
__lowerCAmelCase = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : Optional[Any] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase : Optional[Any] = DPTConfig(embedding_type='hybrid' )
if "large" in checkpoint_url:
lowerCAmelCase : Optional[int] = 1_024
lowerCAmelCase : Tuple = 4_096
lowerCAmelCase : Optional[int] = 24
lowerCAmelCase : Optional[int] = 16
lowerCAmelCase : str = [5, 11, 17, 23]
lowerCAmelCase : Tuple = [256, 512, 1_024, 1_024]
lowerCAmelCase : Optional[int] = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
lowerCAmelCase : Optional[int] = 768
lowerCAmelCase : int = [1, 1, 1, 0.5]
lowerCAmelCase : List[Any] = [256, 512, 768, 768]
lowerCAmelCase : List[Any] = 150
lowerCAmelCase : Optional[Any] = 16
lowerCAmelCase : Union[str, Any] = (1, 384, 384)
lowerCAmelCase : Tuple = False
lowerCAmelCase : List[str] = 'project'
if "ade" in checkpoint_url:
lowerCAmelCase : Tuple = True
lowerCAmelCase : str = 768
lowerCAmelCase : List[str] = [1, 1, 1, 0.5]
lowerCAmelCase : Optional[Any] = 150
lowerCAmelCase : List[str] = 16
lowerCAmelCase : Dict = 'huggingface/label-files'
lowerCAmelCase : Optional[Any] = 'ade20k-id2label.json'
lowerCAmelCase : Tuple = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase, _UpperCAmelCase, repo_type='dataset' ) ), 'r' ) )
lowerCAmelCase : Optional[Any] = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
lowerCAmelCase : int = idalabel
lowerCAmelCase : str = {v: k for k, v in idalabel.items()}
lowerCAmelCase : int = [1, 150, 480, 480]
return config, expected_shape
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
lowerCAmelCase : List[str] = ['pretrained.model.head.weight', 'pretrained.model.head.bias']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase, _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Tuple:
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
lowerCAmelCase : Optional[int] = name.replace('pretrained.model', 'dpt.encoder' )
if "pretrained.model" in name:
lowerCAmelCase : Dict = name.replace('pretrained.model', 'dpt.embeddings' )
if "patch_embed" in name:
lowerCAmelCase : int = name.replace('patch_embed', '' )
if "pos_embed" in name:
lowerCAmelCase : Any = name.replace('pos_embed', 'position_embeddings' )
if "attn.proj" in name:
lowerCAmelCase : str = name.replace('attn.proj', 'attention.output.dense' )
if "proj" in name and "project" not in name:
lowerCAmelCase : Union[str, Any] = name.replace('proj', 'projection' )
if "blocks" in name:
lowerCAmelCase : List[str] = name.replace('blocks', 'layer' )
if "mlp.fc1" in name:
lowerCAmelCase : Optional[Any] = name.replace('mlp.fc1', 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCAmelCase : Any = name.replace('mlp.fc2', 'output.dense' )
if "norm1" in name and "backbone" not in name:
lowerCAmelCase : List[str] = name.replace('norm1', 'layernorm_before' )
if "norm2" in name and "backbone" not in name:
lowerCAmelCase : str = name.replace('norm2', 'layernorm_after' )
if "scratch.output_conv" in name:
lowerCAmelCase : int = name.replace('scratch.output_conv', 'head' )
if "scratch" in name:
lowerCAmelCase : Optional[int] = name.replace('scratch', 'neck' )
if "layer1_rn" in name:
lowerCAmelCase : int = name.replace('layer1_rn', 'convs.0' )
if "layer2_rn" in name:
lowerCAmelCase : Optional[Any] = name.replace('layer2_rn', 'convs.1' )
if "layer3_rn" in name:
lowerCAmelCase : List[str] = name.replace('layer3_rn', 'convs.2' )
if "layer4_rn" in name:
lowerCAmelCase : int = name.replace('layer4_rn', 'convs.3' )
if "refinenet" in name:
lowerCAmelCase : Optional[int] = int(name[len('neck.refinenet' ) : len('neck.refinenet' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
lowerCAmelCase : Any = name.replace(f"refinenet{layer_idx}", f"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
lowerCAmelCase : Dict = name.replace('out_conv', 'projection' )
if "resConfUnit1" in name:
lowerCAmelCase : Optional[int] = name.replace('resConfUnit1', 'residual_layer1' )
if "resConfUnit2" in name:
lowerCAmelCase : List[str] = name.replace('resConfUnit2', 'residual_layer2' )
if "conv1" in name:
lowerCAmelCase : List[Any] = name.replace('conv1', 'convolution1' )
if "conv2" in name:
lowerCAmelCase : Optional[int] = name.replace('conv2', 'convolution2' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
lowerCAmelCase : Union[str, Any] = name.replace('pretrained.act_postprocess1.0.project.0', 'neck.reassemble_stage.readout_projects.0.0' )
if "pretrained.act_postprocess2.0.project.0" in name:
lowerCAmelCase : Optional[Any] = name.replace('pretrained.act_postprocess2.0.project.0', 'neck.reassemble_stage.readout_projects.1.0' )
if "pretrained.act_postprocess3.0.project.0" in name:
lowerCAmelCase : List[Any] = name.replace('pretrained.act_postprocess3.0.project.0', 'neck.reassemble_stage.readout_projects.2.0' )
if "pretrained.act_postprocess4.0.project.0" in name:
lowerCAmelCase : Optional[Any] = name.replace('pretrained.act_postprocess4.0.project.0', 'neck.reassemble_stage.readout_projects.3.0' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
lowerCAmelCase : Tuple = name.replace('pretrained.act_postprocess1.3', 'neck.reassemble_stage.layers.0.projection' )
if "pretrained.act_postprocess1.4" in name:
lowerCAmelCase : str = name.replace('pretrained.act_postprocess1.4', 'neck.reassemble_stage.layers.0.resize' )
if "pretrained.act_postprocess2.3" in name:
lowerCAmelCase : int = name.replace('pretrained.act_postprocess2.3', 'neck.reassemble_stage.layers.1.projection' )
if "pretrained.act_postprocess2.4" in name:
lowerCAmelCase : Optional[Any] = name.replace('pretrained.act_postprocess2.4', 'neck.reassemble_stage.layers.1.resize' )
if "pretrained.act_postprocess3.3" in name:
lowerCAmelCase : List[str] = name.replace('pretrained.act_postprocess3.3', 'neck.reassemble_stage.layers.2.projection' )
if "pretrained.act_postprocess4.3" in name:
lowerCAmelCase : List[str] = name.replace('pretrained.act_postprocess4.3', 'neck.reassemble_stage.layers.3.projection' )
if "pretrained.act_postprocess4.4" in name:
lowerCAmelCase : List[str] = name.replace('pretrained.act_postprocess4.4', 'neck.reassemble_stage.layers.3.resize' )
if "pretrained" in name:
lowerCAmelCase : int = name.replace('pretrained', 'dpt' )
if "bn" in name:
lowerCAmelCase : List[str] = name.replace('bn', 'batch_norm' )
if "head" in name:
lowerCAmelCase : Any = name.replace('head', 'head.head' )
if "encoder.norm" in name:
lowerCAmelCase : Dict = name.replace('encoder.norm', 'layernorm' )
if "auxlayer" in name:
lowerCAmelCase : Tuple = name.replace('auxlayer', 'auxiliary_head.head' )
if "backbone" in name:
lowerCAmelCase : Tuple = name.replace('backbone', 'backbone.bit.encoder' )
if ".." in name:
lowerCAmelCase : Optional[Any] = name.replace('..', '.' )
if "stem.conv" in name:
lowerCAmelCase : List[str] = name.replace('stem.conv', 'bit.embedder.convolution' )
if "blocks" in name:
lowerCAmelCase : Dict = name.replace('blocks', 'layers' )
if "convolution" in name and "backbone" in name:
lowerCAmelCase : Dict = name.replace('convolution', 'conv' )
if "layer" in name and "backbone" in name:
lowerCAmelCase : Dict = name.replace('layer', 'layers' )
if "backbone.bit.encoder.bit" in name:
lowerCAmelCase : List[str] = name.replace('backbone.bit.encoder.bit', 'backbone.bit' )
if "embedder.conv" in name:
lowerCAmelCase : Any = name.replace('embedder.conv', 'embedder.convolution' )
if "backbone.bit.encoder.stem.norm" in name:
lowerCAmelCase : Optional[int] = name.replace('backbone.bit.encoder.stem.norm', 'backbone.bit.embedder.norm' )
return name
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCAmelCase : List[Any] = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight" )
lowerCAmelCase : int = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase : Union[str, Any] = in_proj_weight[: config.hidden_size, :]
lowerCAmelCase : Dict = in_proj_bias[: config.hidden_size]
lowerCAmelCase : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase : Any = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCAmelCase : str = in_proj_weight[
-config.hidden_size :, :
]
lowerCAmelCase : List[str] = in_proj_bias[-config.hidden_size :]
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase : Optional[int] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowerCAmelCase : Tuple = Image.open(requests.get(_UpperCAmelCase, stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase , lowerCAmelCase : List[str] = get_dpt_config(_UpperCAmelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
lowerCAmelCase : str = torch.load(_UpperCAmelCase, map_location='cpu' )
# remove certain keys
remove_ignore_keys_(_UpperCAmelCase )
# rename keys
for key in state_dict.copy().keys():
lowerCAmelCase : str = state_dict.pop(_UpperCAmelCase )
lowerCAmelCase : int = val
# read in qkv matrices
read_in_q_k_v(_UpperCAmelCase, _UpperCAmelCase )
# load HuggingFace model
lowerCAmelCase : int = DPTForSemanticSegmentation(_UpperCAmelCase ) if 'ade' in checkpoint_url else DPTForDepthEstimation(_UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
model.eval()
# Check outputs on an image
lowerCAmelCase : str = 480 if 'ade' in checkpoint_url else 384
lowerCAmelCase : Dict = DPTImageProcessor(size=_UpperCAmelCase )
lowerCAmelCase : Union[str, Any] = prepare_img()
lowerCAmelCase : Union[str, Any] = image_processor(_UpperCAmelCase, return_tensors='pt' )
# forward pass
lowerCAmelCase : Optional[Any] = model(**_UpperCAmelCase ).logits if 'ade' in checkpoint_url else model(**_UpperCAmelCase ).predicted_depth
if show_prediction:
lowerCAmelCase : str = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ), size=(image.size[1], image.size[0]), mode='bicubic', align_corners=_UpperCAmelCase, )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(_UpperCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
model.push_to_hub('ybelkada/dpt-hybrid-midas' )
image_processor.push_to_hub('ybelkada/dpt-hybrid-midas' )
if __name__ == "__main__":
__A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
type=str,
help='''URL of the original DPT checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=False,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
)
parser.add_argument(
'''--model_name''',
default='''dpt-large''',
type=str,
help='''Name of the model, in case you\'re pushing to the hub.''',
)
parser.add_argument(
'''--show_prediction''',
action='''store_true''',
)
__A : Dict = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 138 | 0 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCamelCase_ ( pl.LightningModule):
"""simple docstring"""
def __init__( self : str , UpperCAmelCase__ : List[str] ) -> Union[str, Any]:
super().__init__()
__SCREAMING_SNAKE_CASE = model
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = nn.Linear(self.model.config.hidden_size , self.num_labels )
def UpperCAmelCase_ ( self : int ) -> Any:
pass
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = LongformerModel.from_pretrained(__A )
__SCREAMING_SNAKE_CASE = LightningModel(__A )
__SCREAMING_SNAKE_CASE = torch.load(__A , map_location=torch.device("cpu" ) )
lightning_model.load_state_dict(ckpt["state_dict"] )
# init longformer question answering model
__SCREAMING_SNAKE_CASE = LongformerForQuestionAnswering.from_pretrained(__A )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__A )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
a__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
a__ : List[str] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 357 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
a__ : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase__ : WhisperForConditionalGeneration , UpperCAmelCase__ : WhisperProcessor , UpperCAmelCase__ : AutoencoderKL , UpperCAmelCase__ : CLIPTextModel , UpperCAmelCase__ : CLIPTokenizer , UpperCAmelCase__ : UNetaDConditionModel , UpperCAmelCase__ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , UpperCAmelCase__ : StableDiffusionSafetyChecker , UpperCAmelCase__ : CLIPImageProcessor , ) -> Optional[int]:
super().__init__()
if safety_checker is None:
logger.warning(
F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )
self.register_modules(
speech_model=UpperCAmelCase__ , speech_processor=UpperCAmelCase__ , vae=UpperCAmelCase__ , text_encoder=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ , feature_extractor=UpperCAmelCase__ , )
def UpperCAmelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Optional[Union[str, int]] = "auto" ) -> str:
if slice_size == "auto":
__SCREAMING_SNAKE_CASE = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> List[Any]:
self.enable_attention_slicing(UpperCAmelCase__ )
@torch.no_grad()
def __call__( self : str , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str=1_6_0_0_0 , UpperCAmelCase__ : int = 5_1_2 , UpperCAmelCase__ : int = 5_1_2 , UpperCAmelCase__ : int = 5_0 , UpperCAmelCase__ : float = 7.5 , UpperCAmelCase__ : Optional[Union[str, List[str]]] = None , UpperCAmelCase__ : Optional[int] = 1 , UpperCAmelCase__ : float = 0.0 , UpperCAmelCase__ : Optional[torch.Generator] = None , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[str] = "pil" , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase__ : int = 1 , **UpperCAmelCase__ : Dict , ) -> Any:
__SCREAMING_SNAKE_CASE = self.speech_processor.feature_extractor(
UpperCAmelCase__ , return_tensors="pt" , sampling_rate=UpperCAmelCase__ ).input_features.to(self.device )
__SCREAMING_SNAKE_CASE = self.speech_model.generate(UpperCAmelCase__ , max_length=4_8_0_0_0_0 )
__SCREAMING_SNAKE_CASE = self.speech_processor.tokenizer.batch_decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ , normalize=UpperCAmelCase__ )[
0
]
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = 1
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
else:
raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase__ )}""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or callback_steps <= 0)
):
raise ValueError(
F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
F""" {type(UpperCAmelCase__ )}.""" )
# get prompt text embeddings
__SCREAMING_SNAKE_CASE = self.tokenizer(
UpperCAmelCase__ , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
__SCREAMING_SNAKE_CASE = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
F""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
__SCREAMING_SNAKE_CASE = text_input_ids[:, : self.tokenizer.model_max_length]
__SCREAMING_SNAKE_CASE = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = text_embeddings.shape
__SCREAMING_SNAKE_CASE = text_embeddings.repeat(1 , UpperCAmelCase__ , 1 )
__SCREAMING_SNAKE_CASE = text_embeddings.view(bs_embed * num_images_per_prompt , UpperCAmelCase__ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
__SCREAMING_SNAKE_CASE = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE = 42
if negative_prompt is None:
__SCREAMING_SNAKE_CASE = [""] * batch_size
elif type(UpperCAmelCase__ ) is not type(UpperCAmelCase__ ):
raise TypeError(
F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase__ )} !="""
F""" {type(UpperCAmelCase__ )}.""" )
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = [negative_prompt]
elif batch_size != len(UpperCAmelCase__ ):
raise ValueError(
F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase__ )}, but `prompt`:"""
F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
" the batch size of `prompt`." )
else:
__SCREAMING_SNAKE_CASE = negative_prompt
__SCREAMING_SNAKE_CASE = text_input_ids.shape[-1]
__SCREAMING_SNAKE_CASE = self.tokenizer(
UpperCAmelCase__ , padding="max_length" , max_length=UpperCAmelCase__ , truncation=UpperCAmelCase__ , return_tensors="pt" , )
__SCREAMING_SNAKE_CASE = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__SCREAMING_SNAKE_CASE = uncond_embeddings.shape[1]
__SCREAMING_SNAKE_CASE = uncond_embeddings.repeat(1 , UpperCAmelCase__ , 1 )
__SCREAMING_SNAKE_CASE = uncond_embeddings.view(batch_size * num_images_per_prompt , UpperCAmelCase__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__SCREAMING_SNAKE_CASE = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
__SCREAMING_SNAKE_CASE = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
__SCREAMING_SNAKE_CASE = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
__SCREAMING_SNAKE_CASE = torch.randn(UpperCAmelCase__ , generator=UpperCAmelCase__ , device="cpu" , dtype=UpperCAmelCase__ ).to(
self.device )
else:
__SCREAMING_SNAKE_CASE = torch.randn(UpperCAmelCase__ , generator=UpperCAmelCase__ , device=self.device , dtype=UpperCAmelCase__ )
else:
if latents.shape != latents_shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
__SCREAMING_SNAKE_CASE = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(UpperCAmelCase__ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
__SCREAMING_SNAKE_CASE = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__SCREAMING_SNAKE_CASE = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__SCREAMING_SNAKE_CASE = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__SCREAMING_SNAKE_CASE = {}
if accepts_eta:
__SCREAMING_SNAKE_CASE = eta
for i, t in enumerate(self.progress_bar(UpperCAmelCase__ ) ):
# expand the latents if we are doing classifier free guidance
__SCREAMING_SNAKE_CASE = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__SCREAMING_SNAKE_CASE = self.scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
# predict the noise residual
__SCREAMING_SNAKE_CASE = self.unet(UpperCAmelCase__ , UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ ).sample
# perform guidance
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = noise_pred.chunk(2 )
__SCREAMING_SNAKE_CASE = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
__SCREAMING_SNAKE_CASE = self.scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = 1 / 0.18_215 * latents
__SCREAMING_SNAKE_CASE = self.vae.decode(UpperCAmelCase__ ).sample
__SCREAMING_SNAKE_CASE = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__SCREAMING_SNAKE_CASE = self.numpy_to_pil(UpperCAmelCase__ )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=UpperCAmelCase__ , nsfw_content_detected=UpperCAmelCase__ )
| 195 | 0 |
from __future__ import annotations
def lowercase_ ( _lowerCamelCase : list[int]):
if len(_lowerCamelCase) == 0:
return array
lowercase__ , lowercase__ : Any = min(_lowerCamelCase), max(_lowerCamelCase)
# Compute the variables
lowercase__ : List[str] = _max - _min + 1
lowercase__ , lowercase__ : Optional[Any] = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
lowercase__ : int = i - _min
lowercase__ : Any = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
lowercase__ : Any = 0
for i in range(_lowerCamelCase):
while holes_repeat[i] > 0:
lowercase__ : List[str] = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase = input('''Enter numbers separated by comma:\n''')
UpperCamelCase = [int(x) for x in user_input.split(''',''')]
print(pigeon_sort(unsorted))
| 87 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'IBertForMaskedLM',
'IBertForMultipleChoice',
'IBertForQuestionAnswering',
'IBertForSequenceClassification',
'IBertForTokenClassification',
'IBertModel',
'IBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
A_ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 192 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> List[str]:
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
SCREAMING_SNAKE_CASE :Tuple = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class __magic_name__ ( snake_case ):
@staticmethod
def UpperCAmelCase_ ( _lowercase )-> Optional[int]:
UpperCamelCase_ = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=_lowercase , required=_lowercase , help="Model's type." )
train_parser.add_argument(
"--tf_checkpoint" , type=_lowercase , required=_lowercase , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=_lowercase , required=_lowercase , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=_lowercase , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=_lowercase , default=_lowercase , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=_lowercase )
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , *_lowercase , )-> Union[str, Any]:
UpperCamelCase_ = logging.get_logger("transformers-cli/converting" )
self._logger.info(F"Loading model {model_type}" )
UpperCamelCase_ = model_type
UpperCamelCase_ = tf_checkpoint
UpperCamelCase_ = pytorch_dump_output
UpperCamelCase_ = config
UpperCamelCase_ = finetuning_task_name
def UpperCAmelCase_ ( self )-> Optional[int]:
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowercase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowercase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowercase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(_lowercase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowercase )
if "ckpt" in self._tf_checkpoint.lower():
UpperCamelCase_ = self._tf_checkpoint
UpperCamelCase_ = ""
else:
UpperCamelCase_ = self._tf_checkpoint
UpperCamelCase_ = ""
convert_transfo_xl_checkpoint_to_pytorch(
_lowercase , self._config , self._pytorch_dump_output , _lowercase )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowercase )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowercase )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 60 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False )-> str:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = f"Expected string as input, found {type(SCREAMING_SNAKE_CASE_ )}"
raise ValueError(SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = f"Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_ )}"
raise ValueError(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = input_str.split("_" )
UpperCamelCase_ = 0 if use_pascal else 1
UpperCamelCase_ = words[start_index:]
UpperCamelCase_ = [word[0].upper() + word[1:] for word in words_to_capitalize]
UpperCamelCase_ = "" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 60 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class snake_case ( unittest.TestCase ):
"""simple docstring"""
snake_case__ = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
snake_case__ = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Any ):
UpperCAmelCase__ = TextaTextGenerationPipeline(model=lowerCamelCase__ ,tokenizer=lowerCamelCase__ )
return generator, ["Something to write", "Something else"]
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Optional[Any] ):
UpperCAmelCase__ = generator('Something there' )
self.assertEqual(lowerCamelCase__ ,[{'generated_text': ANY(lowerCamelCase__ )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
UpperCAmelCase__ = generator(['This is great !', 'Something else'] ,num_return_sequences=2 ,do_sample=lowerCamelCase__ )
self.assertEqual(
lowerCamelCase__ ,[
[{'generated_text': ANY(lowerCamelCase__ )}, {'generated_text': ANY(lowerCamelCase__ )}],
[{'generated_text': ANY(lowerCamelCase__ )}, {'generated_text': ANY(lowerCamelCase__ )}],
] ,)
UpperCAmelCase__ = generator(
['This is great !', 'Something else'] ,num_return_sequences=2 ,batch_size=2 ,do_sample=lowerCamelCase__ )
self.assertEqual(
lowerCamelCase__ ,[
[{'generated_text': ANY(lowerCamelCase__ )}, {'generated_text': ANY(lowerCamelCase__ )}],
[{'generated_text': ANY(lowerCamelCase__ )}, {'generated_text': ANY(lowerCamelCase__ )}],
] ,)
with self.assertRaises(lowerCamelCase__ ):
generator(4 )
@require_torch
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = pipeline('text2text-generation' ,model='patrickvonplaten/t5-tiny-random' ,framework='pt' )
# do_sample=False necessary for reproducibility
UpperCAmelCase__ = generator('Something there' ,do_sample=lowerCamelCase__ )
self.assertEqual(lowerCamelCase__ ,[{'generated_text': ''}] )
UpperCAmelCase__ = 3
UpperCAmelCase__ = generator(
'Something there' ,num_return_sequences=lowerCamelCase__ ,num_beams=lowerCamelCase__ ,)
UpperCAmelCase__ = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
UpperCAmelCase__ = generator('This is a test' ,do_sample=lowerCamelCase__ ,num_return_sequences=2 ,return_tensors=lowerCamelCase__ )
self.assertEqual(
lowerCamelCase__ ,[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] ,)
UpperCAmelCase__ = generator.model.config.eos_token_id
UpperCAmelCase__ = '<pad>'
UpperCAmelCase__ = generator(
['This is a test', 'This is a second test'] ,do_sample=lowerCamelCase__ ,num_return_sequences=2 ,batch_size=2 ,return_tensors=lowerCamelCase__ ,)
self.assertEqual(
lowerCamelCase__ ,[
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] ,)
@require_tf
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = pipeline('text2text-generation' ,model='patrickvonplaten/t5-tiny-random' ,framework='tf' )
# do_sample=False necessary for reproducibility
UpperCAmelCase__ = generator('Something there' ,do_sample=lowerCamelCase__ )
self.assertEqual(lowerCamelCase__ ,[{'generated_text': ''}] )
| 98 | """simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase__ : Optional[Any] = logging.getLogger()
def a_ ( ):
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('-f' )
UpperCAmelCase__ = parser.parse_args()
return args.f
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = logging.StreamHandler(sys.stdout )
logger.addHandler(lowerCamelCase__ )
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ):
UpperCAmelCase__ = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 ,'run_glue_deebert.py' )
with patch.object(lowerCamelCase__ ,'argv' ,lowerCamelCase__ ):
UpperCAmelCase__ = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(lowerCamelCase__ ,0.6_6_6 )
@slow
@require_torch_non_multi_gpu
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n '.split()
self.run_and_check(lowerCamelCase__ )
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n '.split()
self.run_and_check(lowerCamelCase__ )
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n '.split()
self.run_and_check(lowerCamelCase__ )
| 98 | 1 |
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE_:Any = """docs/source/en/_toctree.yml"""
def __UpperCamelCase ( _lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
A : List[Any] = defaultdict(_lowerCAmelCase )
for doc in model_doc:
counts[doc["local"]] += 1
A : Dict = [key for key, value in counts.items() if value > 1]
A : Optional[int] = []
for duplicate_key in duplicates:
A : Dict = list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key} )
if len(_lowerCAmelCase ) > 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(_lowerCAmelCase , key=lambda _lowerCAmelCase : s["title"].lower() )
def __UpperCamelCase ( _lowerCAmelCase=False ) -> Tuple:
"""simple docstring"""
with open(_lowerCAmelCase , encoding="""utf-8""" ) as f:
A : List[Any] = yaml.safe_load(f.read() )
