code
stringlengths 82
54.1k
| code_codestyle
int64 0
699
| style_context
stringlengths 111
35.6k
| style_context_codestyle
int64 0
699
| label
int64 0
1
|
|---|---|---|---|---|
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( ):
__magic_name__ : List[str] ={
"""repo_name""": ["""test_repo1""", """test_repo2""", """test_repo3"""],
"""path""": ["""test_1.py""", """test_2.py""", """unit_test.py"""],
"""content""": ["""a """ * 20, """a """ * 30, """b """ * 7],
}
__magic_name__ : List[Any] =Dataset.from_dict(lowerCamelCase )
return dataset
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : List[Any] =get_dataset()
__magic_name__ : List[str] =make_duplicate_clusters(__snake_case , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Dict =get_dataset()
__magic_name__ , __magic_name__ : List[str] =deduplicate_dataset(__snake_case )
self.assertEqual(len(__snake_case ) , 2 )
print(__snake_case )
self.assertEqual(duplicate_clusters[0][0]["""copies"""] , 2 )
self.assertEqual(duplicate_clusters[0][0]["""is_extreme"""] , __snake_case )
| 21 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''megatron-bert'''
def __init__( self , _snake_case=2_90_56 , _snake_case=10_24 , _snake_case=24 , _snake_case=16 , _snake_case=40_96 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
| 4 | 0 |
'''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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
_a = tempfile.mkdtemp()
_a = BlipImageProcessor()
_a = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
_a = BlipaProcessor(lowerCAmelCase_ , lowerCAmelCase_ )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : int , **lowerCAmelCase_ : Any ) -> Any:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase_ ).tokenizer
def __lowerCAmelCase ( self : Tuple , **lowerCAmelCase_ : Any ) -> Any:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase_ ).image_processor
def __lowerCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
_a = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_a = [Image.fromarray(np.moveaxis(lowerCAmelCase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
_a = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_a = self.get_image_processor(do_normalize=lowerCAmelCase_ , padding_value=1.0 )
_a = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=lowerCAmelCase_ , padding_value=1.0 )
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 : str ) -> str:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_a = self.prepare_image_inputs()
_a = image_processor(lowerCAmelCase_ , return_tensors='''np''' )
_a = 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 : Any ) -> str:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_a = '''lower newer'''
_a = processor(text=lowerCAmelCase_ )
_a = tokenizer(lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_a = '''lower newer'''
_a = self.prepare_image_inputs()
_a = processor(text=lowerCAmelCase_ , images=lowerCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase_ ):
processor()
def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_a = processor.batch_decode(lowerCAmelCase_ )
_a = tokenizer.batch_decode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
_a = '''lower newer'''
_a = self.prepare_image_inputs()
_a = processor(text=lowerCAmelCase_ , images=lowerCAmelCase_ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
| 22 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
import sys
import turtle
def _snake_case (__lowercase , __lowercase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase , ):
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1])
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1])
my_pen.goto(vertexa[0] , vertexa[1])
my_pen.goto(vertexa[0] , vertexa[1])
if depth == 0:
return
triangle(__lowercase , get_mid(__lowercase , __lowercase) , get_mid(__lowercase , __lowercase) , depth - 1)
triangle(__lowercase , get_mid(__lowercase , __lowercase) , get_mid(__lowercase , __lowercase) , depth - 1)
triangle(__lowercase , get_mid(__lowercase , __lowercase) , get_mid(__lowercase , __lowercase) , depth - 1)
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
"""Correct format for using this script: """
"""python fractals.py <int:depth_for_fractal>"""
)
snake_case__ : Tuple = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor("""red""")
snake_case__ : Optional[Any] = [(-1_7_5, -1_2_5), (0, 1_7_5), (1_7_5, -1_2_5)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 23 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class a ( a__ ):
snake_case__ = 42
class a ( a__ , a__ ):
@register_to_config
def __init__( self , _snake_case = 3 , _snake_case = 3 , _snake_case = ("DownEncoderBlock2D",) , _snake_case = ("UpDecoderBlock2D",) , _snake_case = (64,) , _snake_case = 1 , _snake_case = "silu" , _snake_case = 3 , _snake_case = 32 , _snake_case = 2_56 , _snake_case = 32 , _snake_case = None , _snake_case = 0.18_215 , _snake_case = "group" , ):
"""simple docstring"""
super().__init__()
# pass init params to Encoder
lowerCAmelCase = Encoder(
in_channels=_snake_case , out_channels=_snake_case , down_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , double_z=_snake_case , )
lowerCAmelCase = vq_embed_dim if vq_embed_dim is not None else latent_channels
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
lowerCAmelCase = VectorQuantizer(_snake_case , _snake_case , beta=0.25 , remap=_snake_case , sane_index_shape=_snake_case )
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
# pass init params to Decoder
lowerCAmelCase = Decoder(
in_channels=_snake_case , out_channels=_snake_case , up_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , norm_type=_snake_case , )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = self.encoder(_snake_case )
lowerCAmelCase = self.quant_conv(_snake_case )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_snake_case )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = False , _snake_case = True ):
"""simple docstring"""
if not force_not_quantize:
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = self.quantize(_snake_case )
else:
lowerCAmelCase = h
lowerCAmelCase = self.post_quant_conv(_snake_case )