# Get to the API doc
A : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
A : Dict = content[api_idx]["""sections"""]
# Then to the model doc
A : int = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
A : Union[str, Any] = api_doc[model_idx]["""sections"""]
A : List[Any] = [(idx, section) for idx, section in enumerate(_lowerCAmelCase ) if """sections""" in section]
A : Optional[Any] = False
for idx, modality_doc in modalities_docs:
A : Union[str, Any] = modality_doc["""sections"""]
A : List[str] = clean_model_doc_toc(_lowerCAmelCase )
if old_modality_doc != new_modality_doc:
A : Optional[Any] = True
if overwrite:
A : Dict = new_modality_doc
if diff:
if overwrite:
A : str = model_doc
A : List[str] = api_doc
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(_lowerCAmelCase , allow_unicode=_lowerCAmelCase ) )
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__":
SCREAMING_SNAKE_CASE_:Dict = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
SCREAMING_SNAKE_CASE_:Dict = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 115 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
A : str = tempfile.mkdtemp()
A : Any = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""的""",
"""价""",
"""格""",
"""是""",
"""15""",
"""便""",
"""alex""",
"""##andra""",
"""，""",
"""。""",
"""-""",
"""t""",
"""shirt""",
]
A : Any = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file, """w""", encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
A : Any = {
"""do_resize""": True,
"""size""": {"""height""": 224, """width""": 224},
"""do_center_crop""": True,
"""crop_size""": {"""height""": 18, """width""": 18},
"""do_normalize""": True,
"""image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
"""image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
"""do_convert_rgb""": True,
}
A : int = os.path.join(self.tmpdirname, lowerCamelCase__ )
with open(self.image_processor_file, """w""", encoding="""utf-8""" ) as fp:
json.dump(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return BertTokenizer.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return BertTokenizerFast.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self ):
shutil.rmtree(self.tmpdirname )
def _lowerCAmelCase ( self ):
A : Optional[int] = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta )]
A : List[Any] = [Image.fromarray(np.moveaxis(lowerCamelCase__, 0, -1 ) ) for x in image_inputs]
return image_inputs
def _lowerCAmelCase ( self ):
A : List[Any] = self.get_tokenizer()
A : Dict = self.get_rust_tokenizer()
A : List[Any] = self.get_image_processor()
A : int = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
A : Optional[Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname, use_fast=lowerCamelCase__ )
A : str = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
A : Tuple = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer, lowerCamelCase__ )
self.assertIsInstance(processor_fast.tokenizer, lowerCamelCase__ )
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor, lowerCamelCase__ )
self.assertIsInstance(processor_fast.image_processor, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : str = ChineseCLIPProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A : Optional[int] = self.get_tokenizer(cls_token="""(CLS)""", sep_token="""(SEP)""" )
A : Optional[int] = self.get_image_processor(do_normalize=lowerCamelCase__ )
A : Optional[int] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname, cls_token="""(CLS)""", sep_token="""(SEP)""", do_normalize=lowerCamelCase__ )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, lowerCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : Tuple = self.get_image_processor()
A : List[str] = self.get_tokenizer()
A : List[str] = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : Any = self.prepare_image_inputs()
A : Union[str, Any] = image_processor(lowerCamelCase__, return_tensors="""np""" )
A : str = processor(images=lowerCamelCase__, 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 _lowerCAmelCase ( self ):
A : List[str] = self.get_image_processor()
A : Tuple = self.get_tokenizer()
A : str = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : Any = """Alexandra，T-shirt的价格是15便士。"""
A : Optional[Any] = processor(text=lowerCamelCase__ )
A : int = tokenizer(lowerCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def _lowerCAmelCase ( self ):
A : Dict = self.get_image_processor()
A : List[str] = self.get_tokenizer()
A : int = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : str = """Alexandra，T-shirt的价格是15便士。"""
A : Dict = self.prepare_image_inputs()
A : Optional[int] = processor(text=lowerCamelCase__, images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ), ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def _lowerCAmelCase ( self ):
A : Union[str, Any] = self.get_image_processor()
A : List[str] = self.get_tokenizer()
A : List[str] = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A : List[Any] = processor.batch_decode(lowerCamelCase__ )
A : str = tokenizer.batch_decode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : List[str] = self.get_image_processor()
A : List[str] = self.get_tokenizer()
A : Any = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : List[Any] = """Alexandra，T-shirt的价格是15便士。"""
A : Optional[Any] = self.prepare_image_inputs()
A : str = processor(text=lowerCamelCase__, images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ), processor.model_input_names )
| 115 | 1 |
'''simple docstring'''
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
A ={
"debug": logging.DEBUG,
"info": logging.INFO,
"warning": logging.WARNING,
"error": logging.ERROR,
"critical": logging.CRITICAL,
}
A =logging.WARNING
def snake_case_ ():
UpperCAmelCase = os.getenv('''DATASETS_VERBOSITY''' , _SCREAMING_SNAKE_CASE )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"Unknown option DATASETS_VERBOSITY={env_level_str}, "
F"has to be one of: { ', '.join(log_levels.keys() ) }" )
return _default_log_level
def snake_case_ ():
return __name__.split('''.''' )[0]
def snake_case_ ():
return logging.getLogger(_get_library_name() )
def snake_case_ ():
UpperCAmelCase = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def snake_case_ ():
UpperCAmelCase = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def snake_case_ (_a : Optional[str] = None ):
if name is None:
UpperCAmelCase = _get_library_name()
return logging.getLogger(_SCREAMING_SNAKE_CASE )
def snake_case_ ():
return _get_library_root_logger().getEffectiveLevel()
def snake_case_ (_a : int ):
_get_library_root_logger().setLevel(_SCREAMING_SNAKE_CASE )
def snake_case_ ():
return set_verbosity(_SCREAMING_SNAKE_CASE )
def snake_case_ ():
return set_verbosity(_SCREAMING_SNAKE_CASE )
def snake_case_ ():
return set_verbosity(_SCREAMING_SNAKE_CASE )
def snake_case_ ():
return set_verbosity(_SCREAMING_SNAKE_CASE )
def snake_case_ ():
UpperCAmelCase = False
def snake_case_ ():
UpperCAmelCase = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class _a :
def __init__( self : Any , *lowercase : Tuple , **lowercase : Tuple ): # pylint: disable=unused-argument
'''simple docstring'''
UpperCAmelCase = args[0] if args else None
def __iter__( self : Union[str, Any] ):
'''simple docstring'''
return iter(self._iterator )
def __getattr__( self : List[str] , lowercase : Optional[Any] ):
'''simple docstring'''
def empty_fn(*lowercase : List[str] , **lowercase : Union[str, Any] ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : str ):
'''simple docstring'''
return self
def __exit__( self : Any , lowercase : List[str] , lowercase : Tuple , lowercase : str ):
'''simple docstring'''
return
A =True
class _a :
def __call__( self : Union[str, Any] , *lowercase : Optional[int] , lowercase : List[Any]=False , **lowercase : List[Any] ):
'''simple docstring'''
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*__UpperCamelCase , **__UpperCamelCase )
else:
return EmptyTqdm(*__UpperCamelCase , **__UpperCamelCase )
def A ( self : Optional[int] , *lowercase : List[str] , **lowercase : Tuple ):
'''simple docstring'''
UpperCAmelCase = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*__UpperCamelCase , **__UpperCamelCase )
def A ( self : Optional[int] ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
A =_tqdm_cls()
def snake_case_ ():
global _tqdm_active
return bool(_tqdm_active )
def snake_case_ ():
global _tqdm_active
UpperCAmelCase = True
def snake_case_ ():
global _tqdm_active
UpperCAmelCase = False
| 34 |
"""simple docstring"""
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 : Tuple = logging.get_logger(__name__)
__A : List[str] = {
"sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """poolformer"""
def __init__( self : List[str] , __UpperCamelCase : int=3 , __UpperCamelCase : List[Any]=1_6 , __UpperCamelCase : str=1_6 , __UpperCamelCase : List[Any]=3 , __UpperCamelCase : int=4.0 , __UpperCamelCase : str=[2, 2, 6, 2] , __UpperCamelCase : Tuple=[6_4, 1_2_8, 3_2_0, 5_1_2] , __UpperCamelCase : int=[7, 3, 3, 3] , __UpperCamelCase : str=[4, 2, 2, 2] , __UpperCamelCase : Union[str, Any]=[2, 1, 1, 1] , __UpperCamelCase : List[str]=4 , __UpperCamelCase : List[str]=0.0 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : List[str]=True , __UpperCamelCase : Union[str, Any]=1e-5 , __UpperCamelCase : str=0.0_2 , **__UpperCamelCase : List[Any] , )->Dict:
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_size
_UpperCAmelCase = stride
_UpperCAmelCase = padding
_UpperCAmelCase = pool_size
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = depths
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = strides
_UpperCAmelCase = num_encoder_blocks
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_layer_scale
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = initializer_range
super().__init__(**__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = version.parse("""1.11""")
@property
def lowercase__ ( self : Union[str, Any] )->Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowercase__ ( self : Tuple )->float:
return 2e-3
| 260 | 0 |
import unittest
from transformers import 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class UpperCamelCase_ :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=13 , lowerCAmelCase_=7 , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=99 , lowerCAmelCase_=32 , lowerCAmelCase_=5 , lowerCAmelCase_=4 , lowerCAmelCase_=37 , lowerCAmelCase_="gelu" , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.1 , lowerCAmelCase_=512 , lowerCAmelCase_=16 , lowerCAmelCase_=2 , lowerCAmelCase_=0.02 , lowerCAmelCase_=3 , lowerCAmelCase_=4 , lowerCAmelCase_=None , ) -> int:
_snake_case = parent
_snake_case = batch_size
_snake_case = seq_length
_snake_case = is_training
_snake_case = use_token_type_ids
_snake_case = use_labels
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = intermediate_size
_snake_case = hidden_act
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = type_sequence_label_size
_snake_case = initializer_range
_snake_case = num_labels
_snake_case = num_choices
_snake_case = scope
_snake_case = self.vocab_size - 1
def lowerCAmelCase ( self ) -> int:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case = None
if self.use_token_type_ids:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case = None
_snake_case = None
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case = ids_tensor([self.batch_size] , self.num_choices )
_snake_case = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
_snake_case = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , *lowerCAmelCase_ ) -> Optional[Any]:
_snake_case = OpenAIGPTModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ )
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )
_snake_case = 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_ ) -> Dict:
_snake_case = OpenAIGPTLMHeadModel(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , *lowerCAmelCase_ ) -> List[str]:
_snake_case = OpenAIGPTDoubleHeadsModel(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , *lowerCAmelCase_ ) -> Dict:
_snake_case = self.num_labels
_snake_case = OpenAIGPTForSequenceClassification(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self ) -> List[Any]:
_snake_case = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) = config_and_inputs
_snake_case = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
lowerCAmelCase_ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
lowerCAmelCase_ = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False ) -> Union[str, Any]:
_snake_case = super()._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
_snake_case = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowerCAmelCase_ , )
_snake_case = inputs_dict['labels']
_snake_case = inputs_dict['labels']
_snake_case = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowerCAmelCase_ , )
_snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_ )
return inputs_dict
def lowerCAmelCase ( self ) -> List[str]:
_snake_case = OpenAIGPTModelTester(self )
_snake_case = ConfigTester(self , config_class=lowerCAmelCase_ , n_embd=37 )
def lowerCAmelCase ( self ) -> Any:
self.config_tester.run_common_tests()
def lowerCAmelCase ( self ) -> List[Any]:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase_ )
def lowerCAmelCase ( self ) -> Dict:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase_ )
def lowerCAmelCase ( self ) -> Any:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase_ )
def lowerCAmelCase ( self ) -> List[Any]:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase_ )
@slow
def lowerCAmelCase ( self ) -> Dict:
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = OpenAIGPTModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
@slow
def lowerCAmelCase ( self ) -> Optional[Any]:
_snake_case = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(lowerCAmelCase_ )
_snake_case = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=lowerCAmelCase_ ) # the president is
_snake_case = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
_snake_case = model.generate(lowerCAmelCase_ , do_sample=lowerCAmelCase_ )
self.assertListEqual(output_ids[0].tolist() , lowerCAmelCase_ )
| 295 |
import random
def lowerCamelCase__ ( UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : bool = False ) -> dict:
'''simple docstring'''
_snake_case = {i: [] for i in range(UpperCamelCase__ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(UpperCamelCase__ )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(UpperCamelCase__ ):
for j in range(i + 1 , UpperCamelCase__ ):
if random.random() < probability:
graph[i].append(UpperCamelCase__ )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(UpperCamelCase__ )
return graph
def lowerCamelCase__ ( UpperCamelCase__ : int ) -> dict:
'''simple docstring'''
return {
i: [j for j in range(UpperCamelCase__ ) if i != j] for i in range(UpperCamelCase__ )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 295 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__( self : Any ,**A : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__A = deprecated_arg[3:]
__A = not kwargs.pop(A )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
__A = kwargs.pop("tpu_name" ,self.tpu_name )
__A = kwargs.pop("device_idx" ,self.device_idx )
__A = kwargs.pop("eager_mode" ,self.eager_mode )
__A = kwargs.pop("use_xla" ,self.use_xla )
super().__init__(**A )
snake_case_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Name of TPU"} , )
snake_case_ = field(
default=0 , metadata={"help": "CPU / GPU device index. Defaults to 0."} , )
snake_case_ = field(default=__SCREAMING_SNAKE_CASE , metadata={"help": "Benchmark models in eager model."} )
snake_case_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
"help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
} , )
@cached_property
def UpperCamelCase_ ( self : Dict ):
requires_backends(self ,["tf"] )
__A = None
if self.tpu:
try:
if self.tpu_name:
__A = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
__A = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
__A = None
return tpu
@cached_property
def UpperCamelCase_ ( self : Tuple ):
requires_backends(self ,["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
__A = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] ,"GPU" )
__A = tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] ,"GPU" ) # disable GPU
__A = tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}''' )
return strategy
@property
def UpperCamelCase_ ( self : List[Any] ):
requires_backends(self ,["tf"] )
return self._setup_tpu is not None
@property
def UpperCamelCase_ ( self : Any ):
requires_backends(self ,["tf"] )
return self._setup_strategy
@property
def UpperCamelCase_ ( self : int ):
requires_backends(self ,["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def UpperCamelCase_ ( self : Tuple ):
requires_backends(self ,["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return self.n_gpu > 0
| 15 |
# Copyright 2021 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 packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
SCREAMING_SNAKE_CASE :List[str] = 'pytorch_model.bin'
SCREAMING_SNAKE_CASE :str = 'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE :Optional[int] = 'adapter_config.json'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.bin'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.safetensors'
SCREAMING_SNAKE_CASE :str = 'tf_model.h5'
SCREAMING_SNAKE_CASE :List[Any] = 'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE :str = 'model.ckpt'
SCREAMING_SNAKE_CASE :List[Any] = 'flax_model.msgpack'
SCREAMING_SNAKE_CASE :Optional[int] = 'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE :Tuple = 'model.safetensors'
SCREAMING_SNAKE_CASE :List[Any] = 'model.safetensors.index.json'
SCREAMING_SNAKE_CASE :str = 'config.json'
SCREAMING_SNAKE_CASE :int = 'preprocessor_config.json'
SCREAMING_SNAKE_CASE :Optional[Any] = FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE :Optional[int] = 'generation_config.json'
SCREAMING_SNAKE_CASE :List[str] = 'modelcard.json'
SCREAMING_SNAKE_CASE :Optional[int] = '▁'
SCREAMING_SNAKE_CASE :Optional[Any] = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
SCREAMING_SNAKE_CASE :str = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE :Optional[Any] = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE :List[Any] = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
if version.parse(a_ ) < version.parse(a_ ):
if "dev" in min_version:
__A = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
__A = F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers." )
| 15 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor
class _snake_case ( unittest.TestCase ):
def __init__( self , a , a=7 , a=3 , a=30 , a=400 , a=True , a=None , a=True , a=[0.5, 0.5, 0.5] , a=[0.5, 0.5, 0.5] , a=True , a=1 / 255 , a=True , ) -> Optional[Any]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
SCREAMING_SNAKE_CASE = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = min_resolution
SCREAMING_SNAKE_CASE = max_resolution
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = image_mean
SCREAMING_SNAKE_CASE = image_std
SCREAMING_SNAKE_CASE = do_rescale
SCREAMING_SNAKE_CASE = rescale_factor
SCREAMING_SNAKE_CASE = do_pad
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE__ ( self , a , a=False) -> Any:
if not batched:
SCREAMING_SNAKE_CASE = image_inputs[0]
if isinstance(a , Image.Image):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image.size
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE = int(self.size['shortest_edge'] * h / w)
SCREAMING_SNAKE_CASE = self.size['shortest_edge']
elif w > h:
SCREAMING_SNAKE_CASE = self.size['shortest_edge']
SCREAMING_SNAKE_CASE = int(self.size['shortest_edge'] * w / h)
else:
SCREAMING_SNAKE_CASE = self.size['shortest_edge']
SCREAMING_SNAKE_CASE = self.size['shortest_edge']
else:
SCREAMING_SNAKE_CASE = []
for image in image_inputs:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
SCREAMING_SNAKE_CASE = max(a , key=lambda a: item[0])[0]
SCREAMING_SNAKE_CASE = max(a , key=lambda a: item[1])[1]
return expected_height, expected_width
@require_torch
@require_vision
class _snake_case ( A__ , unittest.TestCase ):
_lowercase : Dict = DetaImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self) -> Any:
SCREAMING_SNAKE_CASE = DetaImageProcessingTester(self)
@property
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(a , 'image_mean'))
self.assertTrue(hasattr(a , 'image_std'))
self.assertTrue(hasattr(a , 'do_normalize'))
self.assertTrue(hasattr(a , 'do_resize'))
self.assertTrue(hasattr(a , 'do_rescale'))
self.assertTrue(hasattr(a , 'do_pad'))
self.assertTrue(hasattr(a , 'size'))
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1333})
self.assertEqual(image_processor.do_pad , a)
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
pass
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
# Initialize image_processing
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=a)
for image in image_inputs:
self.assertIsInstance(a , Image.Image)
# Test not batched input
SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(a)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(a , batched=a)
SCREAMING_SNAKE_CASE = 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,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
# Initialize image_processing
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(a)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE = image_processing(a , return_tensors='pt').pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(a , batched=a)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
# Initialize image_processing
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(a)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE = image_processing(a , return_tensors='pt').pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(a , batched=a)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
# prepare image and target
SCREAMING_SNAKE_CASE = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r') as f:
SCREAMING_SNAKE_CASE = json.loads(f.read())
SCREAMING_SNAKE_CASE = {'image_id': 3_9769, 'annotations': target}
# encode them
SCREAMING_SNAKE_CASE = DetaImageProcessor()
SCREAMING_SNAKE_CASE = image_processing(images=a , annotations=a , return_tensors='pt')
# verify pixel values
SCREAMING_SNAKE_CASE = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding['pixel_values'].shape , a)
SCREAMING_SNAKE_CASE = torch.tensor([0.27_96, 0.31_38, 0.34_81])
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , a , atol=1E-4))
# verify area
SCREAMING_SNAKE_CASE = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , a))
# verify boxes
SCREAMING_SNAKE_CASE = torch.Size([6, 4])
self.assertEqual(encoding['labels'][0]['boxes'].shape , a)
SCREAMING_SNAKE_CASE = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , a , atol=1E-3))
# verify image_id
SCREAMING_SNAKE_CASE = torch.tensor([3_9769])
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , a))
# verify is_crowd
SCREAMING_SNAKE_CASE = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , a))
# verify class_labels
SCREAMING_SNAKE_CASE = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , a))
# verify orig_size
SCREAMING_SNAKE_CASE = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , a))
# verify size
SCREAMING_SNAKE_CASE = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , a))
@slow
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
# prepare image, target and masks_path
SCREAMING_SNAKE_CASE = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r') as f:
SCREAMING_SNAKE_CASE = json.loads(f.read())
SCREAMING_SNAKE_CASE = {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}
SCREAMING_SNAKE_CASE = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')
# encode them
SCREAMING_SNAKE_CASE = DetaImageProcessor(format='coco_panoptic')
SCREAMING_SNAKE_CASE = image_processing(images=a , annotations=a , masks_path=a , return_tensors='pt')
# verify pixel values
SCREAMING_SNAKE_CASE = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding['pixel_values'].shape , a)
SCREAMING_SNAKE_CASE = torch.tensor([0.27_96, 0.31_38, 0.34_81])
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , a , atol=1E-4))
# verify area
SCREAMING_SNAKE_CASE = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , a))
# verify boxes
SCREAMING_SNAKE_CASE = torch.Size([6, 4])
self.assertEqual(encoding['labels'][0]['boxes'].shape , a)
SCREAMING_SNAKE_CASE = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , a , atol=1E-3))
# verify image_id
SCREAMING_SNAKE_CASE = torch.tensor([3_9769])
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , a))
# verify is_crowd
SCREAMING_SNAKE_CASE = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , a))
# verify class_labels
SCREAMING_SNAKE_CASE = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , a))
# verify masks
SCREAMING_SNAKE_CASE = 82_2873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , a)
# verify orig_size
SCREAMING_SNAKE_CASE = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , a))
# verify size
SCREAMING_SNAKE_CASE = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , a))
| 356 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
class _snake_case :
def __init__( self , a) -> List[Any]:
SCREAMING_SNAKE_CASE = metric_id
class _snake_case :
_lowercase : Optional[Any] = [MetricMock(A__ ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
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 lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
if "tmp_path" in args:
SCREAMING_SNAKE_CASE = tuple(arg if arg != 'tmp_path' else tmp_path for arg in args)
with pytest.warns(_UpperCAmelCase , match='https://huggingface.co/docs/evaluate'):
func(*_UpperCAmelCase)
| 327 | 0 |
def __snake_case ( _lowerCAmelCase : int ) -> str:
A_ : Optional[Any] = int(snake_case__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(snake_case__ )
A_ : Tuple = divmod(snake_case__ , 2 )
return binary_recursive(snake_case__ ) + str(snake_case__ )
def __snake_case ( _lowerCAmelCase : str ) -> str:
A_ : Optional[Any] = str(snake_case__ ).strip()
if not number:
raise ValueError("No input value was provided" )
A_ : Any = '-' if number.startswith("-" ) else ''
A_ : List[str] = number.lstrip("-" )
if not number.isnumeric():
raise ValueError("Input value is not an integer" )
return f"{negative}0b{binary_recursive(int(snake_case__ ) )}"
if __name__ == "__main__":
from doctest import testmod
testmod()
| 300 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
__UpperCAmelCase = None
__UpperCAmelCase = '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__UpperCAmelCase = [
np.dtype('''|b1'''),
np.dtype('''|u1'''),
np.dtype('''<u2'''),
np.dtype('''>u2'''),
np.dtype('''<i2'''),
np.dtype('''>i2'''),
np.dtype('''<u4'''),
np.dtype('''>u4'''),
np.dtype('''<i4'''),
np.dtype('''>i4'''),
np.dtype('''<f4'''),
np.dtype('''>f4'''),
np.dtype('''<f8'''),
np.dtype('''>f8'''),
]
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase__ : bool = True
UpperCAmelCase__ : Optional[str] = None
# Automatically constructed
UpperCAmelCase__ : ClassVar[str] = "PIL.Image.Image"
UpperCAmelCase__ : ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
UpperCAmelCase__ : str = field(default="Image" , init=a__ , repr=a__ )
def __call__( self ) -> Any:
return self.pa_type
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = np.array(SCREAMING_SNAKE_CASE_ )
if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
return {"path": value, "bytes": None}
elif isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
return {"path": None, "bytes": value}
elif isinstance(SCREAMING_SNAKE_CASE_, np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(SCREAMING_SNAKE_CASE_ )
elif isinstance(SCREAMING_SNAKE_CASE_, PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(SCREAMING_SNAKE_CASE_ )
elif value.get('path' ) is not None and os.path.isfile(value['path'] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get('path' )}
elif value.get('bytes' ) is not None or value.get('path' ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get('bytes' ), "path": value.get('path' )}
else:
raise ValueError(
F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=None ) -> "PIL.Image.Image":
if not self.decode:
raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.' )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support decoding images, please install \'Pillow\'.' )
if token_per_repo_id is None:
UpperCamelCase : Any = {}
UpperCamelCase , UpperCamelCase : Union[str, Any] = value['path'], value['bytes']
if bytes_ is None:
if path is None:
raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
else:
if is_local_path(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = PIL.Image.open(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : int = path.split('::' )[-1]
try:
UpperCamelCase : Optional[Any] = string_to_dict(SCREAMING_SNAKE_CASE_, config.HUB_DATASETS_URL )['repo_id']
UpperCamelCase : str = token_per_repo_id.get(SCREAMING_SNAKE_CASE_ )
except ValueError:
UpperCamelCase : Tuple = None
with xopen(SCREAMING_SNAKE_CASE_, 'rb', use_auth_token=SCREAMING_SNAKE_CASE_ ) as f:
UpperCamelCase : Optional[int] = BytesIO(f.read() )
UpperCamelCase : int = PIL.Image.open(bytes_ )
else:
UpperCamelCase : Optional[int] = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def snake_case_ ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
return (
self
if self.decode
else {
"bytes": Value('binary' ),
"path": Value('string' ),
}
)
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
UpperCamelCase : List[str] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.binary() )
UpperCamelCase : Dict = pa.StructArray.from_arrays([bytes_array, storage], ['bytes', 'path'], mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
UpperCamelCase : Optional[int] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.string() )
UpperCamelCase : Union[str, Any] = pa.StructArray.from_arrays([storage, path_array], ['bytes', 'path'], mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('bytes' ) >= 0:
UpperCamelCase : List[str] = storage.field('bytes' )
else:
UpperCamelCase : List[str] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.binary() )
if storage.type.get_field_index('path' ) >= 0:
UpperCamelCase : List[str] = storage.field('path' )
else:
UpperCamelCase : Optional[Any] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.string() )
UpperCamelCase : Optional[int] = pa.StructArray.from_arrays([bytes_array, path_array], ['bytes', 'path'], mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
UpperCamelCase : Optional[Any] = pa.array(
[encode_np_array(np.array(SCREAMING_SNAKE_CASE_ ) )['bytes'] if arr is not None else None for arr in storage.to_pylist()], type=pa.binary(), )
UpperCamelCase : List[str] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.string() )
UpperCamelCase : int = pa.StructArray.from_arrays(
[bytes_array, path_array], ['bytes', 'path'], mask=bytes_array.is_null() )
return array_cast(SCREAMING_SNAKE_CASE_, self.pa_type )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(SCREAMING_SNAKE_CASE_ ):
with xopen(SCREAMING_SNAKE_CASE_, 'rb' ) as f:
UpperCamelCase : Optional[int] = f.read()
return bytes_
UpperCamelCase : Union[str, Any] = pa.array(
[
(path_to_bytes(x['path'] ) if x['bytes'] is None else x['bytes']) if x is not None else None
for x in storage.to_pylist()
], type=pa.binary(), )
UpperCamelCase : Any = pa.array(
[os.path.basename(SCREAMING_SNAKE_CASE_ ) if path is not None else None for path in storage.field('path' ).to_pylist()], type=pa.string(), )
UpperCamelCase : int = pa.StructArray.from_arrays([bytes_array, path_array], ['bytes', 'path'], mask=bytes_array.is_null() )
return array_cast(SCREAMING_SNAKE_CASE_, self.pa_type )
def UpperCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
UpperCamelCase : Dict = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def UpperCamelCase ( snake_case__ : "PIL.Image.Image" ) -> bytes:
UpperCamelCase : Any = BytesIO()
if image.format in list_image_compression_formats():
UpperCamelCase : Tuple = image.format
else:
UpperCamelCase : List[str] = 'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
image.save(snake_case__ , format=snake_case__ )
return buffer.getvalue()
def UpperCamelCase ( snake_case__ : "PIL.Image.Image" ) -> dict:
if hasattr(snake_case__ , 'filename' ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(snake_case__ )}
def UpperCamelCase ( snake_case__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
UpperCamelCase : Union[str, Any] = array.dtype
UpperCamelCase : List[Any] = dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
UpperCamelCase : Optional[Any] = dtype.kind
UpperCamelCase : Any = dtype.itemsize
UpperCamelCase : int = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
UpperCamelCase : Optional[Any] = np.dtype('|u1' )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
UpperCamelCase : List[Any] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
UpperCamelCase : Dict = dtype_byteorder + dtype_kind + str(snake_case__ )
UpperCamelCase : str = np.dtype(snake_case__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
UpperCamelCase : Union[str, Any] = PIL.Image.fromarray(array.astype(snake_case__ ) )
return {"path": None, "bytes": image_to_bytes(snake_case__ )}
def UpperCamelCase ( snake_case__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if objs:
UpperCamelCase , UpperCamelCase : Union[str, Any] = first_non_null_value(snake_case__ )
if isinstance(snake_case__ , snake_case__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(snake_case__ , np.ndarray ):
UpperCamelCase : List[Any] = no_op_if_value_is_null(snake_case__ )
return [obj_to_image_dict_func(snake_case__ ) for obj in objs]
elif isinstance(snake_case__ , PIL.Image.Image ):
UpperCamelCase : Optional[int] = no_op_if_value_is_null(snake_case__ )
return [obj_to_image_dict_func(snake_case__ ) for obj in objs]
else:
return objs
else:
return objs
| 119 | 0 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class a ( _lowerCamelCase ):
snake_case_ = (CMStochasticIterativeScheduler,)
snake_case_ = 10
def A_ ( self : List[Any] , **lowercase_ : Any ):
snake_case_ = {
'''num_train_timesteps''': 201,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
config.update(**lowercase_ )
return config
def A_ ( self : List[Any] ):
snake_case_ = 10
snake_case_ = self.get_scheduler_config()
snake_case_ = self.scheduler_classes[0](**lowercase_ )
scheduler.set_timesteps(lowercase_ )
snake_case_ = scheduler.timesteps[0]
snake_case_ = scheduler.timesteps[1]
snake_case_ = self.dummy_sample
snake_case_ = 0.1 * sample
snake_case_ = scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
snake_case_ = scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A_ ( self : Dict ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def A_ ( self : Tuple ):
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=lowercase_ )
def A_ ( self : Optional[Any] ):
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
snake_case_ = 1
scheduler.set_timesteps(lowercase_ )
snake_case_ = scheduler.timesteps
snake_case_ = torch.manual_seed(0 )
snake_case_ = self.dummy_model()
snake_case_ = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(lowercase_ ):
# 1. scale model input
snake_case_ = scheduler.scale_model_input(lowercase_ , lowercase_ )
# 2. predict noise residual
snake_case_ = model(lowercase_ , lowercase_ )
# 3. predict previous sample x_t-1
snake_case_ = scheduler.step(lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ ).prev_sample
snake_case_ = pred_prev_sample
snake_case_ = torch.sum(torch.abs(lowercase_ ) )
snake_case_ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 192.7614 ) < 1e-2
assert abs(result_mean.item() - 0.2510 ) < 1e-3
def A_ ( self : List[str] ):
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
snake_case_ = [106, 0]
scheduler.set_timesteps(timesteps=lowercase_ )
snake_case_ = scheduler.timesteps
snake_case_ = torch.manual_seed(0 )
snake_case_ = self.dummy_model()
snake_case_ = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
snake_case_ = scheduler.scale_model_input(lowercase_ , lowercase_ )
# 2. predict noise residual
snake_case_ = model(lowercase_ , lowercase_ )
# 3. predict previous sample x_t-1
snake_case_ = scheduler.step(lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ ).prev_sample
snake_case_ = pred_prev_sample
snake_case_ = torch.sum(torch.abs(lowercase_ ) )
snake_case_ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 347.6357 ) < 1e-2
assert abs(result_mean.item() - 0.4527 ) < 1e-3
def A_ ( self : int ):
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
snake_case_ = [39, 30, 12, 15, 0]
with self.assertRaises(lowercase_ , msg='''`timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
snake_case_ = [39, 30, 12, 1, 0]
snake_case_ = len(lowercase_ )
with self.assertRaises(lowercase_ , msg='''Can only pass one of `num_inference_steps` or `timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=lowercase_ , timesteps=lowercase_ )
def A_ ( self : str ):
snake_case_ = self.scheduler_classes[0]
snake_case_ = self.get_scheduler_config()
snake_case_ = scheduler_class(**lowercase_ )
snake_case_ = [scheduler.config.num_train_timesteps]
with self.assertRaises(
lowercase_ , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=lowercase_ )
| 354 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
a : Union[str, Any] = get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0 ) -> Tuple:
'''simple docstring'''
os.makedirs(__UpperCAmelCase, exist_ok=__UpperCAmelCase )
with FSDP.state_dict_type(
__UpperCAmelCase, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ):
snake_case_ = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
snake_case_ = F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase )
if accelerator.process_index == 0:
logger.info(F"Saving model to {output_model_file}" )
torch.save(__UpperCAmelCase, __UpperCAmelCase )
logger.info(F"Model saved to {output_model_file}" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
snake_case_ = (
F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
if model_index == 0
else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
)
snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase )
logger.info(F"Saving model to {output_model_file}" )
torch.save(__UpperCAmelCase, __UpperCAmelCase )
logger.info(F"Model saved to {output_model_file}" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
snake_case_ = os.path.join(__UpperCAmelCase, F"{MODEL_NAME}_{model_index}" )
os.makedirs(__UpperCAmelCase, exist_ok=__UpperCAmelCase )
logger.info(F"Saving model to {ckpt_dir}" )
snake_case_ = {'''model''': state_dict}
dist_cp.save_state_dict(
state_dict=__UpperCAmelCase, storage_writer=dist_cp.FileSystemWriter(__UpperCAmelCase ), planner=DefaultSavePlanner(), )
logger.info(F"Model saved to {ckpt_dir}" )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0 ) -> str:
'''simple docstring'''
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
__UpperCAmelCase, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(__UpperCAmelCase ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
'''Set the `sync_module_states` flag to `True` so that model states are synced across processes when '''
'''initializing FSDP object''' )
return
snake_case_ = F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase )
logger.info(F"Loading model from {input_model_file}" )
snake_case_ = torch.load(__UpperCAmelCase )
logger.info(F"Model loaded from {input_model_file}" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
snake_case_ = (
F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
if model_index == 0
else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
)
snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase )
logger.info(F"Loading model from {input_model_file}" )
snake_case_ = torch.load(__UpperCAmelCase )
logger.info(F"Model loaded from {input_model_file}" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
snake_case_ = (
os.path.join(__UpperCAmelCase, F"{MODEL_NAME}_{model_index}" )
if F"{MODEL_NAME}" not in input_dir
else input_dir
)
logger.info(F"Loading model from {ckpt_dir}" )
snake_case_ = {'''model''': model.state_dict()}
dist_cp.load_state_dict(
state_dict=__UpperCAmelCase, storage_reader=dist_cp.FileSystemReader(__UpperCAmelCase ), planner=DefaultLoadPlanner(), )
snake_case_ = state_dict['''model''']
logger.info(F"Model loaded from {ckpt_dir}" )
model.load_state_dict(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0 ) -> Dict:
'''simple docstring'''
os.makedirs(__UpperCAmelCase, exist_ok=__UpperCAmelCase )
with FSDP.state_dict_type(
__UpperCAmelCase, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ):
snake_case_ = FSDP.optim_state_dict(__UpperCAmelCase, __UpperCAmelCase )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
snake_case_ = (
F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
)
snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase )
logger.info(F"Saving Optimizer state to {output_optimizer_file}" )
torch.save(__UpperCAmelCase, __UpperCAmelCase )
logger.info(F"Optimizer state saved in {output_optimizer_file}" )
else:
snake_case_ = os.path.join(__UpperCAmelCase, F"{OPTIMIZER_NAME}_{optimizer_index}" )
os.makedirs(__UpperCAmelCase, exist_ok=__UpperCAmelCase )
logger.info(F"Saving Optimizer state to {ckpt_dir}" )
dist_cp.save_state_dict(
state_dict={'''optimizer''': optim_state}, storage_writer=dist_cp.FileSystemWriter(__UpperCAmelCase ), planner=DefaultSavePlanner(), )
logger.info(F"Optimizer state saved in {ckpt_dir}" )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0 ) -> Union[str, Any]:
'''simple docstring'''
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
__UpperCAmelCase, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
snake_case_ = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
snake_case_ = (
F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
)
snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase )
logger.info(F"Loading Optimizer state from {input_optimizer_file}" )
snake_case_ = torch.load(__UpperCAmelCase )
logger.info(F"Optimizer state loaded from {input_optimizer_file}" )
else:
snake_case_ = (
os.path.join(__UpperCAmelCase, F"{OPTIMIZER_NAME}_{optimizer_index}" )
if F"{OPTIMIZER_NAME}" not in input_dir
else input_dir
)
logger.info(F"Loading Optimizer from {ckpt_dir}" )
snake_case_ = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict(), optimizer_key='''optimizer''', storage_reader=dist_cp.FileSystemReader(__UpperCAmelCase ), )
snake_case_ = optim_state['''optimizer''']
logger.info(F"Optimizer loaded from {ckpt_dir}" )
snake_case_ = FSDP.optim_state_dict_to_load(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
optimizer.load_state_dict(__UpperCAmelCase )
| 72 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase_ ( __UpperCAmelCase ) -> list[int]:
lowerCAmelCase__ : List[Any] = [True] * limit
lowerCAmelCase__ : Dict = False
lowerCAmelCase__ : Optional[Any] = False
lowerCAmelCase__ : Union[str, Any] = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
lowerCAmelCase__ : int = i * 2
while index < limit:
lowerCAmelCase__ : Tuple = False
lowerCAmelCase__ : Union[str, Any] = index + i
lowerCAmelCase__ : List[str] = [2]
for i in range(3 , __UpperCAmelCase , 2 ):
if is_prime[i]:
primes.append(__UpperCAmelCase )
return primes
def lowercase_ ( __UpperCAmelCase = 100_0000 ) -> int:
lowerCAmelCase__ : Tuple = prime_sieve(__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = 0
lowerCAmelCase__ : List[Any] = 0
for i in range(len(__UpperCAmelCase ) ):
for j in range(i + length , len(__UpperCAmelCase ) ):
lowerCAmelCase__ : List[str] = sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
lowerCAmelCase__ : Tuple = j - i
lowerCAmelCase__ : List[Any] = sol
return largest
if __name__ == "__main__":
print(f"""{solution() = }""")
| 242 |
"""simple docstring"""
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 = logging.get_logger(__name__)
_A = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Optional[Any] = "levit"
def __init__( self : List[Any] , UpperCamelCase : List[str]=2_24 , UpperCamelCase : Any=3 , UpperCamelCase : Optional[Any]=3 , UpperCamelCase : Union[str, Any]=2 , UpperCamelCase : Any=1 , UpperCamelCase : int=16 , UpperCamelCase : List[str]=[1_28, 2_56, 3_84] , UpperCamelCase : Optional[Any]=[4, 8, 12] , UpperCamelCase : Optional[int]=[4, 4, 4] , UpperCamelCase : str=[16, 16, 16] , UpperCamelCase : Tuple=0 , UpperCamelCase : List[str]=[2, 2, 2] , UpperCamelCase : Optional[int]=[2, 2, 2] , UpperCamelCase : Optional[int]=0.02 , **UpperCamelCase : Dict , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase )
lowerCAmelCase__ : int = image_size
lowerCAmelCase__ : Any = num_channels
lowerCAmelCase__ : int = kernel_size
lowerCAmelCase__ : Any = stride
lowerCAmelCase__ : List[str] = padding
lowerCAmelCase__ : Tuple = hidden_sizes
lowerCAmelCase__ : str = num_attention_heads
lowerCAmelCase__ : List[Any] = depths
lowerCAmelCase__ : List[str] = key_dim
lowerCAmelCase__ : List[str] = drop_path_rate
lowerCAmelCase__ : List[Any] = patch_size
lowerCAmelCase__ : Dict = attention_ratio
lowerCAmelCase__ : Tuple = mlp_ratio
lowerCAmelCase__ : Any = initializer_range
lowerCAmelCase__ : Dict = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Tuple = version.parse("1.11" )
@property
def _lowerCAmelCase ( self : int ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowerCAmelCase ( self : List[str] ) -> float:
"""simple docstring"""
return 1E-4
| 242 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ["""ViTFeatureExtractor"""]
__lowercase = ["""ViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTForImageClassification""",
"""ViTForMaskedImageModeling""",
"""ViTModel""",
"""ViTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""TFViTForImageClassification""",
"""TFViTModel""",
"""TFViTPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""FlaxViTForImageClassification""",
"""FlaxViTModel""",
"""FlaxViTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 226 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__lowercase = logging.get_logger(__name__)
class _A ( _a ):
"""simple docstring"""
def __init__( self : List[Any] , *__UpperCAmelCase : int , **__UpperCAmelCase : List[Any]):
warnings.warn(
"The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use DPTImageProcessor instead." , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase)
| 226 | 1 |
import math
def UpperCamelCase( __UpperCamelCase : Optional[Any] ):
return math.sqrt(a_ ) * math.sqrt(a_ ) == num
def UpperCamelCase( __UpperCamelCase : Any ):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : List[Any] = n
while left <= right:
lowerCAmelCase_ : Union[str, Any] = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
lowerCAmelCase_ : Any = mid - 1
else:
lowerCAmelCase_ : Tuple = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 103 |
'''simple docstring'''
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 _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase=3 , _lowerCamelCase=32 , _lowerCamelCase=3 , _lowerCamelCase=10 , _lowerCamelCase=[10, 20, 30, 40] , _lowerCamelCase=[1, 1, 2, 1] , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase="relu" , _lowerCamelCase=3 , _lowerCamelCase=None , ) -> List[str]:
A_ : Any = parent
A_ : List[Any] = batch_size
A_ : List[Any] = image_size
A_ : Optional[int] = num_channels
A_ : Tuple = embeddings_size
A_ : str = hidden_sizes
A_ : Optional[Any] = depths
A_ : Any = is_training
A_ : int = use_labels
A_ : int = hidden_act
A_ : Optional[Any] = num_labels
A_ : str = scope
A_ : Optional[int] = len(_lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Tuple:
A_ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Dict = None
if self.use_labels:
A_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
A_ : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self ) -> Optional[Any]:
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 UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> List[str]:
A_ : Dict = TFRegNetModel(config=_lowerCamelCase )
A_ : Optional[int] = model(_lowerCamelCase , training=_lowerCamelCase )
# 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 UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Optional[int]:
A_ : Optional[Any] = self.num_labels
A_ : int = TFRegNetForImageClassification(_lowerCamelCase )
A_ : Tuple = model(_lowerCamelCase , labels=_lowerCamelCase , training=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self ) -> str:
A_ : Any = self.prepare_config_and_inputs()
A_ , A_ , A_ : str = config_and_inputs
A_ : Optional[int] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class _lowerCAmelCase ( __A, __A, unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowerCamelCase = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
def UpperCAmelCase_ ( self ) -> Optional[Any]:
A_ : Dict = TFRegNetModelTester(self )
A_ : Optional[int] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def UpperCAmelCase_ ( self ) -> str:
return
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def UpperCAmelCase_ ( self ) -> Dict:
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 UpperCAmelCase_ ( self ) -> int:
super().test_keras_fit()
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
pass
def UpperCAmelCase_ ( self ) -> int:
A_ , A_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Optional[Any] = model_class(_lowerCamelCase )
A_ : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : int = [*signature.parameters.keys()]
A_ : Any = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Tuple:
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Dict:
def check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
A_ : Optional[int] = model_class(_lowerCamelCase )
A_ : List[Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) , training=_lowerCamelCase )
A_ : List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A_ : Optional[int] = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , 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] , )
A_ , A_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
A_ : List[str] = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
A_ : Dict = layer_type
A_ : List[Any] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : str = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Dict:
A_ , A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase={} ):
A_ : Dict = model(_lowerCamelCase , return_dict=_lowerCamelCase , **_lowerCamelCase )
A_ : Optional[Any] = model(_lowerCamelCase , return_dict=_lowerCamelCase , **_lowerCamelCase ).to_tuple()
def recursive_check(_lowerCamelCase , _lowerCamelCase ):
if isinstance(_lowerCamelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowerCamelCase , _lowerCamelCase ):
recursive_check(_lowerCamelCase , _lowerCamelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(_lowerCamelCase , _lowerCamelCase ) ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"
) , )
recursive_check(_lowerCamelCase , _lowerCamelCase )
for model_class in self.all_model_classes:
A_ : Optional[Any] = model_class(_lowerCamelCase )
A_ : Optional[Any] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[int] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : Any = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
A_ : Tuple = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : Dict = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A_ : int = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , {"""output_hidden_states""": True} )
A_ : Tuple = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
A_ : str = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , {"""output_hidden_states""": True} )
def UpperCAmelCase_ ( self ) -> str:
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def UpperCAmelCase_ ( self ) -> Tuple:
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Dict = TFRegNetModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
A_ : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self ) -> int:
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self ) -> Optional[Any]:
A_ : str = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
A_ : Tuple = self.default_image_processor
A_ : Optional[int] = prepare_img()
A_ : Any = image_processor(images=_lowerCamelCase , return_tensors="""tf""" )
# forward pass
A_ : List[Any] = model(**_lowerCamelCase , training=_lowerCamelCase )
# verify the logits
A_ : Optional[Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : Optional[Any] = tf.constant([-0.4180, -1.5051, -3.4836] )
tf.debugging.assert_near(outputs.logits[0, :3] , _lowerCamelCase , atol=1e-4 )
| 344 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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 _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
def _a ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _a ( self ):
UpperCamelCase_: Any = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-canny' , from_pt=_lowerCamelCase , dtype=jnp.bfloataa )
UpperCamelCase_: Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=_lowerCamelCase , from_pt=_lowerCamelCase , dtype=jnp.bfloataa )