lowerCAmelCase = self.decoder(_snake_case , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = sample
lowerCAmelCase = self.encode(_snake_case ).latents
lowerCAmelCase = self.decode(_snake_case ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
| 4 | 0 |
'''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 lowerCAmelCase ( unittest.TestCase):
def lowerCAmelCase ( self ) -> Any:
'''simple docstring'''
__snake_case = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
__snake_case = dict(zip(__SCREAMING_SNAKE_CASE , range(len(__SCREAMING_SNAKE_CASE ) ) ) )
__snake_case = {
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
__snake_case = {
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 1_6000,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
__snake_case = tempfile.mkdtemp()
__snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__snake_case = os.path.join(self.tmpdirname , __SCREAMING_SNAKE_CASE )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE ) + '''\n''' )
with open(self.feature_extraction_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE ) + '''\n''' )
# load decoder from hub
__snake_case = '''hf-internal-testing/ngram-beam-search-decoder'''
def lowerCAmelCase ( self , **__SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
__snake_case = self.add_kwargs_tokens_map.copy()
kwargs.update(__SCREAMING_SNAKE_CASE )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self , **__SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self , **__SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> int:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
__snake_case = self.get_tokenizer()
__snake_case = self.get_feature_extractor()
__snake_case = self.get_decoder()
__snake_case = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
__snake_case = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __SCREAMING_SNAKE_CASE )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __SCREAMING_SNAKE_CASE )
# 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 , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
__snake_case = 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
__snake_case = 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 lowerCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
__snake_case = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(__SCREAMING_SNAKE_CASE , '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = self.get_feature_extractor()
__snake_case = self.get_tokenizer()
__snake_case = self.get_decoder()
__snake_case = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
__snake_case = floats_list((3, 1000) )
__snake_case = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
__snake_case = processor(__SCREAMING_SNAKE_CASE , 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 ) -> Tuple:
'''simple docstring'''
__snake_case = self.get_feature_extractor()
__snake_case = self.get_tokenizer()
__snake_case = self.get_decoder()
__snake_case = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
__snake_case = '''This is a test string'''
__snake_case = processor(text=__SCREAMING_SNAKE_CASE )
__snake_case = tokenizer(__SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE=(2, 10, 16) , __SCREAMING_SNAKE_CASE=77 ) -> Tuple:
'''simple docstring'''
np.random.seed(__SCREAMING_SNAKE_CASE )
return np.random.rand(*__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
__snake_case = self.get_feature_extractor()
__snake_case = self.get_tokenizer()
__snake_case = self.get_decoder()
__snake_case = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
__snake_case = self._get_dummy_logits(shape=(10, 16) , seed=13 )
__snake_case = processor.decode(__SCREAMING_SNAKE_CASE )
__snake_case = decoder.decode_beams(__SCREAMING_SNAKE_CASE )[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 lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
__snake_case = self.get_feature_extractor()
__snake_case = self.get_tokenizer()
__snake_case = self.get_decoder()
__snake_case = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
__snake_case = 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:
__snake_case = processor.batch_decode(__SCREAMING_SNAKE_CASE )
else:
with get_context(__SCREAMING_SNAKE_CASE ).Pool() as pool:
__snake_case = processor.batch_decode(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case = list(__SCREAMING_SNAKE_CASE )
with get_context('''fork''' ).Pool() as p:
__snake_case = decoder.decode_beams_batch(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case , __snake_case , __snake_case = [], [], []
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(__SCREAMING_SNAKE_CASE , decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] , decoded_processor.text )
self.assertListEqual(__SCREAMING_SNAKE_CASE , decoded_processor.logit_score )
self.assertListEqual(__SCREAMING_SNAKE_CASE , decoded_processor.lm_score )
def lowerCAmelCase ( self ) -> Any:
'''simple docstring'''
__snake_case = self.get_feature_extractor()
__snake_case = self.get_tokenizer()
__snake_case = self.get_decoder()
__snake_case = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
__snake_case = self._get_dummy_logits()
__snake_case = 15
__snake_case = -20.0
__snake_case = -4.0
__snake_case = processor.batch_decode(
__SCREAMING_SNAKE_CASE , beam_width=__SCREAMING_SNAKE_CASE , beam_prune_logp=__SCREAMING_SNAKE_CASE , token_min_logp=__SCREAMING_SNAKE_CASE , )
__snake_case = decoded_processor_out.text
__snake_case = list(__SCREAMING_SNAKE_CASE )
with get_context('''fork''' ).Pool() as pool:
__snake_case = decoder.decode_beams_batch(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , beam_width=__SCREAMING_SNAKE_CASE , beam_prune_logp=__SCREAMING_SNAKE_CASE , token_min_logp=__SCREAMING_SNAKE_CASE , )
__snake_case = [d[0][0] for d in decoded_decoder_out]
__snake_case = [d[0][2] for d in decoded_decoder_out]
__snake_case = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] , __SCREAMING_SNAKE_CASE )
self.assertTrue(np.array_equal(__SCREAMING_SNAKE_CASE , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , __SCREAMING_SNAKE_CASE , atol=1E-3 ) )
self.assertTrue(np.array_equal(__SCREAMING_SNAKE_CASE , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9_474] , __SCREAMING_SNAKE_CASE , atol=1E-3 ) )
def lowerCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
__snake_case = self.get_feature_extractor()
__snake_case = self.get_tokenizer()
__snake_case = self.get_decoder()
__snake_case = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
__snake_case = self._get_dummy_logits()
__snake_case = 2.0
__snake_case = 5.0
__snake_case = -20.0
__snake_case = True
__snake_case = processor.batch_decode(
__SCREAMING_SNAKE_CASE , alpha=__SCREAMING_SNAKE_CASE , beta=__SCREAMING_SNAKE_CASE , unk_score_offset=__SCREAMING_SNAKE_CASE , lm_score_boundary=__SCREAMING_SNAKE_CASE , )
__snake_case = decoded_processor_out.text
__snake_case = list(__SCREAMING_SNAKE_CASE )
decoder.reset_params(
alpha=__SCREAMING_SNAKE_CASE , beta=__SCREAMING_SNAKE_CASE , unk_score_offset=__SCREAMING_SNAKE_CASE , lm_score_boundary=__SCREAMING_SNAKE_CASE , )
with get_context('''fork''' ).Pool() as pool:
__snake_case = decoder.decode_beams_batch(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )
__snake_case = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] , __SCREAMING_SNAKE_CASE )
__snake_case = 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 , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__snake_case = processor.decoder.model_container[processor.decoder._model_key]
__snake_case = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
__snake_case = os.listdir(__SCREAMING_SNAKE_CASE )
__snake_case = ['''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(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> Any:
'''simple docstring'''
__snake_case = snapshot_download('''hf-internal-testing/processor_with_lm''' )
__snake_case = WavaVecaProcessorWithLM.from_pretrained(__SCREAMING_SNAKE_CASE )
__snake_case = processor.decoder.model_container[processor.decoder._model_key]
__snake_case = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
__snake_case = os.listdir(__SCREAMING_SNAKE_CASE )
__snake_case = os.listdir(__SCREAMING_SNAKE_CASE )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__snake_case = AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__snake_case = floats_list((3, 1000) )
__snake_case = processor_wavaveca(__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
__snake_case = processor_auto(__SCREAMING_SNAKE_CASE , return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
__snake_case = self._get_dummy_logits()
__snake_case = processor_wavaveca.batch_decode(__SCREAMING_SNAKE_CASE )
__snake_case = processor_auto.batch_decode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def lowerCAmelCase ( self ) -> List[str]:
'''simple docstring'''
__snake_case = self.get_feature_extractor()
__snake_case = self.get_tokenizer()
__snake_case = self.get_decoder()
__snake_case = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
@staticmethod
def lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
__snake_case = [d[key] for d in offsets]
return retrieved_list
def lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
__snake_case = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__snake_case = self._get_dummy_logits()[0]
__snake_case = processor.decode(__SCREAMING_SNAKE_CASE , output_word_offsets=__SCREAMING_SNAKE_CASE )
# 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(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
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 lowerCAmelCase ( self ) -> str:
'''simple docstring'''
__snake_case = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__snake_case = self._get_dummy_logits()
__snake_case = processor.batch_decode(__SCREAMING_SNAKE_CASE , output_word_offsets=__SCREAMING_SNAKE_CASE )
# 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(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(__SCREAMING_SNAKE_CASE , '''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 lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
import torch
__snake_case = load_dataset('''common_voice''' , '''en''' , split='''train''' , streaming=__SCREAMING_SNAKE_CASE )
__snake_case = ds.cast_column('''audio''' , datasets.Audio(sampling_rate=1_6000 ) )
__snake_case = iter(__SCREAMING_SNAKE_CASE )
__snake_case = next(__SCREAMING_SNAKE_CASE )
__snake_case = AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
__snake_case = 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
__snake_case = processor(sample['''audio''']['''array'''] , return_tensors='''pt''' ).input_values
with torch.no_grad():
__snake_case = model(__SCREAMING_SNAKE_CASE ).logits.cpu().numpy()
__snake_case = processor.decode(logits[0] , output_word_offsets=__SCREAMING_SNAKE_CASE )
__snake_case = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
__snake_case = [
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
__snake_case = '''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(__SCREAMING_SNAKE_CASE , '''word''' ) ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(''' '''.join(self.get_from_offsets(__SCREAMING_SNAKE_CASE , '''word''' ) ) , output.text )
# output times
__snake_case = torch.tensor(self.get_from_offsets(__SCREAMING_SNAKE_CASE , '''start_time''' ) )
__snake_case = torch.tensor(self.get_from_offsets(__SCREAMING_SNAKE_CASE , '''end_time''' ) )
# fmt: off
__snake_case = torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599] )
__snake_case = torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=0.01 ) )
self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=0.01 ) )
| 24 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = vertices
lowerCAmelCase = {
(min(_snake_case ), max(_snake_case )): weight for edge, weight in edges.items()
}
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
lowerCAmelCase = weight
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Graph({min(self.vertices )} , {} )
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
lowerCAmelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
lowerCAmelCase = edge
lowerCAmelCase = weight
subgraph.add_edge(_snake_case , _snake_case )
return subgraph
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "p107_network.txt" ):
lowerCAmelCase = os.path.abspath(os.path.dirname(_UpperCAmelCase ) )
lowerCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = {}
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
with open(_UpperCAmelCase ) as f:
lowerCAmelCase = f.read().strip().split('\n' )
lowerCAmelCase = [line.split(',' ) for line in data]
for edgea in range(1 , len(_UpperCAmelCase ) ):
for edgea in range(_UpperCAmelCase ):
if adjaceny_matrix[edgea][edgea] != "-":
lowerCAmelCase = int(adjaceny_matrix[edgea][edgea] )
lowerCAmelCase = Graph(set(range(len(_UpperCAmelCase ) ) ) , _UpperCAmelCase )
lowerCAmelCase = graph.prims_algorithm()
lowerCAmelCase = sum(graph.edges.values() )
lowerCAmelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 4 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
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 MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def lowerCamelCase__ ( _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ):
if attention_mask is None:
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE : Tuple = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
SCREAMING_SNAKE_CASE : str = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=_a)
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE : Optional[int] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_a)
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE : List[Any] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_a)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] , a : Optional[Any] , a : Any=13 , a : Optional[int]=7 , a : Optional[Any]=True , a : Dict=False , a : List[str]=99 , a : Any=16 , a : Optional[int]=2 , a : Union[str, Any]=4 , a : List[Any]=4 , a : Dict="relu" , a : Any=0.1 , a : Optional[Any]=0.1 , a : str=0.0 , a : List[Any]=0.0 , a : Dict=20 , a : Optional[int]=2 , a : Optional[Any]=1 , a : Any=0 , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = parent
SCREAMING_SNAKE_CASE : Optional[Any] = batch_size
SCREAMING_SNAKE_CASE : str = seq_length
SCREAMING_SNAKE_CASE : str = is_training
SCREAMING_SNAKE_CASE : List[Any] = use_labels
SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE : Dict = hidden_size
SCREAMING_SNAKE_CASE : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Dict = hidden_act
SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Any = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = encoder_layerdrop
SCREAMING_SNAKE_CASE : int = decoder_layerdrop
SCREAMING_SNAKE_CASE : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = eos_token_id
SCREAMING_SNAKE_CASE : Optional[Any] = pad_token_id
SCREAMING_SNAKE_CASE : Optional[Any] = bos_token_id
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Any = self.eos_token_id # Eos Token
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE : int = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE : str = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE : str = prepare_mam_aaa_inputs_dict(a , a , a )
return config, inputs_dict
def __UpperCamelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def __UpperCamelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Any = self.prepare_config_and_inputs()
return config, inputs_dict
def __UpperCamelCase ( self : Dict , a : Tuple , a : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = MaMaaaModel(config=a ).get_decoder().to(a ).eval()
SCREAMING_SNAKE_CASE : Optional[int] = inputs_dict["input_ids"]
SCREAMING_SNAKE_CASE : Optional[Any] = inputs_dict["attention_mask"]
SCREAMING_SNAKE_CASE : Union[str, Any] = inputs_dict["head_mask"]
# first forward pass
SCREAMING_SNAKE_CASE : Union[str, Any] = model(a , attention_mask=a , head_mask=a , use_cache=a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE : Union[str, Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE : int = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE : List[str] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE : int = model(a , attention_mask=a )["last_hidden_state"]
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , past_key_values=a )[
"last_hidden_state"
]
# select random slice
SCREAMING_SNAKE_CASE : Union[str, Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE : List[str] = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE : Union[str, Any] = 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(a , a , atol=1e-2 ) )
def __UpperCamelCase ( self : Any , a : Any , a : Any ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = MaMaaaModel(config=a ).to(a ).eval()
SCREAMING_SNAKE_CASE : str = model(**a )
SCREAMING_SNAKE_CASE : Dict = outputs.encoder_last_hidden_state
SCREAMING_SNAKE_CASE : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Optional[Any] = model.get_encoder()
encoder.save_pretrained(a )
SCREAMING_SNAKE_CASE : Dict = MaMaaaEncoder.from_pretrained(a ).to(a )
SCREAMING_SNAKE_CASE : Optional[Any] = encoder(inputs_dict["input_ids"] , attention_mask=inputs_dict["attention_mask"] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Optional[Any] = model.get_decoder()
decoder.save_pretrained(a )
SCREAMING_SNAKE_CASE : str = MaMaaaDecoder.from_pretrained(a ).to(a )
SCREAMING_SNAKE_CASE : Dict = decoder(
input_ids=inputs_dict["decoder_input_ids"] , attention_mask=inputs_dict["decoder_attention_mask"] , encoder_hidden_states=a , encoder_attention_mask=inputs_dict["attention_mask"] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class _UpperCamelCase ( __A , __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
lowerCamelCase__ =(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
lowerCamelCase__ =(
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =False
lowerCamelCase__ =False
def __UpperCamelCase ( self : List[Any] , a : Tuple , a : int , a : Optional[Any] , a : Optional[Any] , a : Tuple ) -> Dict:
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = MaMaaaModelTester(self )
SCREAMING_SNAKE_CASE : Tuple = ConfigTester(self , config_class=a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Any ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Dict = model_class(a )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = model_class.from_pretrained(a , output_loading_info=a )
self.assertEqual(info["missing_keys"] , [] )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*a )