UpperCamelCase_: str = controlnet_params
UpperCamelCase_: int = 'bird'
UpperCamelCase_: Union[str, Any] = jax.device_count()
UpperCamelCase_: Union[str, Any] = pipe.prepare_text_inputs([prompts] * num_samples )
UpperCamelCase_: Optional[int] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' )
UpperCamelCase_: Optional[Any] = pipe.prepare_image_inputs([canny_image] * num_samples )
UpperCamelCase_: List[Any] = jax.random.PRNGKey(0 )
UpperCamelCase_: Optional[Any] = jax.random.split(_lowerCamelCase , jax.device_count() )
UpperCamelCase_: Any = replicate(_lowerCamelCase )
UpperCamelCase_: Tuple = shard(_lowerCamelCase )
UpperCamelCase_: int = shard(_lowerCamelCase )
UpperCamelCase_: str = pipe(
prompt_ids=_lowerCamelCase , image=_lowerCamelCase , params=_lowerCamelCase , prng_seed=_lowerCamelCase , num_inference_steps=5_0 , jit=_lowerCamelCase , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
UpperCamelCase_: Tuple = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
UpperCamelCase_: List[str] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
UpperCamelCase_: Any = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase_: List[str] = jnp.array(
[0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8] )
print(f'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _a ( self ):
UpperCamelCase_: List[str] = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-openpose' , from_pt=_lowerCamelCase , dtype=jnp.bfloataa )
UpperCamelCase_: List[str] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=_lowerCamelCase , from_pt=_lowerCamelCase , dtype=jnp.bfloataa )
UpperCamelCase_: int = controlnet_params
UpperCamelCase_: Union[str, Any] = 'Chef in the kitchen'
UpperCamelCase_: List[Any] = jax.device_count()
UpperCamelCase_: Tuple = pipe.prepare_text_inputs([prompts] * num_samples )
UpperCamelCase_: List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png' )
UpperCamelCase_: Tuple = pipe.prepare_image_inputs([pose_image] * num_samples )
UpperCamelCase_: Any = jax.random.PRNGKey(0 )
UpperCamelCase_: str = jax.random.split(_lowerCamelCase , jax.device_count() )
UpperCamelCase_: Optional[int] = replicate(_lowerCamelCase )
UpperCamelCase_: int = shard(_lowerCamelCase )
UpperCamelCase_: Dict = shard(_lowerCamelCase )
UpperCamelCase_: int = pipe(
prompt_ids=_lowerCamelCase , image=_lowerCamelCase , params=_lowerCamelCase , prng_seed=_lowerCamelCase , num_inference_steps=5_0 , jit=_lowerCamelCase , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
UpperCamelCase_: int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
UpperCamelCase_: Optional[int] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
UpperCamelCase_: Dict = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase_: int = jnp.array(
[[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]] )
print(f'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2 | 354 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Optional[Any] = {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : str ='''convbert'''
def __init__( self , _lowerCamelCase=3_0_5_2_2 , _lowerCamelCase=7_6_8 , _lowerCamelCase=1_2 , _lowerCamelCase=1_2 , _lowerCamelCase=3_0_7_2 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=5_1_2 , _lowerCamelCase=2 , _lowerCamelCase=0.0_2 , _lowerCamelCase=1e-12 , _lowerCamelCase=1 , _lowerCamelCase=0 , _lowerCamelCase=2 , _lowerCamelCase=7_6_8 , _lowerCamelCase=2 , _lowerCamelCase=9 , _lowerCamelCase=1 , _lowerCamelCase=None , **_lowerCamelCase , ):
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase , )
UpperCamelCase_: Dict = vocab_size
UpperCamelCase_: Tuple = hidden_size
UpperCamelCase_: Optional[int] = num_hidden_layers
UpperCamelCase_: Optional[int] = num_attention_heads
UpperCamelCase_: Optional[Any] = intermediate_size
UpperCamelCase_: Tuple = hidden_act
UpperCamelCase_: Any = hidden_dropout_prob
UpperCamelCase_: Any = attention_probs_dropout_prob
UpperCamelCase_: List[Any] = max_position_embeddings
UpperCamelCase_: List[Any] = type_vocab_size
UpperCamelCase_: Optional[int] = initializer_range
UpperCamelCase_: Tuple = layer_norm_eps
UpperCamelCase_: List[str] = embedding_size
UpperCamelCase_: int = head_ratio
UpperCamelCase_: Dict = conv_kernel_size
UpperCamelCase_: List[Any] = num_groups
UpperCamelCase_: Dict = classifier_dropout
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
@property
def _a ( self ):
if self.task == "multiple-choice":
UpperCamelCase_: Dict = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
UpperCamelCase_: Dict = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] ) | 292 | 0 |
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 A__ ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A : str = GPTaTokenizer
__A : Optional[Any] = GPTaTokenizerFast
__A : Union[str, Any] = True
__A : int = {'''add_prefix_space''': True}
__A : Optional[int] = False
def __lowercase ( self) -> List[str]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
a__ : Dict = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
a__ : List[str] = dict(zip(lowercase , range(len(lowercase))))
a__ : List[Any] = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
a__ : Any = {'unk_token': '<unk>'}
a__ : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
a__ : List[Any] = 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(lowercase) + '\n')
with open(self.merges_file , 'w' , encoding='utf-8') as fp:
fp.write('\n'.join(lowercase))
def __lowercase ( self , **lowercase) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return GPTaTokenizer.from_pretrained(self.tmpdirname , **lowercase)
def __lowercase ( self , **lowercase) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **lowercase)
def __lowercase ( self , lowercase) -> Dict:
'''simple docstring'''
a__ : Dict = 'lower newer'
a__ : str = 'lower newer'
return input_text, output_text
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : List[str] = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map)
a__ : List[Any] = 'lower newer'
a__ : int = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
a__ : Optional[Any] = tokenizer.tokenize(lowercase , add_prefix_space=lowercase)
self.assertListEqual(lowercase , lowercase)
a__ : List[Any] = tokens + [tokenizer.unk_token]
a__ : Optional[Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase) , lowercase)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a__ : Optional[int] = self.get_tokenizer()
a__ : Optional[Any] = self.get_rust_tokenizer(add_prefix_space=lowercase)
a__ : Optional[Any] = 'lower newer'
# Testing tokenization
a__ : Any = tokenizer.tokenize(lowercase , add_prefix_space=lowercase)
a__ : Optional[Any] = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
# Testing conversion to ids without special tokens
a__ : int = tokenizer.encode(lowercase , add_special_tokens=lowercase , add_prefix_space=lowercase)
a__ : Tuple = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
# Testing conversion to ids with special tokens
a__ : List[str] = self.get_rust_tokenizer(add_prefix_space=lowercase)
a__ : int = tokenizer.encode(lowercase , add_prefix_space=lowercase)
a__ : List[Any] = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
# Testing the unknown token
a__ : Union[str, Any] = tokens + [rust_tokenizer.unk_token]
a__ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase) , lowercase)
def __lowercase ( self , *lowercase , **lowercase) -> List[str]:
'''simple docstring'''
pass
def __lowercase ( self , lowercase=15) -> int:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
a__ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowercase , **lowercase)
# Simple input
a__ : Union[str, Any] = 'This is a simple input'
a__ : Optional[Any] = ['This is a simple input 1', 'This is a simple input 2']
a__ : int = ('This is a simple input', 'This is a pair')
a__ : int = [
('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(lowercase , tokenizer_r.encode , lowercase , max_length=lowercase , padding='max_length')
# Simple input
self.assertRaises(lowercase , tokenizer_r.encode_plus , lowercase , max_length=lowercase , padding='max_length')
# Simple input
self.assertRaises(
lowercase , tokenizer_r.batch_encode_plus , lowercase , max_length=lowercase , padding='max_length' , )
# Pair input
self.assertRaises(lowercase , tokenizer_r.encode , lowercase , max_length=lowercase , padding='max_length')
# Pair input
self.assertRaises(lowercase , tokenizer_r.encode_plus , lowercase , max_length=lowercase , padding='max_length')
# Pair input
self.assertRaises(
lowercase , tokenizer_r.batch_encode_plus , lowercase , max_length=lowercase , padding='max_length' , )
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Tuple = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>')
# Simple input
a__ : Tuple = 'This is a simple input'
a__ : Optional[Any] = ['This is a simple input looooooooong', 'This is a simple input']
a__ : Any = ('This is a simple input', 'This is a pair')
a__ : List[Any] = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
a__ : Optional[int] = tokenizer.pad_token_id
a__ : Dict = tokenizer(lowercase , padding='max_length' , max_length=30 , return_tensors='np')
a__ : str = tokenizer(lowercase , padding=lowercase , truncate=lowercase , return_tensors='np')
a__ : str = tokenizer(*lowercase , padding='max_length' , max_length=60 , return_tensors='np')
a__ : Any = tokenizer(lowercase , padding=lowercase , truncate=lowercase , 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 __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : Any = '$$$'
a__ : int = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=lowercase , add_bos_token=lowercase)
a__ : List[Any] = 'This is a simple input'
a__ : List[Any] = ['This is a simple input 1', 'This is a simple input 2']
a__ : Union[str, Any] = tokenizer.bos_token_id
a__ : List[str] = tokenizer(lowercase)
a__ : Union[str, Any] = tokenizer(lowercase)
self.assertEqual(out_s.input_ids[0] , lowercase)
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))
a__ : str = tokenizer.decode(out_s.input_ids)
a__ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids)
self.assertEqual(decode_s.split()[0] , lowercase)
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
pass
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : str = [self.get_tokenizer(do_lower_case=lowercase , add_bos_token=lowercase)]
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}'):
a__ : Optional[int] = 'Encode this.'
a__ : Optional[int] = 'This one too please.'
a__ : Optional[Any] = tokenizer.encode(lowercase , add_special_tokens=lowercase)
encoded_sequence += tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__ : Optional[int] = tokenizer.encode_plus(
lowercase , lowercase , add_special_tokens=lowercase , return_special_tokens_mask=lowercase , )
a__ : Any = encoded_sequence_dict['input_ids']
a__ : int = encoded_sequence_dict['special_tokens_mask']
self.assertEqual(len(lowercase) , len(lowercase))
a__ : Optional[Any] = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(lowercase)
]
a__ : Tuple = [x for x in filtered_sequence if x is not None]
self.assertEqual(lowercase , lowercase)
@require_tokenizers
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : Optional[int] = AutoTokenizer.from_pretrained('facebook/opt-350m' , from_slow=lowercase)
a__ : int = 'A photo of a cat'
a__ : List[Any] = tokenizer.encode(
lowercase , )
self.assertEqual(lowercase , [2, 250, 1345, 9, 10, 4758])
tokenizer.save_pretrained('test_opt')
a__ : List[str] = AutoTokenizer.from_pretrained('./test_opt')
a__ : Optional[Any] = tokenizer.encode(
lowercase , )
self.assertEqual(lowercase , [2, 250, 1345, 9, 10, 4758])
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Any = AutoTokenizer.from_pretrained('facebook/opt-350m' , use_slow=lowercase)
a__ : str = 'A photo of a cat'
a__ : Tuple = tokenizer.encode(
lowercase , )
# Same as above
self.assertEqual(lowercase , [2, 250, 1345, 9, 10, 4758])
@unittest.skip('This test is failing because of a bug in the fast tokenizer')
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : List[str] = AutoTokenizer.from_pretrained('facebook/opt-350m' , from_slow=lowercase)
a__ : Tuple = 'bos'
a__ : Tuple = tokenizer.get_vocab()['bos']
a__ : Dict = 'A photo of a cat'
a__ : Optional[int] = tokenizer.encode(
lowercase , )
# We changed the bos token
self.assertEqual(lowercase , [3_1957, 250, 1345, 9, 10, 4758])
tokenizer.save_pretrained('./tok')
a__ : List[str] = AutoTokenizer.from_pretrained('./tok')
self.assertTrue(tokenizer.is_fast)
a__ : List[Any] = tokenizer.encode(
lowercase , )
self.assertEqual(lowercase , [3_1957, 250, 1345, 9, 10, 4758])
| 99 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 99 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( _UpperCAmelCase , unittest.TestCase):
_SCREAMING_SNAKE_CASE : Optional[int] = RobertaTokenizer
_SCREAMING_SNAKE_CASE : Union[str, Any] = RobertaTokenizerFast
_SCREAMING_SNAKE_CASE : Any = True
_SCREAMING_SNAKE_CASE : Any = {'''cls_token''': '''<s>'''}
def UpperCamelCase__ ( self ):
"""simple docstring"""
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(lowercase_ , range(len(lowercase_ ) ) ) )
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(lowercase_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(lowercase_ ) )
def UpperCamelCase__ ( self , **_UpperCamelCase ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCamelCase__ ( self , **_UpperCamelCase ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = """lower newer"""
return input_text, output_text
def UpperCamelCase__ ( self ):
"""simple docstring"""
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(lowercase_ ) # , add_prefix_space=True)
self.assertListEqual(lowercase_ , lowercase_ )
lowerCAmelCase__ = tokens + [tokenizer.unk_token]
lowerCAmelCase__ = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('Hello world!' , add_special_tokens=lowercase_ ) , [0, 3_14_14, 2_32, 3_28, 2] )
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418' , add_special_tokens=lowercase_ ) , [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] , )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.tokenizer_class.from_pretrained('roberta-base' )
lowerCAmelCase__ = tokenizer.encode('sequence builders' , add_special_tokens=lowercase_ )
lowerCAmelCase__ = tokenizer.encode('multi-sequence build' , add_special_tokens=lowercase_ )
lowerCAmelCase__ = tokenizer.encode(
'sequence builders' , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
lowerCAmelCase__ = tokenizer.encode(
'sequence builders' , 'multi-sequence build' , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
lowerCAmelCase__ = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowerCAmelCase__ = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = """Encode this sequence."""
lowerCAmelCase__ = tokenizer.byte_encoder[""" """.encode('utf-8' )[0]]
# Testing encoder arguments
lowerCAmelCase__ = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowercase_ , lowercase_ )
lowerCAmelCase__ = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowercase_ , lowercase_ )
tokenizer.add_special_tokens({'bos_token': '<s>'} )
lowerCAmelCase__ = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowercase_ , lowercase_ )
# Testing spaces after special tokens
lowerCAmelCase__ = """<mask>"""
tokenizer.add_special_tokens(
{'mask_token': AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ )} ) # mask token has a left space
lowerCAmelCase__ = tokenizer.convert_tokens_to_ids(lowercase_ )
lowerCAmelCase__ = """Encode <mask> sequence"""
lowerCAmelCase__ = """Encode <mask>sequence"""
lowerCAmelCase__ = tokenizer.encode(lowercase_ )
lowerCAmelCase__ = encoded.index(lowercase_ )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowercase_ , lowercase_ )
lowerCAmelCase__ = tokenizer.encode(lowercase_ )
lowerCAmelCase__ = encoded.index(lowercase_ )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowercase_ , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
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(lowercase_ , **lowercase_ )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowerCAmelCase__ = """A, <mask> AllenNLP sentence."""
lowerCAmelCase__ = tokenizer_r.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
lowerCAmelCase__ = tokenizer_p.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
# 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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
lowercase_ , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
lowercase_ , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
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'] , lowercase_ )
self.assertEqual(post_processor_state['add_prefix_space'] , lowercase_ )
self.assertEqual(post_processor_state['trim_offsets'] , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
# 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(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowerCAmelCase__ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ) + 1, len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowerCAmelCase__ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ) + 1, len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowerCAmelCase__ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ), len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowerCAmelCase__ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ), len(lowercase_ ) + 1 + len(lowercase_ )) , )
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(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowerCAmelCase__ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase_ ) + 1, 1 + len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowerCAmelCase__ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase_ ), 1 + len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowerCAmelCase__ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase_ ), 1 + len(lowercase_ ) + 1 + len(lowercase_ )) , )
| 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
"""tokenization_convbert""": ["""ConvBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[str] = ["""ConvBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[Any] = [
"""CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvBertForMaskedLM""",
"""ConvBertForMultipleChoice""",
"""ConvBertForQuestionAnswering""",
"""ConvBertForSequenceClassification""",
"""ConvBertForTokenClassification""",
"""ConvBertLayer""",
"""ConvBertModel""",
"""ConvBertPreTrainedModel""",
"""load_tf_weights_in_convbert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Union[str, Any] = [
"""TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFConvBertForMaskedLM""",
"""TFConvBertForMultipleChoice""",
"""TFConvBertForQuestionAnswering""",
"""TFConvBertForSequenceClassification""",
"""TFConvBertForTokenClassification""",
"""TFConvBertLayer""",
"""TFConvBertModel""",
"""TFConvBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
__snake_case : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 122 | 0 |
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 __magic_name__ ( A : List[str] ):
'''simple docstring'''
a = filter(lambda A : p.requires_grad, model.parameters() )
a = sum([np.prod(p.size() ) for p in model_parameters] )
return params
__lowerCAmelCase : int = logging.getLogger(__name__)
def __magic_name__ ( A : List[Any], A : Dict ):
'''simple docstring'''
if metric == "rouge2":
a = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
a = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
a = "{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." )
a = ModelCheckpoint(
dirpath=A, filename=A, monitor=F"""val_{metric}""", mode="max", save_top_k=3, every_n_epochs=1, )
return checkpoint_callback
def __magic_name__ ( A : str, A : int ):
'''simple docstring'''
return EarlyStopping(
monitor=F"""val_{metric}""", mode="min" if "loss" in metric else "max", patience=A, verbose=A, )
class snake_case__ (pl.Callback ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Any ) -> Union[str, Any]:
a = {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(__lowerCamelCase )
@rank_zero_only
def __UpperCAmelCase ( self : Any , __lowerCamelCase : pl.Trainer , __lowerCamelCase : pl.LightningModule , __lowerCamelCase : str , __lowerCamelCase : List[Any]=True ) -> None:
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
a = 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
a = Path(pl_module.hparams.output_dir )
if type_path == "test":
a = od / "test_results.txt"
a = 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.
a = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
a = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=__lowerCamelCase )
generations_file.parent.mkdir(exist_ok=__lowerCamelCase )
with open(__lowerCamelCase , "a+" ) as writer:
for key in sorted(__lowerCamelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
a = metrics[key]
if isinstance(__lowerCamelCase , torch.Tensor ):
a = val.item()
a = f"""{key}: {val:.6f}\n"""
writer.write(__lowerCamelCase )
if not save_generations:
return
if "preds" in metrics:
a = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(__lowerCamelCase )
@rank_zero_only
def __UpperCAmelCase ( self : Any , __lowerCamelCase : int , __lowerCamelCase : Tuple ) -> Any:
try:
a = pl_module.model.model.num_parameters()
except AttributeError:
a = pl_module.model.num_parameters()
a = count_trainable_parameters(__lowerCamelCase )
# 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 __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : pl.Trainer , __lowerCamelCase : pl.LightningModule ) -> Optional[Any]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(__lowerCamelCase , __lowerCamelCase , "test" )
@rank_zero_only
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : pl.Trainer , __lowerCamelCase : Any ) -> Tuple:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 107 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class snake_case__ (TensorFormatter[Mapping, """torch.Tensor""", Mapping] ):
"""simple docstring"""
def __init__( self : Tuple , __lowerCamelCase : Union[str, Any]=None , **__lowerCamelCase : Any ) -> Optional[Any]:
super().__init__(features=__lowerCamelCase )
a = torch_tensor_kwargs
import torch # noqa import torch at initialization
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Dict ) -> Dict:
import torch
if isinstance(__lowerCamelCase , __lowerCamelCase ) and column:
if all(
isinstance(__lowerCamelCase , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(__lowerCamelCase )
return column
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : List[Any] ) -> str:
import torch
if isinstance(__lowerCamelCase , (str, bytes, type(__lowerCamelCase )) ):
return value
elif isinstance(__lowerCamelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
a = {}
if isinstance(__lowerCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
a = {"dtype": torch.intaa}
elif isinstance(__lowerCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
a = {"dtype": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__lowerCamelCase , PIL.Image.Image ):
a = np.asarray(__lowerCamelCase )
return torch.tensor(__lowerCamelCase , **{**default_dtype, **self.torch_tensor_kwargs} )
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Tuple ) -> List[str]:
import torch
# support for torch, tf, jax etc.
if hasattr(__lowerCamelCase , "__array__" ) and not isinstance(__lowerCamelCase , torch.Tensor ):
a = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__lowerCamelCase , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__lowerCamelCase ) for substruct in data_struct] )
elif isinstance(__lowerCamelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__lowerCamelCase ) for substruct in data_struct] )
return self._tensorize(__lowerCamelCase )
def __UpperCAmelCase ( self : int , __lowerCamelCase : dict ) -> str:
return map_nested(self._recursive_tensorize , __lowerCamelCase , map_list=__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : pa.Table ) -> Mapping:
a = self.numpy_arrow_extractor().extract_row(__lowerCamelCase )
a = self.python_features_decoder.decode_row(__lowerCamelCase )
return self.recursive_tensorize(__lowerCamelCase )
def __UpperCAmelCase ( self : int , __lowerCamelCase : pa.Table ) -> "torch.Tensor":
a = self.numpy_arrow_extractor().extract_column(__lowerCamelCase )
a = self.python_features_decoder.decode_column(__lowerCamelCase , pa_table.column_names[0] )
a = self.recursive_tensorize(__lowerCamelCase )
a = self._consolidate(__lowerCamelCase )
return column
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : pa.Table ) -> Mapping:
a = self.numpy_arrow_extractor().extract_batch(__lowerCamelCase )
a = self.python_features_decoder.decode_batch(__lowerCamelCase )
a = self.recursive_tensorize(__lowerCamelCase )
for column_name in batch:
a = self._consolidate(batch[column_name] )
return batch
| 107 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase__ ( UpperCAmelCase_):
def __init__( self , lowercase , lowercase , lowercase ) -> Union[str, Any]:
super().__init__()
self.register_modules(vqvae=lowercase , unet=lowercase , scheduler=lowercase )
@torch.no_grad()
def __call__( self , lowercase = 1 , lowercase = None , lowercase = 0.0 , lowercase = 5_0 , lowercase = "pil" , lowercase = True , **lowercase , ) -> Union[Tuple, ImagePipelineOutput]:
__UpperCamelCase = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=lowercase , )
__UpperCamelCase = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__UpperCamelCase = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(lowercase )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
__UpperCamelCase = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__UpperCamelCase = {}
if accepts_eta:
__UpperCamelCase = eta
for t in self.progress_bar(self.scheduler.timesteps ):
__UpperCamelCase = self.scheduler.scale_model_input(lowercase , lowercase )
# predict the noise residual
__UpperCamelCase = self.unet(lowercase , lowercase ).sample
# compute the previous noisy sample x_t -> x_t-1
__UpperCamelCase = self.scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
# decode the image latents with the VAE
__UpperCamelCase = self.vqvae.decode(lowercase ).sample
__UpperCamelCase = (image / 2 + 0.5).clamp(0 , 1 )
__UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__UpperCamelCase = self.numpy_to_pil(lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase )
| 243 |
'''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__":
a__ : Optional[int] = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))
print('Googling.....')
a__ : Optional[int] = f'''https://www.google.com/search?q={query}&num=100'''
a__ : Union[str, Any] = requests.get(
url,
headers={'User-Agent': str(UserAgent().random)},
)
try:
a__ : Optional[Any] = (
BeautifulSoup(res.text, 'html.parser')
.find('div', attrs={'class': 'yuRUbf'})
.find('a')
.get('href')
)
except AttributeError:
a__ : Union[str, Any] = parse_qs(
BeautifulSoup(res.text, 'html.parser')
.find('div', attrs={'class': 'kCrYT'})
.find('a')
.get('href')
)['url'][0]
webbrowser.open(link)
| 243 | 1 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
UpperCamelCase_ = namedtuple("covid_data", "cases deaths recovered")
def lowercase__( __UpperCamelCase: str = "https://www.worldometers.info/coronavirus/" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = '//div[@class = \"maincounter-number\"]/span/text()'
return covid_data(*html.fromstring(requests.get(__UpperCamelCase ).content ).xpath(__UpperCamelCase ) )
UpperCamelCase_ = "Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}"
print(fmt.format(*covid_stats()))
| 251 |
"""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
SCREAMING_SNAKE_CASE__ = "▁"
SCREAMING_SNAKE_CASE__ = {"vocab_file": "spiece.model"}
SCREAMING_SNAKE_CASE__ = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}
SCREAMING_SNAKE_CASE__ = {
"google/pegasus-xsum": 512,
}
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , lowercase , lowercase="<pad>" , lowercase="</s>" , lowercase="<unk>" , lowercase="<mask_2>" , lowercase="<mask_1>" , lowercase=None , lowercase=103 , lowercase = None , **lowercase , ) -> None:
lowerCAmelCase = offset
if additional_special_tokens is not None:
if not isinstance(lowercase , lowercase ):
raise TypeError(
f'additional_special_tokens should be of type {type(lowercase )}, but is'
f' {type(lowercase )}' )
lowerCAmelCase = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(lowercase ) , self.offset - 1 )
]
if len(set(lowercase ) ) != len(lowercase ):
raise ValueError(
"""Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
lowerCAmelCase = additional_special_tokens_extended
else:
lowerCAmelCase = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowercase , unk_token=lowercase , mask_token=lowercase , pad_token=lowercase , mask_token_sent=lowercase , offset=lowercase , additional_special_tokens=lowercase , sp_model_kwargs=self.sp_model_kwargs , **lowercase , )
lowerCAmelCase = mask_token_sent
lowerCAmelCase = vocab_file
lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase )
# add special tokens to encoder dict
lowerCAmelCase = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowerCAmelCase = {v: k for k, v in self.encoder.items()}
@property
def _snake_case ( self ) -> int:
return len(self.sp_model ) + self.offset
def _snake_case ( self ) -> Dict[str, int]:
lowerCAmelCase = {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Optional[int]:
lowerCAmelCase = self.__dict__.copy()
lowerCAmelCase = None
return state
def __setstate__( self , lowercase ) -> List[Any]:
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.vocab_file )
def _snake_case ( self , lowercase ) -> List[str]:
return self.sp_model.encode(lowercase , out_type=lowercase )
def _snake_case ( self , lowercase ) -> int:
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowerCAmelCase = self.sp_model.piece_to_id(lowercase )
return sp_id + self.offset
def _snake_case ( self , lowercase ) -> str:
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowerCAmelCase = self.sp_model.IdToPiece(index - self.offset )
return token
def _snake_case ( self , lowercase ) -> Optional[int]:
lowerCAmelCase = []
lowerCAmelCase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowercase ) + token
lowerCAmelCase = []
else:
current_sub_tokens.append(lowercase )
out_string += self.sp_model.decode(lowercase )
return out_string.strip()
def _snake_case ( self , lowercase=False ) -> Tuple:
return 1
def _snake_case ( self , lowercase ) -> Tuple:
lowerCAmelCase = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _snake_case ( self , lowercase , lowercase = None , lowercase = False ) -> List[int]:
if already_has_special_tokens:
return self._special_token_mask(lowercase )
elif token_ids_a is None:
return self._special_token_mask(lowercase ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _snake_case ( self , lowercase , lowercase=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _snake_case ( self , lowercase , lowercase = None ) -> Tuple[str]:
if not os.path.isdir(lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase = os.path.join(
lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase , """wb""" ) as fi:
lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(lowercase )
return (out_vocab_file,)
| 46 | 0 |
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
UpperCamelCase = get_logger(__name__)
UpperCamelCase = 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 _lowerCamelCase :
"""simple docstring"""
@add_start_docstrings(_SCREAMING_SNAKE_CASE )
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class _lowerCamelCase :
"""simple docstring"""
@add_start_docstrings(_SCREAMING_SNAKE_CASE )
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
@add_start_docstrings(_SCREAMING_SNAKE_CASE )
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
for processor in self:
A_ : Any = inspect.signature(processor.__call__ ).parameters
if len(_SCREAMING_SNAKE_CASE ) > 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.''' )
A_ : Any = processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
else:
A_ : Union[str, Any] = processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE )->Union[str, Any]:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not (temperature > 0):
raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}''' )
A_ : Optional[int] = temperature
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
A_ : List[str] = scores / self.temperature
return scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = -float('''Inf''' ) , _SCREAMING_SNAKE_CASE = 1 )->str:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) 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(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) 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}''' )
A_ : int = top_p
A_ : Any = filter_value
A_ : str = min_tokens_to_keep
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
A_ , A_ : List[Any] = lax.top_k(_SCREAMING_SNAKE_CASE , scores.shape[-1] )
A_ : int = jnp.full_like(_SCREAMING_SNAKE_CASE , self.filter_value )
A_ : str = jax.nn.softmax(_SCREAMING_SNAKE_CASE , axis=-1 ).cumsum(axis=-1 )
A_ : Optional[int] = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
A_ : Tuple = jnp.roll(_SCREAMING_SNAKE_CASE , 1 )
score_mask |= score_mask.at[:, 0].set(_SCREAMING_SNAKE_CASE )
# min tokens to keep
A_ : Union[str, Any] = score_mask.at[:, : self.min_tokens_to_keep].set(_SCREAMING_SNAKE_CASE )
A_ : Tuple = jnp.where(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : int = jax.lax.sort_key_val(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )[-1]
return next_scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = -float('''Inf''' ) , _SCREAMING_SNAKE_CASE = 1 )->Optional[Any]:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or top_k <= 0:
raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}''' )
A_ : Tuple = max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Any = filter_value
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
A_ , A_ : Optional[Any] = scores.shape
A_ : int = jnp.full(batch_size * vocab_size , self.filter_value )
A_ : Optional[int] = min(self.top_k , scores.shape[-1] ) # Safety check
A_ , A_ : Union[str, Any] = lax.top_k(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : List[Any] = jnp.broadcast_to((jnp.arange(_SCREAMING_SNAKE_CASE ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
A_ : Tuple = topk_scores.flatten()
A_ : List[str] = topk_indices.flatten() + shift
A_ : Optional[int] = next_scores_flat.at[topk_indices_flat].set(_SCREAMING_SNAKE_CASE )
A_ : List[str] = next_scores_flat.reshape(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return next_scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE )->List[Any]:
'''simple docstring'''
A_ : Tuple = bos_token_id
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
A_ : Optional[int] = jnp.full(scores.shape , -float('''inf''' ) )
A_ : Dict = 1 - jnp.bool_(cur_len - 1 )
A_ : Tuple = jnp.where(_SCREAMING_SNAKE_CASE , new_scores.at[:, self.bos_token_id].set(0 ) , _SCREAMING_SNAKE_CASE )
return scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Tuple:
'''simple docstring'''
A_ : Any = max_length
A_ : Any = eos_token_id
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
A_ : int = jnp.full(scores.shape , -float('''inf''' ) )
A_ : Dict = 1 - jnp.bool_(cur_len - self.max_length + 1 )
A_ : Tuple = jnp.where(_SCREAMING_SNAKE_CASE , new_scores.at[:, self.eos_token_id].set(0 ) , _SCREAMING_SNAKE_CASE )
return scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Union[str, Any]:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or min_length < 0:
raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}''' )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or eos_token_id < 0:
raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}''' )
A_ : str = min_length
A_ : int = eos_token_id
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
A_ : List[Any] = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
A_ : Dict = jnp.where(_SCREAMING_SNAKE_CASE , scores.at[:, self.eos_token_id].set(-float('''inf''' ) ) , _SCREAMING_SNAKE_CASE )
return scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Optional[int]:
'''simple docstring'''
A_ : str = list(_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = begin_index
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->List[Any]:
'''simple docstring'''
A_ : Optional[Any] = 1 - jnp.bool_(cur_len - self.begin_index )
A_ : Any = jnp.where(_SCREAMING_SNAKE_CASE , scores.at[:, self.begin_suppress_tokens].set(-float('''inf''' ) ) , _SCREAMING_SNAKE_CASE )
return scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE )->Any:
'''simple docstring'''
A_ : Union[str, Any] = list(_SCREAMING_SNAKE_CASE )
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
A_ : str = scores.at[..., self.suppress_tokens].set(-float('''inf''' ) )
return scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE )->Optional[int]:
'''simple docstring'''
A_ : Optional[int] = dict(_SCREAMING_SNAKE_CASE )
# 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.
A_ : List[str] = 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:
A_ : List[Any] = force_token_array.at[index].set(_SCREAMING_SNAKE_CASE )
A_ : List[Any] = jnp.intaa(_SCREAMING_SNAKE_CASE )
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->jnp.ndarray:
'''simple docstring'''
def _force_token(_SCREAMING_SNAKE_CASE ):
A_ : str = scores.shape[0]
A_ : Tuple = self.force_token_array[generation_idx]
A_ : Optional[int] = jnp.ones_like(_SCREAMING_SNAKE_CASE , dtype=scores.dtype ) * -float('''inf''' )
A_ : Dict = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
A_ : Dict = lax.dynamic_update_slice(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (0, current_token) )
return new_scores
A_ : Optional[Any] = 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(_SCREAMING_SNAKE_CASE ) , lambda: scores , ) , )
return scores
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Dict:
'''simple docstring'''
A_ : Dict = generate_config.eos_token_id
A_ : Optional[int] = generate_config.no_timestamps_token_id
A_ : List[Any] = generate_config.no_timestamps_token_id + 1
A_ : List[str] = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(_SCREAMING_SNAKE_CASE , '''max_initial_timestamp_index''' ):
A_ : List[Any] = generate_config.max_initial_timestamp_index
else:
A_ : List[Any] = model_config.vocab_size
if self.max_initial_timestamp_index is None:
A_ : int = model_config.vocab_size
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Dict:
'''simple docstring'''
A_ : Dict = scores.at[:, self.no_timestamps_token_id].set(-float('''inf''' ) )
def handle_pairs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ : List[Any] = jnp.where((cur_len - self.begin_index) >= 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Optional[int] = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , _SCREAMING_SNAKE_CASE , )
A_ : Union[str, Any] = jnp.where((cur_len - self.begin_index) < 2 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : List[Any] = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
return jnp.where(
_SCREAMING_SNAKE_CASE , 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''' ) ) , ) , _SCREAMING_SNAKE_CASE , )
A_ : Any = jax.vmap(_SCREAMING_SNAKE_CASE )(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : List[str] = jnp.where(cur_len == self.begin_index , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , _SCREAMING_SNAKE_CASE , )
A_ : Any = self.timestamp_begin + self.max_initial_timestamp_index
A_ : Tuple = jnp.where(
_SCREAMING_SNAKE_CASE , scores.at[:, last_allowed + 1 :].set(-float('''inf''' ) ) , _SCREAMING_SNAKE_CASE , )
# if sum of probability over timestamps is above any other token, sample timestamp
A_ : Optional[int] = jax.nn.log_softmax(_SCREAMING_SNAKE_CASE , axis=-1 )
def handle_cumulative_probs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ : int = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
A_ : Optional[int] = 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''' ) ) , _SCREAMING_SNAKE_CASE , )
A_ : str = jax.vmap(_SCREAMING_SNAKE_CASE )(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return scores
| 65 |
import math
import random
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
UpperCamelCase = 0.02
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : str = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(SCREAMING_SNAKE_CASE ):
# Forward propagation
A_ : Optional[Any] = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
A_ : Any = (expected / 100) - layer_a
# Error delta
A_ : List[str] = layer_1_error * sigmoid_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase = int(input("""Expected value: """))
UpperCamelCase = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 65 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 211 |
'''simple docstring'''
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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}
class __A ( A , A ):
'''simple docstring'''
__lowerCamelCase : Tuple = 'resnet'
__lowerCamelCase : Any = ['basic', 'bottleneck']
def __init__(self , A=3 , A=64 , A=[256, 512, 1_024, 2_048] , A=[3, 4, 6, 3] , A="bottleneck" , A="relu" , A=False , A=None , A=None , **A , ) -> Dict:
"""simple docstring"""
super().__init__(**A )
if layer_type not in self.layer_types:
raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' )
_a = num_channels
_a = embedding_size
_a = hidden_sizes
_a = depths
_a = layer_type
_a = hidden_act
_a = downsample_in_first_stage
_a = ['''stem'''] + [f'''stage{idx}''' for idx in range(1 , len(A ) + 1 )]
_a , _a = get_aligned_output_features_output_indices(
out_features=A , out_indices=A , stage_names=self.stage_names )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any = version.parse('1.11' )
@property
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def a__ (self ) -> float:
"""simple docstring"""
return 1E-3
| 211 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return 10 - x * x
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
# Bolzano theory in order to find if there is a root between a and b
if equation(__UpperCAmelCase ) * equation(__UpperCAmelCase ) >= 0:
raise ValueError("""Wrong space!""" )
_lowercase : str = a
while (b - a) >= 0.0_1:
# Find middle point
_lowercase : int = (a + b) / 2
# Check if middle point is root
if equation(__UpperCAmelCase ) == 0.0:
break
# Decide the side to repeat the steps
if equation(__UpperCAmelCase ) * equation(__UpperCAmelCase ) < 0:
_lowercase : Tuple = c
else:
_lowercase : Tuple = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 336 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=0.9_9_9 , __UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_lowercase : str = []
for i in range(__UpperCAmelCase ):
_lowercase : Any = i / num_diffusion_timesteps
_lowercase : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ) , __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
class UpperCamelCase ( snake_case , snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [e.name for e in KarrasDiffusionSchedulers]
SCREAMING_SNAKE_CASE_ : str = 2
@register_to_config
def __init__( self ,UpperCAmelCase_ = 10_00 ,UpperCAmelCase_ = 0.00085 ,UpperCAmelCase_ = 0.012 ,UpperCAmelCase_ = "linear" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = "epsilon" ,UpperCAmelCase_ = "linspace" ,UpperCAmelCase_ = 0 ,):
if trained_betas is not None:
_lowercase : str = torch.tensor(UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
_lowercase : Optional[Any] = torch.linspace(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_lowercase : Any = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,UpperCAmelCase_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_lowercase : Any = betas_for_alpha_bar(UpperCAmelCase_ )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
_lowercase : Tuple = 1.0 - self.betas
_lowercase : Dict = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ):
if schedule_timesteps is None:
_lowercase : Optional[int] = self.timesteps
_lowercase : Union[str, Any] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_lowercase : Optional[Any] = 1 if len(UpperCAmelCase_ ) > 1 else 0
else:
_lowercase : Dict = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
_lowercase : List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCamelCase__ ( self ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
_lowercase : str = self.index_for_timestep(UpperCAmelCase_ )
if self.state_in_first_order:
_lowercase : Optional[Any] = self.sigmas[step_index]
else:
_lowercase : Dict = self.sigmas_interpol[step_index]
_lowercase : Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,):
_lowercase : List[str] = num_inference_steps
_lowercase : Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_lowercase : Dict = np.linspace(0 ,num_train_timesteps - 1 ,UpperCAmelCase_ ,dtype=UpperCAmelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_lowercase : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : str = (np.arange(0 ,UpperCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_lowercase : str = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : Optional[int] = (np.arange(UpperCAmelCase_ ,0 ,-step_ratio )).round().copy().astype(UpperCAmelCase_ )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_lowercase : Union[str, Any] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_lowercase : Optional[Any] = torch.from_numpy(np.log(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_lowercase : List[str] = np.interp(UpperCAmelCase_ ,np.arange(0 ,len(UpperCAmelCase_ ) ) ,UpperCAmelCase_ )
_lowercase : int = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_lowercase : Any = torch.from_numpy(UpperCAmelCase_ ).to(device=UpperCAmelCase_ )
# interpolate sigmas
_lowercase : List[str] = sigmas.log().lerp(sigmas.roll(1 ).log() ,0.5 ).exp()
_lowercase : Union[str, Any] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
_lowercase : Tuple = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(UpperCAmelCase_ ).startswith("""mps""" ):
# mps does not support float64
_lowercase : Tuple = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=torch.floataa )
else:
_lowercase : str = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ )
# interpolate timesteps
_lowercase : int = self.sigma_to_t(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=timesteps.dtype )
_lowercase : Union[str, Any] = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,dim=-1 ).flatten()
_lowercase : str = torch.cat([timesteps[:1], interleaved_timesteps] )
_lowercase : List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_lowercase : Optional[Any] = defaultdict(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# get log sigma
_lowercase : Optional[Any] = sigma.log()
# get distribution
_lowercase : Optional[int] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
_lowercase : Tuple = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
_lowercase : List[Any] = low_idx + 1
_lowercase : int = self.log_sigmas[low_idx]
_lowercase : Any = self.log_sigmas[high_idx]
# interpolate sigmas
_lowercase : Any = (low - log_sigma) / (low - high)
_lowercase : Dict = w.clamp(0 ,1 )
# transform interpolation to time range
_lowercase : List[str] = (1 - w) * low_idx + w * high_idx
_lowercase : Optional[int] = t.view(sigma.shape )
return t
@property
def lowerCamelCase__ ( self ):
return self.sample is None
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = True ,):
_lowercase : Optional[int] = self.index_for_timestep(UpperCAmelCase_ )
# advance index counter by 1
_lowercase : str = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_lowercase : Any = self.sigmas[step_index]
_lowercase : Any = self.sigmas_interpol[step_index + 1]
_lowercase : Tuple = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
_lowercase : Union[str, Any] = self.sigmas[step_index - 1]
_lowercase : int = self.sigmas_interpol[step_index]
_lowercase : Tuple = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_lowercase : Any = 0
_lowercase : int = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_lowercase : Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : Optional[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_lowercase : str = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_lowercase : List[str] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_lowercase : Any = sigma_interpol - sigma_hat
# store for 2nd order step
_lowercase : List[Any] = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
_lowercase : Optional[Any] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
_lowercase : Optional[Any] = sigma_next - sigma_hat
_lowercase : Any = self.sample
_lowercase : Optional[int] = None
_lowercase : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_lowercase : int = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_ ):
# mps does not support float64
_lowercase : str = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
_lowercase : Any = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
_lowercase : List[Any] = self.timesteps.to(original_samples.device )
_lowercase : Union[str, Any] = timesteps.to(original_samples.device )
_lowercase : List[Any] = [self.index_for_timestep(UpperCAmelCase_ ,UpperCAmelCase_ ) for t in timesteps]
_lowercase : Optional[Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_lowercase : List[Any] = sigma.unsqueeze(-1 )
_lowercase : int = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
return self.config.num_train_timesteps
| 336 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
stooge(a_ , 0 , len(a_ ) - 1 )
return arr
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
A__ = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
A__ = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(a_ , a_ , (h - t) )
# Recursively sort last 2/3 elements
stooge(a_ , i + t , (a_) )
# Recursively sort first 2/3 elements
stooge(a_ , a_ , (h - t) )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = input("Enter numbers separated by a comma:\n").strip()
SCREAMING_SNAKE_CASE = [int(item) for item in user_input.split(",")]
print(stooge_sort(unsorted))
| 247 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class _SCREAMING_SNAKE_CASE( A ):
SCREAMING_SNAKE_CASE_ : torch.FloatTensor
SCREAMING_SNAKE_CASE_ : torch.FloatTensor
SCREAMING_SNAKE_CASE_ : Optional[torch.FloatTensor] = None
class _SCREAMING_SNAKE_CASE( A , A ):
SCREAMING_SNAKE_CASE_ : Any = 2
@register_to_config
def __init__( self ,SCREAMING_SNAKE_CASE__ = 0.0_2 ,SCREAMING_SNAKE_CASE__ = 1_00 ,SCREAMING_SNAKE_CASE__ = 1.0_0_7 ,SCREAMING_SNAKE_CASE__ = 80 ,SCREAMING_SNAKE_CASE__ = 0.0_5 ,SCREAMING_SNAKE_CASE__ = 50 ,) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = sigma_max
# setable values
__SCREAMING_SNAKE_CASE :int = None
__SCREAMING_SNAKE_CASE :np.IntTensor = None
__SCREAMING_SNAKE_CASE :torch.FloatTensor = None # sigma(t_i)
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ = None ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = num_inference_steps
__SCREAMING_SNAKE_CASE :int = np.arange(0 ,self.num_inference_steps )[::-1].copy()
__SCREAMING_SNAKE_CASE :Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Any = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
__SCREAMING_SNAKE_CASE :List[str] = torch.tensor(SCREAMING_SNAKE_CASE__ ,dtype=torch.floataa ,device=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ = None ) -> Tuple[torch.FloatTensor, float]:
"""simple docstring"""
if self.config.s_min <= sigma <= self.config.s_max:
__SCREAMING_SNAKE_CASE :List[str] = min(self.config.s_churn / self.num_inference_steps ,2**0.5 - 1 )
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = 0
# sample eps ~ N(0, S_noise^2 * I)
__SCREAMING_SNAKE_CASE :Optional[int] = self.config.s_noise * randn_tensor(sample.shape ,generator=SCREAMING_SNAKE_CASE__ ).to(sample.device )
__SCREAMING_SNAKE_CASE :List[str] = sigma + gamma * sigma
__SCREAMING_SNAKE_CASE :str = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ = True ,) -> Union[KarrasVeOutput, Tuple]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :str = sample_hat + sigma_hat * model_output
__SCREAMING_SNAKE_CASE :Tuple = (sample_hat - pred_original_sample) / sigma_hat
__SCREAMING_SNAKE_CASE :List[Any] = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=SCREAMING_SNAKE_CASE__ ,derivative=SCREAMING_SNAKE_CASE__ ,pred_original_sample=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ = True ,) -> Union[KarrasVeOutput, Tuple]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = sample_prev + sigma_prev * model_output
__SCREAMING_SNAKE_CASE :List[Any] = (sample_prev - pred_original_sample) / sigma_prev
__SCREAMING_SNAKE_CASE :Dict = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=SCREAMING_SNAKE_CASE__ ,derivative=SCREAMING_SNAKE_CASE__ ,pred_original_sample=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
raise NotImplementedError() | 191 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase__() ->List[Any]:
"""simple docstring"""
from torch.utils.cpp_extension import load
lowercase__ : Any= Path(A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
lowercase__ : Any= [
root / filename
for filename in [
"vision.cpp",
os.path.join("cpu" , "ms_deform_attn_cpu.cpp" ),
os.path.join("cuda" , "ms_deform_attn_cuda.cu" ),
]
]
load(
"MultiScaleDeformableAttention" , A , with_cuda=A , extra_include_paths=[str(A )] , extra_cflags=["-DWITH_CUDA=1"] , extra_cuda_cflags=[
"-DCUDA_HAS_FP16=1",
"-D__CUDA_NO_HALF_OPERATORS__",
"-D__CUDA_NO_HALF_CONVERSIONS__",
"-D__CUDA_NO_HALF2_OPERATORS__",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 150 |
"""simple docstring"""
from __future__ import annotations
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__=None ):
'''simple docstring'''
lowercase__ : Union[str, Any]= data
lowercase__ : Optional[Any]= None
def __repr__( self ):
'''simple docstring'''
lowercase__ : str= []
lowercase__ : Tuple= self
while temp:
string_rep.append(F'''{temp.data}''' )
lowercase__ : Optional[int]= temp.next
return "->".join(snake_case__ )
def lowercase__(A ) ->Dict:
"""simple docstring"""
if not elements_list:
raise Exception("The Elements List is empty" )
lowercase__ : Optional[int]= Node(elements_list[0] )
for i in range(1 , len(A ) ):
lowercase__ : Optional[Any]= Node(elements_list[i] )
lowercase__ : str= current.next
return head
def lowercase__(A ) ->None:
"""simple docstring"""
if head_node is not None and isinstance(A , A ):
print_reverse(head_node.next )
print(head_node.data )
def lowercase__() ->str:
"""simple docstring"""
from doctest import testmod
testmod()
lowercase__ : Optional[int]= make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(A )
print("Elements in Reverse:" )
print_reverse(A )
if __name__ == "__main__":
main()
| 150 | 1 |
'''simple docstring'''
import math
class __UpperCamelCase :
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
__a : Dict = 0.0
__a : Optional[int] = 0.0
for i in range(len(__a ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def __UpperCAmelCase ( self , __a , __a , __a , __a ):
'''simple docstring'''
for i in range(len(__a ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def lowerCamelCase ():
# Training Examples ( m, n )
__a : int = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
__a : Optional[Any] = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
__a : Optional[Any] = SelfOrganizingMap()
__a : Any = 3
__a : Tuple = 0.5
for _ in range(_SCREAMING_SNAKE_CASE ):
for j in range(len(_SCREAMING_SNAKE_CASE ) ):
# training sample
__a : List[str] = training_samples[j]
# Compute the winning vector
__a : Union[str, Any] = self_organizing_map.get_winner(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Update the winning vector
__a : int = self_organizing_map.update(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# classify test sample
__a : Union[str, Any] = [0, 0, 0, 1]
__a : Optional[int] = self_organizing_map.get_winner(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# results
print(F"""Clusters that the test sample belongs to : {winner}""" )
print(F"""Weights that have been trained : {weights}""" )
# running the main() function
if __name__ == "__main__":
main()
| 27 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return 12
@property
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return 12
@property
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
return 32
@property
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(a__ )
@property
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ = 12
snake_case_ = 12
snake_case_ = {
"attention_bias": True,
"cross_attention_dim": 32,
"attention_head_dim": height * width,
"num_attention_heads": 1,
"num_vector_embeds": self.num_embed,
"num_embeds_ada_norm": self.num_embeds_ada_norm,
"norm_num_groups": 32,
"sample_size": width,
"activation_fn": "geglu-approximate",
}
snake_case_ = TransformeraDModel(**a__ )
return model
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = "cpu"
snake_case_ = self.dummy_vqvae
snake_case_ = self.dummy_text_encoder
snake_case_ = self.dummy_tokenizer
snake_case_ = self.dummy_transformer
snake_case_ = VQDiffusionScheduler(self.num_embed )
snake_case_ = LearnedClassifierFreeSamplingEmbeddings(learnable=a__ )
snake_case_ = VQDiffusionPipeline(
vqvae=a__ , text_encoder=a__ , tokenizer=a__ , transformer=a__ , scheduler=a__ , learned_classifier_free_sampling_embeddings=a__ , )
snake_case_ = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
snake_case_ = "teddy bear playing in the pool"
snake_case_ = torch.Generator(device=a__ ).manual_seed(0 )
snake_case_ = pipe([prompt] , generator=a__ , num_inference_steps=2 , output_type="np" )
snake_case_ = output.images
snake_case_ = torch.Generator(device=a__ ).manual_seed(0 )
snake_case_ = pipe(
[prompt] , generator=a__ , output_type="np" , return_dict=a__ , num_inference_steps=2 )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
snake_case_ = np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ = "cpu"
snake_case_ = self.dummy_vqvae
snake_case_ = self.dummy_text_encoder
snake_case_ = self.dummy_tokenizer
snake_case_ = self.dummy_transformer
snake_case_ = VQDiffusionScheduler(self.num_embed )
snake_case_ = LearnedClassifierFreeSamplingEmbeddings(
learnable=a__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
snake_case_ = VQDiffusionPipeline(
vqvae=a__ , text_encoder=a__ , tokenizer=a__ , transformer=a__ , scheduler=a__ , learned_classifier_free_sampling_embeddings=a__ , )
snake_case_ = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
snake_case_ = "teddy bear playing in the pool"
snake_case_ = torch.Generator(device=a__ ).manual_seed(0 )
snake_case_ = pipe([prompt] , generator=a__ , num_inference_steps=2 , output_type="np" )
snake_case_ = output.images
snake_case_ = torch.Generator(device=a__ ).manual_seed(0 )
snake_case_ = pipe(
[prompt] , generator=a__ , output_type="np" , return_dict=a__ , num_inference_steps=2 )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
snake_case_ = np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy" )
snake_case_ = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq" )
snake_case_ = pipeline.to(a__ )
pipeline.set_progress_bar_config(disable=a__ )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
snake_case_ = torch.Generator(device=a__ ).manual_seed(0 )
snake_case_ = pipeline(
"teddy bear playing in the pool" , num_images_per_prompt=1 , generator=a__ , output_type="np" , )
snake_case_ = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 85 | 0 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
SCREAMING_SNAKE_CASE = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
datarow=DataRow("", "|", "|"),
padding=1,
with_header_hide=None,
)
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = {"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}
SCREAMING_SNAKE_CASE = [
{
"type": "header",
"text": {
"type": "plain_text",
"text": f'🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results',
"emoji": True,
},
}
]
SCREAMING_SNAKE_CASE = 0
for log in Path().glob("*.log"):
SCREAMING_SNAKE_CASE = 0
with open(log, "r") as f:
for line in f:
SCREAMING_SNAKE_CASE = json.loads(line)
if line.get("nodeid", "") != "":
SCREAMING_SNAKE_CASE = line["nodeid"]
if line.get("duration", None) is not None:
SCREAMING_SNAKE_CASE = f'{line["duration"]:.4f}'
if line.get("outcome", "") == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split("_")[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
SCREAMING_SNAKE_CASE = []
log.unlink()
SCREAMING_SNAKE_CASE = ""
SCREAMING_SNAKE_CASE = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += f"*{name[1:]}: {num_failed} failed test*\n"
else:
message += f"*{name[1:]}: {num_failed} failed tests*\n"
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = {}
for test in failed_tests:
SCREAMING_SNAKE_CASE = test[0].split("::")
SCREAMING_SNAKE_CASE = data[0].split("/")[-1]
if data[0] not in filesafailed:
SCREAMING_SNAKE_CASE = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
SCREAMING_SNAKE_CASE = [test[0] for test in failed_table]
SCREAMING_SNAKE_CASE = list(set(files))
# Count number of instances in failed_tests
SCREAMING_SNAKE_CASE = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
SCREAMING_SNAKE_CASE = tabulate(
table,
headers=["Test Location", "Num Failed"],
tablefmt=hf_table_format,
stralign="right",
)
message += f"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3000:
SCREAMING_SNAKE_CASE = "Too many failed tests, please see the full report in the Action results."