def __UpperCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
SCREAMING_SNAKE_CASE : List[str] = model_class(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = copy.deepcopy(self._prepare_for_class(a , a ) )
if not self.is_encoder_decoder:
SCREAMING_SNAKE_CASE : Optional[Any] = inputs["input_ids"]
del inputs["input_ids"]
else:
SCREAMING_SNAKE_CASE : str = inputs["input_ids"]
SCREAMING_SNAKE_CASE : Optional[Any] = inputs.get("decoder_input_ids" , a )
del inputs["input_ids"]
inputs.pop("decoder_input_ids" , a )
SCREAMING_SNAKE_CASE : Any = model.get_input_embeddings()
if not self.is_encoder_decoder:
SCREAMING_SNAKE_CASE : Tuple = wte(a )
else:
SCREAMING_SNAKE_CASE : List[str] = wte(a )
SCREAMING_SNAKE_CASE : Dict = wte(a )
with torch.no_grad():
model(**a )[0]
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE : Optional[int] = input_dict["input_ids"]
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.ne(1 ).to(a )
SCREAMING_SNAKE_CASE : str = MaMaaaForConditionalGeneration(a ).eval().to(a )
if torch_device == "cuda":
model.half()
model.generate(a , attention_mask=a )
model.generate(num_beams=4 , do_sample=a , early_stopping=a , num_return_sequences=3 )
def lowerCamelCase__ ( _a):
return torch.tensor(_a , dtype=torch.long , device=_a)
a_ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __UpperCamelCase ( self : Any ) -> Tuple:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(a )
SCREAMING_SNAKE_CASE : List[str] = _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]] )
SCREAMING_SNAKE_CASE : Union[str, Any] = _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]] )
SCREAMING_SNAKE_CASE : Any = prepare_mam_aaa_inputs_dict(model.config , a , a )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Dict = model(**a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , a )
# change to expected output here
SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=a )
self.assertTrue(torch.allclose(output[:, :3, :3] , a , atol=a ) )
def __UpperCamelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(a )
# change to intended input
SCREAMING_SNAKE_CASE : str = _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]] )
SCREAMING_SNAKE_CASE : Dict = _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]] )
SCREAMING_SNAKE_CASE : Any = prepare_mam_aaa_inputs_dict(model.config , a , a )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Union[str, Any] = model(**a )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , a )
# change to expected output here
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=a )
self.assertTrue(torch.allclose(output[:, :3, :3] , a , atol=a ) )
def __UpperCamelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(a )
SCREAMING_SNAKE_CASE : Tuple = MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" , src_lang="fr" , tgt_lang="en" )
SCREAMING_SNAKE_CASE : str = [
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(a , padding=a , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Union[str, Any] = model.generate(
input_ids=dct["input_ids"].to(a ) , attention_mask=dct["attention_mask"].to(a ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("en" ) , )
SCREAMING_SNAKE_CASE : str = [
"The NSA case highlights the total absence of intelligence debate",
"I think there are two levels of response from the French government.",
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
" communications in France.",
]
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=a , skip_special_tokens=a )
assert generated == expected_en | 25 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase )] )
lowerCAmelCase = np.array(_UpperCAmelCase )
lowerCAmelCase = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , _UpperCAmelCase ) ) , x.transpose() ) , _UpperCAmelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = (1, 2, 1)
lowerCAmelCase = (1, 1, 0, 7)
lowerCAmelCase = SARIMAX(
_UpperCAmelCase , exog=_UpperCAmelCase , order=_UpperCAmelCase , seasonal_order=_UpperCAmelCase )
lowerCAmelCase = model.fit(disp=_UpperCAmelCase , maxiter=600 , method='nm' )
lowerCAmelCase = model_fit.predict(1 , len(_UpperCAmelCase ) , exog=[test_match] )
return result[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = SVR(kernel='rbf' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = regressor.predict(_UpperCAmelCase )
return y_pred[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list ):
train_user.sort()
lowerCAmelCase = np.percentile(_UpperCAmelCase , 25 )
lowerCAmelCase = np.percentile(_UpperCAmelCase , 75 )
lowerCAmelCase = qa - qa
lowerCAmelCase = qa - (iqr * 0.1)
return low_lim
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : float ):
lowerCAmelCase = 0
lowerCAmelCase = 0
for i in list_vote:
if i > actual_result:
lowerCAmelCase = not_safe + 1
else:
if abs(abs(_UpperCAmelCase ) - abs(_UpperCAmelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
__UpperCamelCase : Optional[Any] = [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
__UpperCamelCase : Any = pd.DataFrame(
data_input, columns=['''total_user''', '''total_even''', '''days''']
)
__UpperCamelCase : Dict = Normalizer().fit_transform(data_input_df.values)
# split data
__UpperCamelCase : Dict = normalize_df[:, 2].tolist()
__UpperCamelCase : Union[str, Any] = normalize_df[:, 0].tolist()
__UpperCamelCase : List[str] = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
__UpperCamelCase : Optional[int] = normalize_df[:, [1, 2]].tolist()
__UpperCamelCase : Tuple = x[: len(x) - 1]
__UpperCamelCase : Any = x[len(x) - 1 :]
# for linear regression & sarimax
__UpperCamelCase : str = total_date[: len(total_date) - 1]
__UpperCamelCase : Union[str, Any] = total_user[: len(total_user) - 1]
__UpperCamelCase : List[Any] = total_match[: len(total_match) - 1]
__UpperCamelCase : Optional[Any] = total_date[len(total_date) - 1 :]
__UpperCamelCase : str = total_user[len(total_user) - 1 :]
__UpperCamelCase : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
__UpperCamelCase : Any = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
__UpperCamelCase : List[str] = '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
print('''Today\'s data is {not_str}safe.''')