SCREAMING_SNAKE_CASE = len(err) + 10
SCREAMING_SNAKE_CASE = message[: 3000 - offset] + f'\n...\n```\n{err}'
print(f'### {message}')
else:
SCREAMING_SNAKE_CASE = "No failed tests! 🤗"
print(f'## {message}')
payload.append(no_error_payload)
if os.environ.get("TEST_TYPE", "") != "":
from slack_sdk import WebClient
SCREAMING_SNAKE_CASE = WebClient(token=os.environ["SLACK_API_TOKEN"])
if message != "No failed tests! 🤗":
SCREAMING_SNAKE_CASE = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": message,
},
}
payload.append(md_report)
SCREAMING_SNAKE_CASE = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": "*For more details:*",
},
"accessory": {
"type": "button",
"text": {
"type": "plain_text",
"text": "Check Action results",
"emoji": True,
},
"url": f'https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
payload.append(action_button)
SCREAMING_SNAKE_CASE = {
"type": "context",
"elements": [
{
"type": "plain_text",
"text": f'Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}',
}
],
}
payload.append(date_report)
SCREAMING_SNAKE_CASE = client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
SCREAMING_SNAKE_CASE = response.data["ts"]
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
SCREAMING_SNAKE_CASE = ""
for i, row in enumerate(test_failures):
if row[0] != test_class:
SCREAMING_SNAKE_CASE = row[0]
else:
SCREAMING_SNAKE_CASE = ""
SCREAMING_SNAKE_CASE = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f'Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```',
},
}
client.chat_postMessage(
channel="#accelerate-ci-daily",
thread_ts=ts,
blocks=[payload],
)
| 230 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''tokenizer''']
lowercase__ = '''ViTImageProcessor'''
lowercase__ = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Optional[Any] , snake_case_ : Union[str, Any]=None , snake_case_ : Dict=None , **snake_case_ : Optional[Any] ) -> List[str]:
'''simple docstring'''
A__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , snake_case_ , )
A__ = kwargs.pop("feature_extractor" )
A__ = 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__(snake_case_ , snake_case_ )
def __call__( self : int , snake_case_ : Union[str, Any]=None , snake_case_ : int=None , snake_case_ : Dict=None , snake_case_ : int=None , **snake_case_ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError("You have to specify either text, visual prompt or images." )
if text is not None and visual_prompt is not None:
raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt." )
if text is not None:
A__ = self.tokenizer(snake_case_ , return_tensors=snake_case_ , **snake_case_ )
if visual_prompt is not None:
A__ = self.image_processor(snake_case_ , return_tensors=snake_case_ , **snake_case_ )
if images is not None:
A__ = self.image_processor(snake_case_ , return_tensors=snake_case_ , **snake_case_ )
if visual_prompt is not None and images is not None:
A__ = {
"pixel_values": image_features.pixel_values,
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
A__ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
A__ = {
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**snake_case_ ) , tensor_type=snake_case_ )
def __magic_name__ ( self : Tuple , *snake_case_ : List[str] , **snake_case_ : Optional[int] ) -> str:
'''simple docstring'''
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def __magic_name__ ( self : Optional[Any] , *snake_case_ : Any , **snake_case_ : List[Any] ) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
def __magic_name__ ( self : int ) -> Optional[int]:
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , snake_case_ , )
return self.image_processor_class
@property
def __magic_name__ ( self : int ) -> int:
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , snake_case_ , )
return self.image_processor
| 230 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
lowercase : Optional[int] = int(input('Enter the base: ').strip())
lowercase : Tuple = int(input('Enter the exponent: ').strip())
lowercase : str = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
lowercase : Tuple = 1 / result
print(f"{base} to the power of {exponent} is {result}") | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vertex:
print(a , " -> " , " -> ".join([str(a ) for j in self.vertex[i]] ) )
def _lowerCamelCase ( self :List[Any] , a :int , a :int ) -> None:
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(a )
else:
# else make a new vertex
__UpperCamelCase : Optional[Any] = [to_vertex]
def _lowerCamelCase ( self :Tuple ) -> None:
# visited array for storing already visited nodes
__UpperCamelCase : Dict = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(a , a )
def _lowerCamelCase ( self :Any , a :int , a :list ) -> None:
# mark start vertex as visited
__UpperCamelCase : int = True
print(a , end=" " )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(a , a )
if __name__ == "__main__":
lowercase : Dict = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('DFS:')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3 | 232 | 1 |
from __future__ import annotations
import math
__magic_name__ = "2020.9.26"
__magic_name__ = "xcodz-dot, cclaus, dhruvmanila"
def _lowerCAmelCase ( A__: float , A__: float , A__: float , A__: float , A__: float ):
'''simple docstring'''
if not all(isinstance(A__ , (float, int) ) for val in locals().values() ):
UpperCAmelCase = F"""Input values must either be float or int: {list(locals().values() )}"""
raise TypeError(A__ )
UpperCAmelCase = ((x * distance) / (z + distance)) * scale
UpperCAmelCase = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def _lowerCAmelCase ( A__: float , A__: float , A__: float , A__: str , A__: float ):
'''simple docstring'''
if not isinstance(A__ , A__ ):
raise TypeError('''Axis must be a str''' )
UpperCAmelCase = locals()
del input_variables["axis"]
if not all(isinstance(A__ , (float, int) ) for val in input_variables.values() ):
UpperCAmelCase = (
'''Input values except axis must either be float or int: '''
F"""{list(input_variables.values() )}"""
)
raise TypeError(A__ )
UpperCAmelCase = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
UpperCAmelCase = x * math.cos(A__ ) - y * math.sin(A__ )
UpperCAmelCase = y * math.cos(A__ ) + x * math.sin(A__ )
UpperCAmelCase = z
elif axis == "x":
UpperCAmelCase = y * math.cos(A__ ) - z * math.sin(A__ )
UpperCAmelCase = z * math.cos(A__ ) + y * math.sin(A__ )
UpperCAmelCase = x
elif axis == "y":
UpperCAmelCase = x * math.cos(A__ ) - z * math.sin(A__ )
UpperCAmelCase = z * math.cos(A__ ) + x * math.sin(A__ )
UpperCAmelCase = y
else:
raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')
| 152 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
__magic_name__ = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
__magic_name__ = (
subprocess.check_output(f'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("utf-8").split()
)
__magic_name__ = "|".join(sys.argv[1:])
__magic_name__ = re.compile(rf'''^({joined_dirs}).*?\.py$''')
__magic_name__ = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| 152 | 1 |
from __future__ import annotations
def lowercase( UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None ) -> None:
'''simple docstring'''
if start is None:
UpperCamelCase = 0
if end is None:
UpperCamelCase = len(UpperCamelCase_ ) - 1
if start >= end:
return
UpperCamelCase = (start + end) // 2
slowsort(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
slowsort(UpperCamelCase_ , mid + 1 , UpperCamelCase_ )
if sequence[end] < sequence[mid]:
UpperCamelCase , UpperCamelCase = sequence[mid], sequence[end]
slowsort(UpperCamelCase_ , UpperCamelCase_ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 343 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["""ConvNextFeatureExtractor"""]
_SCREAMING_SNAKE_CASE = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 343 | 1 |
'''simple docstring'''
from manim import *
class __lowerCamelCase ( a_ ):
"""simple docstring"""
def A ( self : Any):
_A : Any = Rectangle(height=0.5 , width=0.5)
_A : List[str] = Rectangle(height=0.46 , width=0.46).set_stroke(width=0)
_A : List[str] = Rectangle(height=0.25 , width=0.25)
_A : Optional[Any] = [mem.copy() for i in range(6)]
_A : Tuple = [mem.copy() for i in range(6)]
_A : Dict = VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0)
_A : Optional[int] = VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0)
_A : str = VGroup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0)
_A : Dict = Text('CPU' , font_size=24)
_A : List[str] = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE)
cpu.move_to([-2.5, -0.5, 0])
self.add(_SCREAMING_SNAKE_CASE)
_A : Union[str, Any] = [mem.copy() for i in range(4)]
_A : Tuple = VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0)
_A : Union[str, Any] = Text('GPU' , font_size=24)
_A : Optional[int] = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE)
gpu.move_to([-1, -1, 0])
self.add(_SCREAMING_SNAKE_CASE)
_A : Tuple = [mem.copy() for i in range(6)]
_A : List[str] = VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0)
_A : int = Text('Model' , font_size=24)
_A : List[str] = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE)
model.move_to([3, -1.0, 0])
self.add(_SCREAMING_SNAKE_CASE)
_A : Union[str, Any] = []
_A : str = []
for i, rect in enumerate(_SCREAMING_SNAKE_CASE):
_A : Tuple = fill.copy().set_fill(_SCREAMING_SNAKE_CASE , opacity=0.8)
target.move_to(_SCREAMING_SNAKE_CASE)
model_arr.append(_SCREAMING_SNAKE_CASE)
_A : Optional[int] = Rectangle(height=0.46 , width=0.46).set_stroke(width=0.0).set_fill(_SCREAMING_SNAKE_CASE , opacity=0.8)
cpu_target.move_to(cpu_left_col_base[i])
model_cpu_arr.append(_SCREAMING_SNAKE_CASE)
self.add(*_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE)
_A : int = [meta_mem.copy() for i in range(6)]
_A : List[str] = [meta_mem.copy() for i in range(6)]
_A : Tuple = VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0)
_A : Optional[int] = VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0)
_A : Union[str, Any] = VGroup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0)
_A : Optional[int] = Text('Disk' , font_size=24)
_A : Tuple = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE)
disk.move_to([-4, -1.25, 0])
self.add(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
_A : Union[str, Any] = Square(side_length=2.2)
key.move_to([-5, 2, 0])
_A : Optional[int] = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0])
self.add(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
_A : Dict = MarkupText(
F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(_SCREAMING_SNAKE_CASE , DOWN * 2.4 , aligned_edge=key_text.get_left())
self.add(_SCREAMING_SNAKE_CASE)
_A : str = MarkupText(
F'Now watch as an input is passed through the model\nand how the memory is utilized and handled.' , font_size=24 , )
step_a.move_to([2, 2, 0])
self.play(Write(_SCREAMING_SNAKE_CASE))
_A : Tuple = Square(0.3)
input.set_fill(_SCREAMING_SNAKE_CASE , opacity=1.0)
input.set_stroke(width=0.0)
input.next_to(model_base[0] , _SCREAMING_SNAKE_CASE , buff=0.5)
self.play(Write(_SCREAMING_SNAKE_CASE))
input.generate_target()
input.target.next_to(model_arr[0] , direction=_SCREAMING_SNAKE_CASE , buff=0.02)
self.play(MoveToTarget(_SCREAMING_SNAKE_CASE))
self.play(FadeOut(_SCREAMING_SNAKE_CASE))
_A : str = Arrow(start=_SCREAMING_SNAKE_CASE , end=_SCREAMING_SNAKE_CASE , color=_SCREAMING_SNAKE_CASE , buff=0.5)
a.next_to(model_arr[0].get_left() , _SCREAMING_SNAKE_CASE , buff=0.2)
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0])
_A : Tuple = MarkupText(
F'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.' , font_size=24 , )
step_a.move_to([2, 2, 0])
self.play(Write(_SCREAMING_SNAKE_CASE , run_time=3))
_A : Optional[Any] = {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}
self.play(
Write(_SCREAMING_SNAKE_CASE) , Circumscribe(model_arr[0] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , Circumscribe(model_cpu_arr[0] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , Circumscribe(gpu_rect[0] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , )
self.play(MoveToTarget(model_cpu_arr[0]))
_A : int = a.copy()
for i in range(6):
a_c.next_to(model_arr[i].get_right() + 0.02 , _SCREAMING_SNAKE_CASE , buff=0.2)
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02)
_A : Optional[int] = AnimationGroup(
FadeOut(_SCREAMING_SNAKE_CASE , run_time=0.5) , MoveToTarget(_SCREAMING_SNAKE_CASE , run_time=0.5) , FadeIn(_SCREAMING_SNAKE_CASE , run_time=0.5) , lag_ratio=0.2)
self.play(_SCREAMING_SNAKE_CASE)
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i])
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0])
if i >= 1:
_A : List[Any] = 0.7
self.play(
Circumscribe(model_arr[i] , **_SCREAMING_SNAKE_CASE) , Circumscribe(cpu_left_col_base[i] , **_SCREAMING_SNAKE_CASE) , Circumscribe(cpu_left_col_base[i + 1] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , Circumscribe(gpu_rect[0] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , Circumscribe(model_arr[i + 1] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i]) , MoveToTarget(model_cpu_arr[i + 1]) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1])
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2)
self.play(
Circumscribe(model_arr[-1] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , Circumscribe(cpu_left_col_base[-1] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , Circumscribe(gpu_rect[0] , color=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) , )
self.play(MoveToTarget(model_cpu_arr[i]))
_A : Dict = a_c
_A : int = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5)
self.play(
FadeOut(_SCREAMING_SNAKE_CASE) , FadeOut(_SCREAMING_SNAKE_CASE , run_time=0.5) , )
_A : List[Any] = MarkupText(F'Inference on a model too large for GPU memory\nis successfully completed.' , font_size=24)
step_a.move_to([2, 2, 0])
self.play(Write(_SCREAMING_SNAKE_CASE , run_time=3) , MoveToTarget(_SCREAMING_SNAKE_CASE))
self.wait()
| 371 |
'''simple docstring'''
from __future__ import annotations
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : list[list[int]]):
_A : Dict = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which must be of type '
'int or float.')
if len(SCREAMING_SNAKE_CASE) != 0:
_A : str = len(rows[0])
if cols == 0:
raise error
for row in rows:
if len(SCREAMING_SNAKE_CASE) != cols:
raise error
for value in row:
if not isinstance(SCREAMING_SNAKE_CASE , (int, float)):
raise error
_A : str = rows
else:
_A : Tuple = []
def A ( self : Optional[Any]):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]
@property
def A ( self : List[str]):
return len(self.rows)
@property
def A ( self : Optional[int]):
return len(self.rows[0])
@property
def A ( self : Optional[int]):
return (self.num_rows, self.num_columns)
@property
def A ( self : Any):
return self.order[0] == self.order[1]
def A ( self : int):
_A : Union[str, Any] = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
for row_num in range(self.num_rows)
]
return Matrix(SCREAMING_SNAKE_CASE)
def A ( self : List[Any]):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0])
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]))
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns))
def A ( self : Tuple):
return bool(self.determinant())
def A ( self : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int):
_A : str = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns)
if other_column != column
]
for other_row in range(self.num_rows)
if other_row != row
]
return Matrix(SCREAMING_SNAKE_CASE).determinant()
def A ( self : List[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int):
if (row + column) % 2 == 0:
return self.get_minor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
return -1 * self.get_minor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
def A ( self : Optional[int]):
return Matrix(
[
[self.get_minor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) for column in range(self.num_columns)]
for row in range(self.num_rows)
])
def A ( self : Optional[int]):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns)
]
for row in range(self.minors().num_rows)
])
def A ( self : Tuple):
_A : str = [
[self.cofactors().rows[column][row] for column in range(self.num_columns)]
for row in range(self.num_rows)
]
return Matrix(SCREAMING_SNAKE_CASE)
def A ( self : Tuple):
_A : str = self.determinant()
if not determinant:
raise TypeError('Only matrices with a non-zero determinant have an inverse')
return self.adjugate() * (1 / determinant)
def __repr__( self : str):
return str(self.rows)
def __str__( self : Optional[int]):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0])) + "]]"
return (
"["
+ "\n ".join(
[
'[' + '. '.join([str(SCREAMING_SNAKE_CASE) for value in row]) + '.]'