| 4 | 0 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCamelCase = 16
__UpperCamelCase = 32
def _a ( _lowerCamelCase , _lowerCamelCase = 16 ) -> Dict:
"""simple docstring"""
__snake_case : Optional[Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
__snake_case : Dict = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(_lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
__snake_case : List[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=_lowerCamelCase , max_length=_lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__snake_case : Optional[Any] = datasets.map(
_lowerCamelCase , batched=_lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__snake_case : int = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(_lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__snake_case : Optional[int] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__snake_case : int = 16
elif accelerator.mixed_precision != "no":
__snake_case : Any = 8
else:
__snake_case : List[Any] = None
return tokenizer.pad(
_lowerCamelCase , padding="""longest""" , max_length=_lowerCamelCase , pad_to_multiple_of=_lowerCamelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
__snake_case : List[str] = DataLoader(
tokenized_datasets["""train"""] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase )
__snake_case : Dict = DataLoader(
tokenized_datasets["""validation"""] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__UpperCamelCase = mocked_dataloaders # noqa: F811
def _a ( _lowerCamelCase , _lowerCamelCase ) -> Tuple:
"""simple docstring"""
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , _lowerCamelCase ) == "1":
__snake_case : Optional[int] = 2
# Initialize accelerator
__snake_case : List[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__snake_case : Union[str, Any] = config["""lr"""]
__snake_case : Optional[int] = int(config["""num_epochs"""] )
__snake_case : Optional[int] = int(config["""seed"""] )
__snake_case : List[Any] = int(config["""batch_size"""] )
__snake_case : int = evaluate.load("""glue""" , """mrpc""" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=_lowerCamelCase )
def inner_training_loop(_lowerCamelCase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(_lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__snake_case : str = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=_lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__snake_case : Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
__snake_case : int = AdamW(params=model.parameters() , lr=_lowerCamelCase )
__snake_case , __snake_case : Optional[int] = get_dataloaders(_lowerCamelCase , _lowerCamelCase )
# Instantiate scheduler
__snake_case : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=_lowerCamelCase , num_warmup_steps=100 , num_training_steps=(len(_lowerCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : int = accelerator.prepare(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# Now we train the model
for epoch in range(_lowerCamelCase ):
model.train()
for step, batch in enumerate(_lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__snake_case : Optional[int] = model(**_lowerCamelCase )
__snake_case : Optional[Any] = outputs.loss
accelerator.backward(_lowerCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__snake_case : Optional[Any] = model(**_lowerCamelCase )
__snake_case : List[Any] = outputs.logits.argmax(dim=-1 )
__snake_case , __snake_case : Optional[int] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=_lowerCamelCase , references=_lowerCamelCase , )
__snake_case : Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , _lowerCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def _a ( ) -> Optional[int]:
"""simple docstring"""
__snake_case : Optional[Any] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=_lowerCamelCase , default=_lowerCamelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
__snake_case : Any = parser.parse_args()
__snake_case : Any = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(_lowerCamelCase , _lowerCamelCase )
if __name__ == "__main__":
main()
| 26 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'-m' , '--pretrained_model_name_or_path' , type=_UpperCAmelCase , default=_UpperCAmelCase , required=_UpperCAmelCase , help='Path to pretrained model or model identifier from huggingface.co/models.' , )
parser.add_argument(
'-c' , '--caption' , type=_UpperCAmelCase , default='robotic cat with wings' , help='Text used to generate images.' , )
parser.add_argument(
'-n' , '--images_num' , type=_UpperCAmelCase , default=4 , help='How much images to generate.' , )
parser.add_argument(
'-s' , '--seed' , type=_UpperCAmelCase , default=42 , help='Seed for random process.' , )
parser.add_argument(
'-ci' , '--cuda_id' , type=_UpperCAmelCase , default=0 , help='cuda_id.' , )
lowerCAmelCase = parser.parse_args()
return args
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] ):
if not len(_UpperCAmelCase ) == rows * cols:
raise ValueError('The specified number of rows and columns are not correct.' )
lowerCAmelCase ,lowerCAmelCase = imgs[0].size
lowerCAmelCase = Image.new('RGB' , size=(cols * w, rows * h) )
lowerCAmelCase ,lowerCAmelCase = grid.size
for i, img in enumerate(_UpperCAmelCase ):
grid.paste(_UpperCAmelCase , box=(i % cols * w, i // cols * h) )
return grid
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any]="robotic cat with wings" , _UpperCAmelCase : Optional[int]=7.5 , _UpperCAmelCase : Dict=50 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : int=42 , ):
lowerCAmelCase = torch.Generator(pipeline.device ).manual_seed(_UpperCAmelCase )
lowerCAmelCase = pipeline(
_UpperCAmelCase , guidance_scale=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , generator=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , ).images
lowerCAmelCase = int(math.sqrt(_UpperCAmelCase ) )
lowerCAmelCase = image_grid(_UpperCAmelCase , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__UpperCamelCase : Optional[Any] = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase : List[Any] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__UpperCamelCase : str = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__UpperCamelCase : Optional[int] = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__UpperCamelCase : List[str] = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')
__UpperCamelCase : Tuple = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase : Union[str, Any] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
__UpperCamelCase : Dict = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, '''unet''', unet)
else:
__UpperCamelCase : Dict = unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase : Optional[Any] = pipeline.to(unet.device)
__UpperCamelCase ,__UpperCamelCase : List[Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
__UpperCamelCase : int = os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))
| 4 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCamelCase( __snake_case , unittest.TestCase ):
'''simple docstring'''
__magic_name__ = KandinskyVaaControlnetPipeline
__magic_name__ = ['image_embeds', 'negative_image_embeds', 'hint']
__magic_name__ = ['image_embeds', 'negative_image_embeds', 'hint']
__magic_name__ = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
__magic_name__ = False
@property