for row in self.rows
])
+ "]"
)
def A ( self : List[Any] , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int | None = None):
_A : Tuple = TypeError('Row must be a list containing all ints and/or floats')
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
raise type_error
for value in row:
if not isinstance(SCREAMING_SNAKE_CASE , (int, float)):
raise type_error
if len(SCREAMING_SNAKE_CASE) != self.num_columns:
raise ValueError(
'Row must be equal in length to the other rows in the matrix')
if position is None:
self.rows.append(SCREAMING_SNAKE_CASE)
else:
_A : List[str] = self.rows[0:position] + [row] + self.rows[position:]
def A ( self : List[Any] , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int | None = None):
_A : int = TypeError(
'Column must be a list containing all ints and/or floats')
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
raise type_error
for value in column:
if not isinstance(SCREAMING_SNAKE_CASE , (int, float)):
raise type_error
if len(SCREAMING_SNAKE_CASE) != self.num_rows:
raise ValueError(
'Column must be equal in length to the other columns in the matrix')
if position is None:
_A : str = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
else:
_A : Dict = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows)
]
def __eq__( self : List[str] , SCREAMING_SNAKE_CASE : object):
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
return NotImplemented
return self.rows == other.rows
def __ne__( self : Any , SCREAMING_SNAKE_CASE : object):
return not self == other
def __neg__( self : Optional[Any]):
return self * -1
def __add__( self : Any , SCREAMING_SNAKE_CASE : Matrix):
if self.order != other.order:
raise ValueError('Addition requires matrices of the same order')
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
])
def __sub__( self : List[str] , SCREAMING_SNAKE_CASE : Matrix):
if self.order != other.order:
raise ValueError('Subtraction requires matrices of the same order')
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
])
def __mul__( self : Dict , SCREAMING_SNAKE_CASE : Matrix | int | float):
if isinstance(SCREAMING_SNAKE_CASE , (int, float)):
return Matrix(
[[int(element * other) for element in row] for row in self.rows])
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
if self.num_columns != other.num_rows:
raise ValueError(
'The number of columns in the first matrix must '
'be equal to the number of rows in the second')
return Matrix(
[
[Matrix.dot_product(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) for column in other.columns()]
for row in self.rows
])
else:
raise TypeError(
'A Matrix can only be multiplied by an int, float, or another matrix')
def __pow__( self : Any , SCREAMING_SNAKE_CASE : int):
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
raise TypeError('A Matrix can only be raised to the power of an int')
if not self.is_square:
raise ValueError('Only square matrices can be raised to a power')
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'Only invertable matrices can be raised to a negative power')
_A : Union[str, Any] = self
for _ in range(other - 1):
result *= self
return result
@classmethod
def A ( cls : List[str] , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : list[int]):
return sum(row[i] * column[i] for i in range(len(SCREAMING_SNAKE_CASE)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 227 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class UpperCAmelCase (_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase :Optional[Any] = 42
_UpperCAmelCase :Any = 42
class UpperCAmelCase (nn.Module ):
"""simple docstring"""
_UpperCAmelCase :List[str] = 42
_UpperCAmelCase :Optional[int] = (16, 32, 96, 256)
_UpperCAmelCase :List[Any] = jnp.floataa
def _snake_case ( self ):
lowercase__: Tuple = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase__: str = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase__: Tuple = self.block_out_channels[i]
lowercase__: int = self.block_out_channels[i + 1]
lowercase__: Tuple = nn.Conv(
_UpperCAmelCase , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(_UpperCAmelCase )
lowercase__: Optional[Any] = nn.Conv(
_UpperCAmelCase , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(_UpperCAmelCase )
lowercase__: Tuple = blocks
lowercase__: Dict = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , _UpperCAmelCase ):
lowercase__: Optional[Any] = self.conv_in(_UpperCAmelCase )
lowercase__: Optional[Any] = nn.silu(_UpperCAmelCase )
for block in self.blocks:
lowercase__: Optional[Any] = block(_UpperCAmelCase )
lowercase__: str = nn.silu(_UpperCAmelCase )
lowercase__: Union[str, Any] = self.conv_out(_UpperCAmelCase )
return embedding
@flax_register_to_config
class UpperCAmelCase (nn.Module ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase :int = 32
_UpperCAmelCase :Tuple = 4
_UpperCAmelCase :List[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCAmelCase :Any = False
_UpperCAmelCase :List[Any] = (320, 640, 1280, 1280)
_UpperCAmelCase :Dict = 2
_UpperCAmelCase :Dict = 8
_UpperCAmelCase :Any = None
_UpperCAmelCase :Optional[int] = 1280
_UpperCAmelCase :Union[str, Any] = 0.0
_UpperCAmelCase :int = False
_UpperCAmelCase :List[Any] = jnp.floataa
_UpperCAmelCase :Union[str, Any] = True
_UpperCAmelCase :List[Any] = 0
_UpperCAmelCase :Dict = "rgb"
_UpperCAmelCase :Optional[Any] = (16, 32, 96, 256)
def _snake_case ( self , _UpperCAmelCase ):
# init input tensors
lowercase__: Dict = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase__: Optional[int] = jnp.zeros(_UpperCAmelCase , dtype=jnp.floataa )
lowercase__: Optional[Any] = jnp.ones((1,) , dtype=jnp.intaa )
lowercase__: Any = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase__: int = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase__: Tuple = jnp.zeros(_UpperCAmelCase , dtype=jnp.floataa )
lowercase__, lowercase__: Tuple = jax.random.split(_UpperCAmelCase )
lowercase__: int = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )["params"]
def _snake_case ( self ):
lowercase__: List[Any] = self.block_out_channels
lowercase__: List[Any] = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase__: Tuple = self.num_attention_heads or self.attention_head_dim
# input
lowercase__: int = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase__: Union[str, Any] = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase__: Optional[int] = FlaxTimestepEmbedding(_UpperCAmelCase , dtype=self.dtype )
lowercase__: Optional[int] = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase__: Dict = self.only_cross_attention
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Optional[Any] = (only_cross_attention,) * len(self.down_block_types )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Any = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase__: Any = []
lowercase__: Union[str, Any] = []
lowercase__: int = block_out_channels[0]
lowercase__: Dict = nn.Conv(
_UpperCAmelCase , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(_UpperCAmelCase )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase__: List[Any] = output_channel
lowercase__: str = block_out_channels[i]
lowercase__: List[Any] = i == len(_UpperCAmelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase__: Optional[Any] = FlaxCrossAttnDownBlockaD(
in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase__: Optional[int] = FlaxDownBlockaD(
in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(_UpperCAmelCase )
for _ in range(self.layers_per_block ):
lowercase__: Tuple = nn.Conv(
_UpperCAmelCase , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(_UpperCAmelCase )
if not is_final_block:
lowercase__: Dict = nn.Conv(
_UpperCAmelCase , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(_UpperCAmelCase )
lowercase__: int = down_blocks
lowercase__: Optional[Any] = controlnet_down_blocks
# mid
lowercase__: Tuple = block_out_channels[-1]
lowercase__: Dict = FlaxUNetMidBlockaDCrossAttn(
in_channels=_UpperCAmelCase , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase__: List[str] = nn.Conv(
_UpperCAmelCase , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1.0 , _UpperCAmelCase = True , _UpperCAmelCase = False , ):
lowercase__: int = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase__: List[Any] = jnp.flip(_UpperCAmelCase , axis=1 )
# 1. time
if not isinstance(_UpperCAmelCase , jnp.ndarray ):
lowercase__: Union[str, Any] = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(_UpperCAmelCase , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase__: Any = timesteps.astype(dtype=jnp.floataa )
lowercase__: Optional[int] = jnp.expand_dims(_UpperCAmelCase , 0 )
lowercase__: Optional[Any] = self.time_proj(_UpperCAmelCase )
lowercase__: str = self.time_embedding(_UpperCAmelCase )
# 2. pre-process
lowercase__: Tuple = jnp.transpose(_UpperCAmelCase , (0, 2, 3, 1) )
lowercase__: Tuple = self.conv_in(_UpperCAmelCase )
lowercase__: int = jnp.transpose(_UpperCAmelCase , (0, 2, 3, 1) )
lowercase__: Optional[Any] = self.controlnet_cond_embedding(_UpperCAmelCase )
sample += controlnet_cond
# 3. down
lowercase__: int = (sample,)
for down_block in self.down_blocks:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__, lowercase__: List[str] = down_block(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , deterministic=not train )
else:
lowercase__, lowercase__: int = down_block(_UpperCAmelCase , _UpperCAmelCase , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase__: List[Any] = self.mid_block(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , deterministic=not train )
# 5. contronet blocks
lowercase__: List[str] = ()
for down_block_res_sample, controlnet_block in zip(_UpperCAmelCase , self.controlnet_down_blocks ):
lowercase__: List[Any] = controlnet_block(_UpperCAmelCase )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase__: List[Any] = controlnet_down_block_res_samples
lowercase__: Any = self.controlnet_mid_block(_UpperCAmelCase )
# 6. scaling
lowercase__: str = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=_UpperCAmelCase , mid_block_res_sample=_UpperCAmelCase )
| 177 |
'''simple docstring'''
def _A (lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str ) -> List[Any]:
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
move_disk(lowerCAmelCase__ , lowerCAmelCase__ )
move_tower(height - 1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def _A (lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :int ) -> Optional[Any]:
'''simple docstring'''
print('moving disk from' , lowerCAmelCase__ , 'to' , lowerCAmelCase__ )
def _A () -> str:
'''simple docstring'''
_a = int(input('Height of hanoi: ' ).strip() )
move_tower(lowerCAmelCase__ , 'A' , 'B' , 'C' )
if __name__ == "__main__":
main()
| 168 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__magic_name__: Dict = None
__magic_name__: List[Any] = logging.get_logger(__name__)
__magic_name__: Union[str, Any] = "▁"
__magic_name__: Optional[int] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
__magic_name__: Any = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
"tokenizer_file": {
"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
},
}
__magic_name__: int = {
"google/pegasus-xsum": 512,
}
class snake_case__ ( _lowerCAmelCase ):
lowercase__ : List[Any] = VOCAB_FILES_NAMES
lowercase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : str = PegasusTokenizer
lowercase__ : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__="<pad>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<mask_2>" , lowerCAmelCase__="<mask_1>" , lowerCAmelCase__=None , lowerCAmelCase__=1_03 , **lowerCAmelCase__ , ) -> Union[str, Any]:
__magic_name__ : Optional[Any] = offset
if additional_special_tokens is not None:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError(
F'additional_special_tokens should be of type {type(lowerCAmelCase__ )}, but is'
F' {type(lowerCAmelCase__ )}' )
__magic_name__ : Optional[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F'<unk_{i}>' for i in range(len(lowerCAmelCase__ ) , self.offset - 1 )
]
if len(set(lowerCAmelCase__ ) ) != len(lowerCAmelCase__ ):
raise ValueError(
"""Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
__magic_name__ : Union[str, Any] = additional_special_tokens_extended
else:
__magic_name__ : Any = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F'<unk_{i}>' for i in range(2 , self.offset )]
super().__init__(
lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , mask_token_sent=lowerCAmelCase__ , offset=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
__magic_name__ : Any = vocab_file
__magic_name__ : Tuple = False if not self.vocab_file else True
def __magic_name__ ( self , lowerCAmelCase__ ) -> List[str]:
__magic_name__ : Union[str, Any] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"""There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
F' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}' )
return [1 if x in all_special_ids else 0 for x in seq]
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False ) -> List[int]:
if already_has_special_tokens:
return self._special_token_mask(lowerCAmelCase__ )
elif token_ids_a is None:
return self._special_token_mask(lowerCAmelCase__ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__magic_name__ : Optional[int] = 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__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,)
| 138 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class snake_case__ ( _lowerCAmelCase ):
lowercase__ : torch.FloatTensor
lowercase__ : Optional[torch.FloatTensor] = None
def UpperCamelCase ( _A, _A=0.999, _A="cosine", ):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(_A ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_A ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )
__magic_name__ : Optional[Any] = []
for i in range(_A ):
__magic_name__ : Dict = i / num_diffusion_timesteps
__magic_name__ : Any = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_A ) / alpha_bar_fn(_A ), _A ) )
return torch.tensor(_A, dtype=torch.floataa )
class snake_case__ ( _lowerCAmelCase , _lowerCAmelCase ):
@register_to_config
def __init__( self , lowerCAmelCase__ = 10_00 , lowerCAmelCase__ = "fixed_small_log" , lowerCAmelCase__ = True , lowerCAmelCase__ = 1.0 , lowerCAmelCase__ = "epsilon" , lowerCAmelCase__ = "squaredcos_cap_v2" , ) -> Union[str, Any]:
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )
__magic_name__ : Tuple = betas_for_alpha_bar(lowerCAmelCase__ )
__magic_name__ : Union[str, Any] = 1.0 - self.betas
__magic_name__ : str = torch.cumprod(self.alphas , dim=0 )
__magic_name__ : Any = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
__magic_name__ : Tuple = 1.0
# setable values
__magic_name__ : List[Any] = None
__magic_name__ : int = torch.from_numpy(np.arange(0 , lowerCAmelCase__ )[::-1].copy() )
__magic_name__ : List[Any] = variance_type
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> torch.FloatTensor:
return sample
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> str:
__magic_name__ : List[Any] = num_inference_steps
__magic_name__ : Union[str, Any] = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
__magic_name__ : List[Any] = (np.arange(0 , lowerCAmelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
__magic_name__ : Dict = torch.from_numpy(lowerCAmelCase__ ).to(lowerCAmelCase__ )
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None ) -> Tuple:
if prev_timestep is None:
__magic_name__ : int = t - 1
__magic_name__ : Optional[Any] = self.alphas_cumprod[t]
__magic_name__ : Any = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__magic_name__ : Tuple = 1 - alpha_prod_t
__magic_name__ : int = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__magic_name__ : List[str] = self.betas[t]
else:
__magic_name__ : List[Any] = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
__magic_name__ : Dict = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
__magic_name__ : str = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
__magic_name__ : str = torch.log(torch.clamp(lowerCAmelCase__ , min=1e-2_0 ) )
__magic_name__ : Optional[Any] = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
__magic_name__ : List[str] = variance.log()
__magic_name__ : Optional[int] = beta.log()
__magic_name__ : Any = (predicted_variance + 1) / 2
__magic_name__ : Any = frac * max_log + (1 - frac) * min_log
return variance
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__=None , lowerCAmelCase__ = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
__magic_name__ : List[Any] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
__magic_name__ ,__magic_name__ : List[Any] = torch.split(lowerCAmelCase__ , sample.shape[1] , dim=1 )
else:
__magic_name__ : List[str] = None
# 1. compute alphas, betas
if prev_timestep is None:
__magic_name__ : Union[str, Any] = t - 1
__magic_name__ : List[str] = self.alphas_cumprod[t]
__magic_name__ : Dict = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__magic_name__ : Any = 1 - alpha_prod_t
__magic_name__ : Dict = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__magic_name__ : Union[str, Any] = self.betas[t]
__magic_name__ : int = self.alphas[t]
else:
__magic_name__ : Tuple = 1 - alpha_prod_t / alpha_prod_t_prev
__magic_name__ : Tuple = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
__magic_name__ : Optional[int] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
__magic_name__ : Tuple = model_output
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
""" for the UnCLIPScheduler.""" )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
__magic_name__ : Tuple = torch.clamp(
lowerCAmelCase__ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__magic_name__ : List[Any] = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
__magic_name__ : Dict = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__magic_name__ : str = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
__magic_name__ : Tuple = 0
if t > 0:
__magic_name__ : Any = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=lowerCAmelCase__ , device=model_output.device )
__magic_name__ : Tuple = self._get_variance(
lowerCAmelCase__ , predicted_variance=lowerCAmelCase__ , prev_timestep=lowerCAmelCase__ , )
if self.variance_type == "fixed_small_log":
__magic_name__ : Tuple = variance
elif self.variance_type == "learned_range":
__magic_name__ : int = (0.5 * variance).exp()
else:
raise ValueError(
F'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
""" for the UnCLIPScheduler.""" )
__magic_name__ : Tuple = variance * variance_noise
__magic_name__ : List[str] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase__ , pred_original_sample=lowerCAmelCase__ )
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
__magic_name__ : List[str] = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
__magic_name__ : Any = timesteps.to(original_samples.device )
__magic_name__ : int = alphas_cumprod[timesteps] ** 0.5
__magic_name__ : Union[str, Any] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
__magic_name__ : int = sqrt_alpha_prod.unsqueeze(-1 )
__magic_name__ : Any = (1 - alphas_cumprod[timesteps]) ** 0.5
__magic_name__ : str = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
__magic_name__ : Any = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
__magic_name__ : Any = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 138 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
SCREAMING_SNAKE_CASE_: Dict ='0.12' # assumed parallelism: 8
if is_torch_available():
import torch
def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : Optional[Any] , snake_case_ : Union[str, Any]=None ) -> List[str]:
'''simple docstring'''
if rng is None:
UpperCAmelCase_ = random.Random()
UpperCAmelCase_ = 1
for dim in shape:
total_dims *= dim
UpperCAmelCase_ = []
for _ in range(lowerCAmelCase__ ):
values.append(rng.randint(0 , vocab_size - 1 ) )
UpperCAmelCase_ = np.array(lowerCAmelCase__ , dtype=jnp.intaa ).reshape(lowerCAmelCase__ )
return output
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : Tuple=None ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = ids_tensor(lowerCAmelCase__ , vocab_size=2 , rng=lowerCAmelCase__ )
# make sure that at least one token is attended to for each batch
UpperCAmelCase_ = 1
return attn_mask
@require_flax
class __A :
a__ : Any = None
a__ : Union[str, Any] = ()
def _lowercase (self : str ):
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
UpperCAmelCase_ = 2
UpperCAmelCase_ = inputs["""input_ids"""].shape[-1] // 2
UpperCAmelCase_ = inputs["""input_ids"""][:max_batch_size, :sequence_length]
UpperCAmelCase_ = jnp.ones_like(__UpperCAmelCase )
UpperCAmelCase_ = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
UpperCAmelCase_ = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
UpperCAmelCase_ = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def _lowercase (self : str ):
UpperCAmelCase_ = self._get_input_ids_and_config()
UpperCAmelCase_ = False
UpperCAmelCase_ = max_length
UpperCAmelCase_ = 0
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase_ = getattr(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase_ = pt_model_class(__UpperCAmelCase ).eval()
UpperCAmelCase_ = load_flax_weights_in_pytorch_model(__UpperCAmelCase , flax_model.params )
UpperCAmelCase_ = flax_model.generate(__UpperCAmelCase ).sequences
UpperCAmelCase_ = pt_model.generate(torch.tensor(__UpperCAmelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
UpperCAmelCase_ = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def _lowercase (self : Optional[Any] ):
UpperCAmelCase_ = self._get_input_ids_and_config()
UpperCAmelCase_ = False
UpperCAmelCase_ = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowercase (self : int ):
UpperCAmelCase_ = self._get_input_ids_and_config()
UpperCAmelCase_ = True
UpperCAmelCase_ = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowercase (self : Optional[int] ):
UpperCAmelCase_ = self._get_input_ids_and_config()
UpperCAmelCase_ = False
UpperCAmelCase_ = max_length
UpperCAmelCase_ = 2
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowercase (self : Any ):
UpperCAmelCase_ = self._get_input_ids_and_config()
UpperCAmelCase_ = False
UpperCAmelCase_ = max_length
UpperCAmelCase_ = 2
UpperCAmelCase_ = 2
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def _lowercase (self : Union[str, Any] ):
UpperCAmelCase_ = self._get_input_ids_and_config()
UpperCAmelCase_ = True
UpperCAmelCase_ = max_length
UpperCAmelCase_ = 0.8
UpperCAmelCase_ = 10
UpperCAmelCase_ = 0.3
UpperCAmelCase_ = 1
UpperCAmelCase_ = 8
UpperCAmelCase_ = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowercase (self : List[str] ):
UpperCAmelCase_ = self._get_input_ids_and_config()
UpperCAmelCase_ = max_length
UpperCAmelCase_ = 1
UpperCAmelCase_ = 8
UpperCAmelCase_ = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowercase (self : Tuple ):
UpperCAmelCase_ = self._get_input_ids_and_config()
UpperCAmelCase_ = max_length
UpperCAmelCase_ = 2
UpperCAmelCase_ = 1
UpperCAmelCase_ = 8
UpperCAmelCase_ = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowercase (self : Union[str, Any] ):
UpperCAmelCase_ = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase_ = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase_ = False
UpperCAmelCase_ = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase , attention_mask=__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase , attention_mask=__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowercase (self : Union[str, Any] ):
UpperCAmelCase_ = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase_ = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase_ = True
UpperCAmelCase_ = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase , attention_mask=__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase , attention_mask=__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowercase (self : Tuple ):
UpperCAmelCase_ = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase_ = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase_ = 2
UpperCAmelCase_ = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase_ = model_class(__UpperCAmelCase )
UpperCAmelCase_ = model.generate(__UpperCAmelCase , attention_mask=__UpperCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCAmelCase )
UpperCAmelCase_ = jit(model.generate )
UpperCAmelCase_ = jit_generate(__UpperCAmelCase , attention_mask=__UpperCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class __A ( unittest.TestCase ):
def _lowercase (self : List[str] ):
UpperCAmelCase_ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
UpperCAmelCase_ = FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
UpperCAmelCase_ = """Hello world"""
UpperCAmelCase_ = tokenizer(__UpperCAmelCase , return_tensors="np" ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(__UpperCAmelCase , "do_samples" ):
model.generate(__UpperCAmelCase , do_samples=__UpperCAmelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(__UpperCAmelCase , "foo" ):
UpperCAmelCase_ = {"""foo""": """bar"""}
model.generate(__UpperCAmelCase , **__UpperCAmelCase )
| 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int = 50 ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F'{solution() = }')
| 254 | 0 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ (__A : List[Any] , __A : Any , __A : Union[str, Any] ) -> Tuple:
# Initialise PyTorch model
__lowerCAmelCase : Tuple = BertConfig.from_json_file(__A )
print(f'''Building PyTorch model from configuration: {config}''' )
__lowerCAmelCase : List[Any] = BertForPreTraining(__A )
# Load weights from tf checkpoint
load_tf_weights_in_bert(__A , __A , __A )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , __A )
if __name__ == "__main__":
__UpperCAmelCase = 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."""
)
__UpperCAmelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 363 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def snake_case_ (__A : Optional[int] , __A : Any ) -> Any:
__lowerCAmelCase : Union[str, Any] = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f'''encoder.deit.blocks.{i}.norm1.weight''', f'''encoder.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.norm1.bias''', f'''encoder.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.attn.proj.weight''', f'''encoder.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.attn.proj.bias''', f'''encoder.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.norm2.weight''', f'''encoder.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.norm2.bias''', f'''encoder.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc1.weight''', f'''encoder.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc1.bias''', f'''encoder.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc2.weight''', f'''encoder.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.mlp.fc2.bias''', f'''encoder.encoder.layer.{i}.output.dense.bias''') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
] )
return rename_keys
def snake_case_ (__A : List[str] , __A : str ) -> Optional[Any]:
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
__lowerCAmelCase : Optional[Any] = state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight''' )
__lowerCAmelCase : Tuple = in_proj_weight[
: encoder_config.hidden_size, :
]
__lowerCAmelCase : str = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
__lowerCAmelCase : str = in_proj_weight[
-encoder_config.hidden_size :, :
]
def snake_case_ (__A : Union[str, Any] , __A : str , __A : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase : Any = dct.pop(__A )
__lowerCAmelCase : str = val
def snake_case_ (__A : int ) -> Tuple:
if "handwritten" in checkpoint_url:
__lowerCAmelCase : Tuple = """https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg""" # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
__lowerCAmelCase : Optional[Any] = """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
__lowerCAmelCase : Dict = Image.open(requests.get(__A , stream=__A ).raw ).convert("""RGB""" )
return im
@torch.no_grad()
def snake_case_ (__A : Any , __A : Union[str, Any] ) -> Optional[int]:
__lowerCAmelCase : List[Any] = ViTConfig(image_size=3_8_4 , qkv_bias=__A )
__lowerCAmelCase : List[Any] = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
__lowerCAmelCase : Union[str, Any] = 7_6_8
elif "large" in checkpoint_url:
# use ViT-large encoder
__lowerCAmelCase : Any = 1_0_2_4
__lowerCAmelCase : Any = 4_0_9_6
__lowerCAmelCase : Optional[int] = 2_4
__lowerCAmelCase : str = 1_6
__lowerCAmelCase : List[Any] = 1_0_2_4
else:
raise ValueError("""Should either find 'base' or 'large' in checkpoint URL""" )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
__lowerCAmelCase : Tuple = False
__lowerCAmelCase : Union[str, Any] = """relu"""
__lowerCAmelCase : List[Any] = 1_0_2_4
__lowerCAmelCase : Any = True
__lowerCAmelCase : List[Any] = False
__lowerCAmelCase : Dict = False
# load HuggingFace model
__lowerCAmelCase : Dict = ViTModel(__A , add_pooling_layer=__A )
__lowerCAmelCase : Union[str, Any] = TrOCRForCausalLM(__A )
__lowerCAmelCase : Any = VisionEncoderDecoderModel(encoder=__A , decoder=__A )
model.eval()
# load state_dict of original model, rename some keys
__lowerCAmelCase : Union[str, Any] = torch.hub.load_state_dict_from_url(__A , map_location="""cpu""" , check_hash=__A )["""model"""]
__lowerCAmelCase : Any = create_rename_keys(__A , __A )
for src, dest in rename_keys:
rename_key(__A , __A , __A )
read_in_q_k_v(__A , __A )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
__lowerCAmelCase : Tuple = state_dict.pop(__A )
if key.startswith("""decoder""" ) and "output_projection" not in key:
__lowerCAmelCase : str = val
else:
__lowerCAmelCase : Tuple = val
# load state dict
model.load_state_dict(__A )
# Check outputs on an image
__lowerCAmelCase : List[Any] = ViTImageProcessor(size=encoder_config.image_size )
__lowerCAmelCase : List[str] = RobertaTokenizer.from_pretrained("""roberta-large""" )
__lowerCAmelCase : List[Any] = TrOCRProcessor(__A , __A )
__lowerCAmelCase : List[str] = processor(images=prepare_img(__A ) , return_tensors="""pt""" ).pixel_values
# verify logits
__lowerCAmelCase : List[str] = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
__lowerCAmelCase : List[str] = model(pixel_values=__A , decoder_input_ids=__A )
__lowerCAmelCase : Optional[Any] = outputs.logits
__lowerCAmelCase : Union[str, Any] = torch.Size([1, 1, 5_0_2_6_5] )
if "trocr-base-handwritten" in checkpoint_url:
__lowerCAmelCase : Dict = torch.tensor(
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311] )
elif "trocr-large-handwritten" in checkpoint_url:
__lowerCAmelCase : List[Any] = torch.tensor(
[-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170] )
elif "trocr-base-printed" in checkpoint_url:
__lowerCAmelCase : Tuple = torch.tensor(
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210] )
elif "trocr-large-printed" in checkpoint_url:
__lowerCAmelCase : List[Any] = torch.tensor(
[-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :1_0] , __A , atol=1e-3 ), "First elements of logits not as expected"
Path(__A ).mkdir(exist_ok=__A )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__A )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
__UpperCAmelCase = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 139 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__snake_case : Tuple ={'processing_layoutxlm': ['LayoutXLMProcessor']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Dict =['LayoutXLMTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : int =['LayoutXLMTokenizerFast']
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
__snake_case : List[str] =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 129 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ ="""Speech2TextFeatureExtractor"""
snake_case_ ="""Speech2TextTokenizer"""
def __init__(self ,__lowerCamelCase ,__lowerCamelCase ) -> str:
"""simple docstring"""
super().__init__(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : int = self.feature_extractor
lowerCAmelCase__ : List[str] = False
def __call__(self ,*__lowerCamelCase ,**__lowerCamelCase ) -> Dict:
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*__lowerCamelCase ,**__lowerCamelCase )
if "raw_speech" in kwargs:
warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
lowerCAmelCase__ : Optional[Any] = kwargs.pop('''raw_speech''' )
else:
lowerCAmelCase__ : str = kwargs.pop('''audio''' ,__lowerCamelCase )
lowerCAmelCase__ : List[str] = kwargs.pop('''sampling_rate''' ,__lowerCamelCase )
lowerCAmelCase__ : List[str] = kwargs.pop('''text''' ,__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
lowerCAmelCase__ : Union[str, Any] = args[0]
lowerCAmelCase__ : str = 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 audio is not None:
lowerCAmelCase__ : str = self.feature_extractor(__lowerCamelCase ,*__lowerCamelCase ,sampling_rate=__lowerCamelCase ,**__lowerCamelCase )
if text is not None:
lowerCAmelCase__ : Any = self.tokenizer(__lowerCamelCase ,**__lowerCamelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCAmelCase__ : str = encodings['''input_ids''']
return inputs
def lowerCAmelCase__ (self ,*__lowerCamelCase ,**__lowerCamelCase ) -> List[str]:
"""simple docstring"""
return self.tokenizer.batch_decode(*__lowerCamelCase ,**__lowerCamelCase )
def lowerCAmelCase__ (self ,*__lowerCamelCase ,**__lowerCamelCase ) -> Tuple:
"""simple docstring"""
return self.tokenizer.decode(*__lowerCamelCase ,**__lowerCamelCase )
@contextmanager
def lowerCAmelCase__ (self ) -> Any:
"""simple docstring"""
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your audio inputs, or in a separate call.''' )
lowerCAmelCase__ : int = True
lowerCAmelCase__ : Union[str, Any] = self.tokenizer
yield
lowerCAmelCase__ : List[str] = self.feature_extractor
lowerCAmelCase__ : Any = False
| 129 | 1 |
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
UpperCAmelCase : str = [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase : Optional[int] = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 369 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase : int = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = os.path.dirname(os.path.realpath(a__ ) )
__SCREAMING_SNAKE_CASE = os.path.join(a__ , """words.txt""" )
__SCREAMING_SNAKE_CASE = """"""
with open(a__ ) as f:
__SCREAMING_SNAKE_CASE = f.readline()
__SCREAMING_SNAKE_CASE = [word.strip("""\"""" ) for word in words.strip("""\r\n""" ).split(""",""" )]
__SCREAMING_SNAKE_CASE = [
word
for word in [sum(ord(a__ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(a__ )
if __name__ == "__main__":
print(solution())
| 331 | 0 |
def a ( snake_case__: int ):
'''simple docstring'''
lowercase_ = [0] * len(snake_case__ )
lowercase_ = []
lowercase_ = [1] * len(snake_case__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(snake_case__ ) ):
if indegree[i] == 0:
queue.append(snake_case__ )
while queue:
lowercase_ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowercase_ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(snake_case__ )
print(max(snake_case__ ) )
# Adjacency list of Graph
__a = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 30 |
'''simple docstring'''
import argparse
import os
import re
_snake_case = 'src/transformers'
# Pattern that looks at the indentation in a line.
_snake_case = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
_snake_case = re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_snake_case = re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
_snake_case = re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_snake_case = re.compile(r'\[([^\]]+)\]')
def _A ( snake_case ) -> str:
_lowercase : Union[str, Any] = _re_indent.search(snake_case )
return "" if search is None else search.groups()[0]
def _A ( snake_case , snake_case="" , snake_case=None , snake_case=None ) -> Optional[int]:
_lowercase : List[str] = 0
_lowercase : str = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(snake_case ):
index += 1
_lowercase : Optional[int] = ["\n".join(lines[:index] )]
else:
_lowercase : Dict = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
_lowercase : Any = [lines[index]]
index += 1
while index < len(snake_case ) and (end_prompt is None or not lines[index].startswith(snake_case )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(snake_case ) )
if index < len(snake_case ) - 1:
_lowercase : int = [lines[index + 1]]
index += 1
else:
_lowercase : Optional[int] = []
else:
blocks.append("\n".join(snake_case ) )
_lowercase : Optional[Any] = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case ) > 0:
blocks.append("\n".join(snake_case ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def _A ( snake_case ) -> Optional[int]:
def _inner(snake_case ):
return key(snake_case ).lower().replace("_" , "" )
return _inner
def _A ( snake_case , snake_case=None ) -> List[str]:
# If no key is provided, we use a noop.
def noop(snake_case ):
return x
if key is None:
_lowercase : Optional[int] = noop
# Constants are all uppercase, they go first.
_lowercase : Dict = [obj for obj in objects if key(snake_case ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
_lowercase : int = [obj for obj in objects if key(snake_case )[0].isupper() and not key(snake_case ).isupper()]
# Functions begin with a lowercase, they go last.
_lowercase : Dict = [obj for obj in objects if not key(snake_case )[0].isupper()]
_lowercase : Union[str, Any] = ignore_underscore(snake_case )
return sorted(snake_case , key=snake_case ) + sorted(snake_case , key=snake_case ) + sorted(snake_case , key=snake_case )
def _A ( snake_case ) -> List[Any]:
# This inner function sort imports between [ ].
def _replace(snake_case ):
_lowercase : Optional[Any] = match.groups()[0]
if "," not in imports:
return F'''[{imports}]'''
_lowercase : str = [part.strip().replace("\"" , "" ) for part in imports.split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_lowercase : Optional[int] = keys[:-1]
return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(snake_case )] ) + "]"
_lowercase : Tuple = import_statement.split("\n" )
if len(snake_case ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
_lowercase : Union[str, Any] = 2 if lines[1].strip() == "[" else 1
_lowercase : Optional[int] = [(i, _re_strip_line.search(snake_case ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
_lowercase : Any = sort_objects(snake_case , key=lambda snake_case : x[1] )
_lowercase : Tuple = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
_lowercase : Dict = _re_bracket_content.sub(_replace , lines[1] )
else:
_lowercase : Optional[Any] = [part.strip().replace("\"" , "" ) for part in lines[1].split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_lowercase : Optional[int] = keys[:-1]
_lowercase : Optional[Any] = get_indent(lines[1] ) + ", ".join([F'''"{k}"''' for k in sort_objects(snake_case )] )
return "\n".join(snake_case )
else:
# Finally we have to deal with imports fitting on one line
_lowercase : Optional[Any] = _re_bracket_content.sub(_replace , snake_case )
return import_statement
def _A ( snake_case , snake_case=True ) -> Dict:
with open(snake_case , encoding="utf-8" ) as f:
_lowercase : Dict = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
_lowercase : Optional[Any] = split_code_in_indented_blocks(
snake_case , start_prompt="_import_structure = {" , end_prompt="if TYPE_CHECKING:" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(snake_case ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
_lowercase : Dict = main_blocks[block_idx]
_lowercase : Union[str, Any] = block.split("\n" )
# Get to the start of the imports.
_lowercase : int = 0
while line_idx < len(snake_case ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
_lowercase : Optional[Any] = len(snake_case )
else:
line_idx += 1
if line_idx >= len(snake_case ):
continue
# Ignore beginning and last line: they don't contain anything.
_lowercase : Any = "\n".join(block_lines[line_idx:-1] )
_lowercase : int = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
_lowercase : Optional[int] = split_code_in_indented_blocks(snake_case , indent_level=snake_case )
# We have two categories of import key: list or _import_structure[key].append/extend
_lowercase : Union[str, Any] = _re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
_lowercase : str = [(pattern.search(snake_case ).groups()[0] if pattern.search(snake_case ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
_lowercase : List[str] = [(i, key) for i, key in enumerate(snake_case ) if key is not None]
_lowercase : Tuple = [x[0] for x in sorted(snake_case , key=lambda snake_case : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
_lowercase : Any = 0
_lowercase : str = []
for i in range(len(snake_case ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
_lowercase : Tuple = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case )
count += 1
# And we put our main block back together with its first and last line.
_lowercase : int = "\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case ):
if check_only:
return True
else:
print(F'''Overwriting {file}.''' )
with open(snake_case , "w" , encoding="utf-8" ) as f:
f.write("\n".join(snake_case ) )
def _A ( snake_case=True ) -> str:
_lowercase : List[Any] = []
for root, _, files in os.walk(snake_case ):
if "__init__.py" in files:
_lowercase : Tuple = sort_imports(os.path.join(snake_case , "__init__.py" ) , check_only=snake_case )
if result:
_lowercase : Any = [os.path.join(snake_case , "__init__.py" )]
if len(snake_case ) > 0:
raise ValueError(F'''Would overwrite {len(snake_case )} files, run `make style`.''' )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
_snake_case = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 250 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = ["pixel_values"]
def __init__( self , _a = True , _a = None , _a = PILImageResampling.BICUBIC , _a = True , _a = None , _a = True , _a = 1 / 255 , _a = True , _a = None , _a = None , _a = True , **_a , ):
"""simple docstring"""
super().__init__(**_a )
lowerCamelCase = size if size is not None else {"""shortest_edge""": 224}
lowerCamelCase = get_size_dict(_a , default_to_square=_a )
lowerCamelCase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowerCamelCase = get_size_dict(_a , default_to_square=_a , param_name="""crop_size""" )
lowerCamelCase = do_resize
lowerCamelCase = size
lowerCamelCase = resample
lowerCamelCase = do_center_crop
lowerCamelCase = crop_size
lowerCamelCase = do_rescale
lowerCamelCase = rescale_factor
lowerCamelCase = do_normalize
lowerCamelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCamelCase = image_std if image_std is not None else OPENAI_CLIP_STD
lowerCamelCase = do_convert_rgb
def _lowerCAmelCase ( self , _a , _a , _a = PILImageResampling.BICUBIC , _a = None , **_a , ):
"""simple docstring"""
lowerCamelCase = get_size_dict(_a , default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
lowerCamelCase = get_resize_output_image_size(_a , size=size["""shortest_edge"""] , default_to_square=_a )
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def _lowerCAmelCase ( self , _a , _a , _a = None , **_a , ):
"""simple docstring"""
lowerCamelCase = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_a , size=(size["""height"""], size["""width"""]) , data_format=_a , **_a )
def _lowerCAmelCase ( self , _a , _a , _a = None , **_a , ):
"""simple docstring"""
return rescale(_a , scale=_a , data_format=_a , **_a )
def _lowerCAmelCase ( self , _a , _a , _a , _a = None , **_a , ):
"""simple docstring"""
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def _lowerCAmelCase ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ):
"""simple docstring"""
lowerCamelCase = do_resize if do_resize is not None else self.do_resize
lowerCamelCase = size if size is not None else self.size
lowerCamelCase = get_size_dict(_a , param_name="""size""" , default_to_square=_a )
lowerCamelCase = resample if resample is not None else self.resample
lowerCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase = crop_size if crop_size is not None else self.crop_size
lowerCamelCase = get_size_dict(_a , param_name="""crop_size""" , default_to_square=_a )
lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase = image_mean if image_mean is not None else self.image_mean
lowerCamelCase = image_std if image_std is not None else self.image_std
lowerCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase = make_list_of_images(_a )
if not valid_images(_a ):
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.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase = [convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase = [to_numpy_array(_a ) for image in images]
if do_resize:
lowerCamelCase = [self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_center_crop:
lowerCamelCase = [self.center_crop(image=_a , size=_a ) for image in images]
if do_rescale:
lowerCamelCase = [self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
lowerCamelCase = [self.normalize(image=_a , mean=_a , std=_a ) for image in images]
lowerCamelCase = [to_channel_dimension_format(_a , _a ) for image in images]
lowerCamelCase = {"""pixel_values""": images}
return BatchFeature(data=_a , tensor_type=_a )
| 365 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase : List[Any] = logging.get_logger(__name__)
lowerCAmelCase : int = {
"""salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "blip_2_vision_model"
def __init__( self , _a=1_408 , _a=6_144 , _a=39 , _a=16 , _a=224 , _a=14 , _a="gelu" , _a=0.00_001 , _a=0.0 , _a=1e-1_0 , _a=True , **_a , ):
"""simple docstring"""
super().__init__(**_a )
lowerCamelCase = hidden_size
lowerCamelCase = intermediate_size
lowerCamelCase = num_hidden_layers
lowerCamelCase = num_attention_heads
lowerCamelCase = patch_size
lowerCamelCase = image_size
lowerCamelCase = initializer_range
lowerCamelCase = attention_dropout
lowerCamelCase = layer_norm_eps
lowerCamelCase = hidden_act
lowerCamelCase = qkv_bias
@classmethod
def _lowerCAmelCase ( cls , _a , **_a ):
"""simple docstring"""
cls._set_token_in_kwargs(_a )
lowerCamelCase , lowerCamelCase = cls.get_config_dict(_a , **_a )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get("""model_type""" ) == "blip-2":
lowerCamelCase = 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 __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "blip_2_qformer"
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=0.02 , _a=1e-1_2 , _a=0 , _a="absolute" , _a=2 , _a=1_408 , **_a , ):
"""simple docstring"""
super().__init__(pad_token_id=_a , **_a )
lowerCamelCase = vocab_size
lowerCamelCase = hidden_size
lowerCamelCase = num_hidden_layers
lowerCamelCase = num_attention_heads
lowerCamelCase = hidden_act
lowerCamelCase = intermediate_size
lowerCamelCase = hidden_dropout_prob
lowerCamelCase = attention_probs_dropout_prob
lowerCamelCase = max_position_embeddings
lowerCamelCase = initializer_range
lowerCamelCase = layer_norm_eps
lowerCamelCase = position_embedding_type
lowerCamelCase = cross_attention_frequency
lowerCamelCase = encoder_hidden_size
@classmethod
def _lowerCAmelCase ( cls , _a , **_a ):
"""simple docstring"""
cls._set_token_in_kwargs(_a )
lowerCamelCase , lowerCamelCase = cls.get_config_dict(_a , **_a )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get("""model_type""" ) == "blip-2":
lowerCamelCase = config_dict["""qformer_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 __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "blip-2"
__UpperCamelCase = True
def __init__( self , _a=None , _a=None , _a=None , _a=32 , **_a ):
"""simple docstring"""
super().__init__(**_a )
if vision_config is None:
lowerCamelCase = {}
logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values.""" )
if qformer_config is None:
lowerCamelCase = {}
logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values.""" )
if text_config is None:
lowerCamelCase = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
lowerCamelCase = BlipaVisionConfig(**_a )
lowerCamelCase = BlipaQFormerConfig(**_a )
lowerCamelCase = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
lowerCamelCase = CONFIG_MAPPING[text_model_type](**_a )
lowerCamelCase = self.text_config.tie_word_embeddings
lowerCamelCase = self.text_config.is_encoder_decoder
lowerCamelCase = num_query_tokens
lowerCamelCase = self.vision_config.hidden_size
lowerCamelCase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
lowerCamelCase = 1.0
lowerCamelCase = 0.02
@classmethod
def _lowerCAmelCase ( cls , _a , _a , _a , **_a , ):
"""simple docstring"""
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_a , )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = copy.deepcopy(self.__dict__ )
lowerCamelCase = self.vision_config.to_dict()
lowerCamelCase = self.qformer_config.to_dict()
lowerCamelCase = self.text_config.to_dict()
lowerCamelCase = self.__class__.model_type
return output
| 168 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
UpperCAmelCase_ : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase_ : Optional[int] = """
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class lowercase__ ( __UpperCAmelCase ):
'''simple docstring'''
A_ : Union[PIL.Image.Image, np.ndarray]
class lowercase__ ( __UpperCAmelCase ):
'''simple docstring'''
def __init__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
super().__init__()
self.register_modules(
prior=__snake_case , image_encoder=__snake_case , image_processor=__snake_case , scheduler=__snake_case , renderer=__snake_case , )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ):
if latents is None:
_SCREAMING_SNAKE_CASE : List[Any] = randn_tensor(__snake_case , generator=__snake_case , device=__snake_case , dtype=__snake_case )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
_SCREAMING_SNAKE_CASE : List[str] = latents.to(__snake_case )
_SCREAMING_SNAKE_CASE : Dict = latents * scheduler.init_noise_sigma
return latents
def UpperCAmelCase_ ( self , __snake_case=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
_SCREAMING_SNAKE_CASE : Any = torch.device(f"""cuda:{gpu_id}""" )
_SCREAMING_SNAKE_CASE : Optional[Any] = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__snake_case , __snake_case )
@property
def UpperCAmelCase_ ( self ):
if self.device != torch.device("""meta""" ) or not hasattr(self.image_encoder , """_hf_hook""" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(__snake_case , """_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
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , ):
if isinstance(__snake_case , __snake_case ) and isinstance(image[0] , torch.Tensor ):
_SCREAMING_SNAKE_CASE : Dict = torch.cat(__snake_case , axis=0 ) if image[0].ndim == 4 else torch.stack(__snake_case , axis=0 )
if not isinstance(__snake_case , torch.Tensor ):
_SCREAMING_SNAKE_CASE : List[Any] = self.image_processor(__snake_case , return_tensors="""pt""" ).pixel_values[0].unsqueeze(0 )
_SCREAMING_SNAKE_CASE : Optional[Any] = image.to(dtype=self.image_encoder.dtype , device=__snake_case )
_SCREAMING_SNAKE_CASE : int = self.image_encoder(__snake_case )['last_hidden_state']
_SCREAMING_SNAKE_CASE : Optional[Any] = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
_SCREAMING_SNAKE_CASE : Optional[int] = image_embeds.repeat_interleave(__snake_case , dim=0 )
if do_classifier_free_guidance:
_SCREAMING_SNAKE_CASE : Tuple = torch.zeros_like(__snake_case )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_SCREAMING_SNAKE_CASE : int = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(__snake_case )
def __call__( self , __snake_case , __snake_case = 1 , __snake_case = 25 , __snake_case = None , __snake_case = None , __snake_case = 4.0 , __snake_case = 64 , __snake_case = "pil" , __snake_case = True , ):
if isinstance(__snake_case , PIL.Image.Image ):
_SCREAMING_SNAKE_CASE : List[str] = 1
elif isinstance(__snake_case , torch.Tensor ):
_SCREAMING_SNAKE_CASE : List[str] = image.shape[0]
elif isinstance(__snake_case , __snake_case ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
_SCREAMING_SNAKE_CASE : List[str] = len(__snake_case )
else:
raise ValueError(
f"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__snake_case )}""" )
_SCREAMING_SNAKE_CASE : Tuple = self._execution_device
_SCREAMING_SNAKE_CASE : List[Any] = batch_size * num_images_per_prompt
_SCREAMING_SNAKE_CASE : Optional[Any] = guidance_scale > 1.0
_SCREAMING_SNAKE_CASE : Optional[int] = self._encode_image(__snake_case , __snake_case , __snake_case , __snake_case )
# prior
self.scheduler.set_timesteps(__snake_case , device=__snake_case )
_SCREAMING_SNAKE_CASE : str = self.scheduler.timesteps
_SCREAMING_SNAKE_CASE : Tuple = self.prior.config.num_embeddings
_SCREAMING_SNAKE_CASE : Optional[int] = self.prior.config.embedding_dim
_SCREAMING_SNAKE_CASE : Dict = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , __snake_case , __snake_case , __snake_case , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
_SCREAMING_SNAKE_CASE : Tuple = latents.reshape(latents.shape[0] , __snake_case , __snake_case )
for i, t in enumerate(self.progress_bar(__snake_case ) ):
# expand the latents if we are doing classifier free guidance
_SCREAMING_SNAKE_CASE : List[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_SCREAMING_SNAKE_CASE : Optional[int] = self.scheduler.scale_model_input(__snake_case , __snake_case )
_SCREAMING_SNAKE_CASE : Tuple = self.prior(
__snake_case , timestep=__snake_case , proj_embedding=__snake_case , ).predicted_image_embedding
# remove the variance
_SCREAMING_SNAKE_CASE : Any = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
_SCREAMING_SNAKE_CASE : Any = noise_pred.chunk(2 )
_SCREAMING_SNAKE_CASE : str = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
_SCREAMING_SNAKE_CASE : List[str] = self.scheduler.step(
__snake_case , timestep=__snake_case , sample=__snake_case , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=__snake_case )
_SCREAMING_SNAKE_CASE : List[Any] = []
for i, latent in enumerate(__snake_case ):
print()
_SCREAMING_SNAKE_CASE : Dict = self.renderer.decode(
latent[None, :] , __snake_case , size=__snake_case , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(__snake_case )
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.stack(__snake_case )
if output_type not in ["np", "pil"]:
raise ValueError(f"""Only the output types `pil` and `np` are supported not output_type={output_type}""" )
_SCREAMING_SNAKE_CASE : List[Any] = images.cpu().numpy()
if output_type == "pil":
_SCREAMING_SNAKE_CASE : Any = [self.numpy_to_pil(__snake_case ) for image in images]
# Offload last model to CPU
if hasattr(self , """final_offload_hook""" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=__snake_case )
| 200 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def A_ ( A__ ) -> Tuple:
# A local function to see if a dot lands in the circle.
def is_in_circle(A__ , A__ ) -> bool:
a__ : List[str] = 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
a__ : List[str] = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(A__ ) )
# The ratio of the area for circle to square is pi/4.
a__ : Optional[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 A_ ( A__ , A__ , A__ = 0.0 , A__ = 1.0 , ) -> float:
return mean(
function_to_integrate(uniform(A__ , A__ ) ) for _ in range(A__ ) ) * (max_value - min_value)
def A_ ( A__ , A__ = 0.0 , A__ = 1.0 ) -> None:
def identity_function(A__ ) -> float:
return x
a__ : List[Any] = area_under_curve_estimator(
A__ , A__ , A__ , A__ )
a__ : Union[str, Any] = (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 A_ ( A__ ) -> None:
def function_to_integrate(A__ ) -> float:
return sqrt(4.0 - x * x )
a__ : Dict = area_under_curve_estimator(
A__ , A__ , 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()
| 99 | 0 |
__A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def snake_case_(_UpperCamelCase ) -> bytes:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
_snake_case = F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(_UpperCamelCase )
_snake_case = ''''''.join(bin(_UpperCamelCase )[2:].zfill(8 ) for byte in data )
_snake_case = len(_UpperCamelCase ) % 6 != 0
if padding_needed:
# The padding that will be added later
_snake_case = b'''=''' * ((6 - len(_UpperCamelCase ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_UpperCamelCase ) % 6)
else:
_snake_case = b''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_UpperCamelCase ) , 6 ) ).encode()
+ padding
)
def snake_case_(_UpperCamelCase ) -> bytes:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ) and not isinstance(_UpperCamelCase , _UpperCamelCase ):
_snake_case = (
'''argument should be a bytes-like object or ASCII string, '''
F"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(_UpperCamelCase )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_UpperCamelCase , _UpperCamelCase ):
try:
_snake_case = encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
_snake_case = encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_UpperCamelCase ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
_snake_case = encoded_data[:-padding]
_snake_case = ''''''.join(
bin(B64_CHARSET.index(_UpperCamelCase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
_snake_case = ''''''.join(
bin(B64_CHARSET.index(_UpperCamelCase ) )[2:].zfill(6 ) for char in encoded_data )
_snake_case = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_UpperCamelCase ) , 8 )
]
return bytes(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 278 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A = {'''allegro/herbert-base-cased''': 5_14}
__A = {}
class lowercase_ ( __lowercase ):
UpperCamelCase_ : Any = VOCAB_FILES_NAMES
UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : List[str] = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Optional[Any] = HerbertTokenizer
def __init__( self : Tuple , A__ : str=None , A__ : Optional[Any]=None , A__ : Union[str, Any]=None , A__ : Optional[int]="<s>" , A__ : Optional[int]="<unk>" , A__ : str="<pad>" , A__ : List[Any]="<mask>" , A__ : Dict="</s>" , **A__ : Optional[int] , ) -> Optional[int]:
super().__init__(
A__ , A__ , tokenizer_file=A__ , cls_token=A__ , unk_token=A__ , pad_token=A__ , mask_token=A__ , sep_token=A__ , **A__ , )
def UpperCamelCase_ ( self : str , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
_snake_case = [self.cls_token_id]
_snake_case = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Tuple , A__ : List[int] , A__ : Optional[List[int]] = None , A__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A__ , token_ids_a=A__ , already_has_special_tokens=A__ )
if token_ids_a is None:
return [1] + ([0] * len(A__ )) + [1]
return [1] + ([0] * len(A__ )) + [1] + ([0] * len(A__ )) + [1]
def UpperCamelCase_ ( self : Any , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
_snake_case = [self.sep_token_id]
_snake_case = [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 UpperCamelCase_ ( self : Union[str, Any] , A__ : str , A__ : Optional[str] = None ) -> Tuple[str]:
_snake_case = self._tokenizer.model.save(A__ , name=A__ )
return tuple(A__ )
| 278 | 1 |
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