def lowerCAmelCase__ ( self ):
return 32
@property
def lowerCAmelCase__ ( self ):
return 32
@property
def lowerCAmelCase__ ( self ):
return self.time_input_dim
@property
def lowerCAmelCase__ ( self ):
return self.time_input_dim * 4
@property
def lowerCAmelCase__ ( self ):
return 100
@property
def lowerCAmelCase__ ( self ):
torch.manual_seed(0 )
_A = {
'in_channels': 8,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image_hint',
'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,
}
_A = UNetaDConditionModel(**snake_case_ )
return model
@property
def lowerCAmelCase__ ( self ):
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def lowerCAmelCase__ ( self ):
torch.manual_seed(0 )
_A = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCAmelCase__ ( self ):
_A = self.dummy_unet
_A = self.dummy_movq
_A = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='linear' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , steps_offset=1 , prediction_type='epsilon' , thresholding=snake_case_ , )
_A = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCAmelCase__ ( self , snake_case_ , snake_case_=0 ):
_A = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
_A = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case_ )
# create hint
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
if str(snake_case_ ).startswith('mps' ):
_A = torch.manual_seed(snake_case_ )
else:
_A = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_A = {
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'hint': hint,
'generator': generator,
'height': 64,
'width': 64,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def lowerCAmelCase__ ( self ):
_A = 'cpu'
_A = self.get_dummy_components()
_A = self.pipeline_class(**snake_case_ )
_A = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_A = pipe(**self.get_dummy_inputs(snake_case_ ) )
_A = output.images
_A = pipe(
**self.get_dummy_inputs(snake_case_ ) , return_dict=snake_case_ , )[0]
_A = image[0, -3:, -3:, -1]
_A = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_A = np.array(
[0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595] )
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 lowerCamelCase( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase__ ( self ):
_A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy' )
_A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/hint_image_cat.png' )
_A = torch.from_numpy(np.array(snake_case_ ) ).float() / 255.0
_A = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
_A = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case_ )
_A = KandinskyVaaControlnetPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-controlnet-depth' , torch_dtype=torch.floataa )
_A = pipeline.to(snake_case_ )
pipeline.set_progress_bar_config(disable=snake_case_ )
_A = 'A robot, 4k photo'
_A = torch.Generator(device='cuda' ).manual_seed(0 )
_A, _A = pipe_prior(
snake_case_ , generator=snake_case_ , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
_A = torch.Generator(device='cuda' ).manual_seed(0 )
_A = pipeline(
image_embeds=snake_case_ , negative_image_embeds=snake_case_ , hint=snake_case_ , generator=snake_case_ , num_inference_steps=100 , output_type='np' , )
_A = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ )
| 27 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : List[int] ):
lowerCAmelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(_UpperCAmelCase ) == len(_UpperCAmelCase ), F'{len(_UpperCAmelCase )} != {len(_UpperCAmelCase )}'
dest_layers.load_state_dict(layers_to_copy.state_dict() )
__UpperCamelCase : Optional[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCamelCase : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] ):
try:
lowerCAmelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
F' {n_student}' )
return list(range(_UpperCAmelCase ) )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ):
if n_student > n_teacher:
raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}' )
elif n_teacher == n_student:
return list(range(_UpperCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, PreTrainedModel] , _UpperCAmelCase : Union[str, Path] = "student" , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Optional[Any]=None , **_UpperCAmelCase : str , ):
lowerCAmelCase = 'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
assert (e is not None) or (d is not None), _msg
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
AutoTokenizer.from_pretrained(_UpperCAmelCase ).save_pretrained(_UpperCAmelCase ) # purely for convenience
lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).eval()
else:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ), F'teacher must be a model or string got type {type(_UpperCAmelCase )}'
lowerCAmelCase = teacher.config.to_diff_dict()
try:
lowerCAmelCase ,lowerCAmelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
init_kwargs.update({'encoder_layers': e, 'decoder_layers': d} )
except AttributeError: # T5
if hasattr(teacher.config , 'num_encoder_layers' ):
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
if hasattr(teacher.config , 'num_encoder_layers' ):
init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d} )
else:
init_kwargs.update({'num_layers': e, 'num_decoder_layers': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(_UpperCAmelCase )
# Copy weights
lowerCAmelCase = teacher.config_class(**_UpperCAmelCase )
lowerCAmelCase = AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
lowerCAmelCase = student.load_state_dict(teacher.state_dict() , strict=_UpperCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
lowerCAmelCase ,lowerCAmelCase = list(range(_UpperCAmelCase ) ), list(range(_UpperCAmelCase ) )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
F' {save_path}' )
student.save_pretrained(_UpperCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
if d_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
try:
if hasattr(
_UpperCAmelCase , 'prophetnet' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _UpperCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _UpperCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _UpperCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , _UpperCAmelCase )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' )
lowerCAmelCase = {
'teacher_type': teacher.config.model_type,
'copied_encoder_layers': e_layers_to_copy,
'copied_decoder_layers': d_layers_to_copy,
}
student.save_pretrained(_UpperCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowercase__( __UpperCamelCase: dict ,__UpperCamelCase: str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = set(__UpperCamelCase ), [start]
while stack:
SCREAMING_SNAKE_CASE : Any = stack.pop()
explored.add(__UpperCamelCase )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(__UpperCamelCase )
return explored
UpperCamelCase_ = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 28 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Dict = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = ['''LayoutXLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = ['''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
__UpperCamelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 4 | 0 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class __lowerCamelCase ( enum.Enum ):
a__: Any = 0
a__: Tuple = 1
a__: Optional[Any] = 2
@add_end_docstrings(lowerCAmelCase )
class __lowerCamelCase ( lowerCAmelCase ):
a__: Tuple = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ):
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
lowerCamelCase_ = None
if self.model.config.prefix is not None:
lowerCamelCase_ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
lowerCamelCase_ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = self._sanitize_parameters(prefix=UpperCAmelCase , **self._forward_params )
lowerCamelCase_ = {**self._preprocess_params, **preprocess_params}
lowerCamelCase_ = {**self._forward_params, **forward_params}
def UpperCAmelCase__ ( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase , ):
lowerCamelCase_ = {}
if prefix is not None:
lowerCamelCase_ = prefix
if prefix:
lowerCamelCase_ = self.tokenizer(
UpperCAmelCase , padding=UpperCAmelCase , add_special_tokens=UpperCAmelCase , return_tensors=self.framework )
lowerCamelCase_ = prefix_inputs['''input_ids'''].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
''' [None, \'hole\']''' )
lowerCamelCase_ = handle_long_generation
preprocess_params.update(UpperCAmelCase )
lowerCamelCase_ = generate_kwargs
lowerCamelCase_ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_full_text`''' )
if return_tensors is not None:
raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`''' )
lowerCamelCase_ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_tensors`''' )
lowerCamelCase_ = ReturnType.TENSORS
if return_type is not None:
lowerCamelCase_ = return_type
if clean_up_tokenization_spaces is not None:
lowerCamelCase_ = clean_up_tokenization_spaces
if stop_sequence is not None:
lowerCamelCase_ = self.tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
if len(UpperCAmelCase ) > 1:
warnings.warn(
'''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
''' the stop sequence will be used as the stop sequence string in the interim.''' )
lowerCamelCase_ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def UpperCAmelCase__ ( self , *UpperCAmelCase , **UpperCAmelCase ):
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({'''add_space_before_punct_symbol''': True} )
return super()._parse_and_tokenize(*UpperCAmelCase , **UpperCAmelCase )
def __call__( self , UpperCAmelCase , **UpperCAmelCase ):
return super().__call__(UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase="" , UpperCAmelCase=None , **UpperCAmelCase ):
lowerCamelCase_ = self.tokenizer(
prefix + prompt_text , padding=UpperCAmelCase , add_special_tokens=UpperCAmelCase , return_tensors=self.framework )
lowerCamelCase_ = prompt_text
if handle_long_generation == "hole":
lowerCamelCase_ = inputs['''input_ids'''].shape[-1]
if "max_new_tokens" in generate_kwargs:
lowerCamelCase_ = generate_kwargs['''max_new_tokens''']
else:
lowerCamelCase_ = generate_kwargs.get('''max_length''' , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError('''We cannot infer how many new tokens are expected''' )
if cur_len + new_tokens > self.tokenizer.model_max_length:
lowerCamelCase_ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
'''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
''' models max length''' )
lowerCamelCase_ = inputs['''input_ids'''][:, -keep_length:]
if "attention_mask" in inputs:
lowerCamelCase_ = inputs['''attention_mask'''][:, -keep_length:]
return inputs
def UpperCAmelCase__ ( self , UpperCAmelCase , **UpperCAmelCase ):
lowerCamelCase_ = model_inputs['''input_ids''']
lowerCamelCase_ = model_inputs.get('''attention_mask''' , UpperCAmelCase )
# Allow empty prompts
if input_ids.shape[1] == 0:
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = 1
else:
lowerCamelCase_ = input_ids.shape[0]
lowerCamelCase_ = model_inputs.pop('''prompt_text''' )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
lowerCamelCase_ = generate_kwargs.pop('''prefix_length''' , 0 )
if prefix_length > 0:
lowerCamelCase_ = '''max_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].max_new_tokens is not None
)
if not has_max_new_tokens:
lowerCamelCase_ = generate_kwargs.get('''max_length''' ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
lowerCamelCase_ = '''min_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
lowerCamelCase_ = self.model.generate(input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , **UpperCAmelCase )
lowerCamelCase_ = generated_sequence.shape[0]
if self.framework == "pt":
lowerCamelCase_ = generated_sequence.reshape(UpperCAmelCase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
lowerCamelCase_ = tf.reshape(UpperCAmelCase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase=ReturnType.FULL_TEXT , UpperCAmelCase=True ):
lowerCamelCase_ = model_outputs['''generated_sequence'''][0]
lowerCamelCase_ = model_outputs['''input_ids''']
lowerCamelCase_ = model_outputs['''prompt_text''']
lowerCamelCase_ = generated_sequence.numpy().tolist()
lowerCamelCase_ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
lowerCamelCase_ = {'''generated_token_ids''': sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
lowerCamelCase_ = self.tokenizer.decode(
UpperCAmelCase , skip_special_tokens=UpperCAmelCase , clean_up_tokenization_spaces=UpperCAmelCase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
lowerCamelCase_ = 0
else:
lowerCamelCase_ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=UpperCAmelCase , clean_up_tokenization_spaces=UpperCAmelCase , ) )
if return_type == ReturnType.FULL_TEXT:
lowerCamelCase_ = prompt_text + text[prompt_length:]
else:
lowerCamelCase_ = text[prompt_length:]
lowerCamelCase_ = {'''generated_text''': all_text}
records.append(UpperCAmelCase )
return records
| 29 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase = F'Resistor at index {index} has a negative or zero value!'
raise ValueError(_UpperCAmelCase )
first_sum += 1 / float(_UpperCAmelCase )
index += 1
return 1 / first_sum
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowerCAmelCase = F'Resistor at index {index} has a negative value!'
raise ValueError(_UpperCAmelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
import unittest
import numpy as np
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase = None , ):
'''simple docstring'''
UpperCAmelCase_ : Dict = np.shape(_lowercase )
UpperCAmelCase_ : Optional[Any] = np.shape(_lowercase )
UpperCAmelCase_ : Tuple = np.shape(_lowercase )
if shape_a[0] != shape_b[0]:
UpperCAmelCase_ : Tuple = (
'''Expected the same number of rows for A and B. '''
f'''Instead found A of size {shape_a} and B of size {shape_b}'''
)
raise ValueError(_lowercase )
if shape_b[1] != shape_c[1]:
UpperCAmelCase_ : List[Any] = (
'''Expected the same number of columns for B and C. '''
f'''Instead found B of size {shape_b} and C of size {shape_c}'''
)
raise ValueError(_lowercase )
UpperCAmelCase_ : Dict = pseudo_inv
if a_inv is None:
try:
UpperCAmelCase_ : Any = np.linalg.inv(_lowercase )
except np.linalg.LinAlgError:
raise ValueError(
'''Input matrix A is not invertible. Cannot compute Schur complement.''' )
return mat_c - mat_b.T @ a_inv @ mat_b
class __a( unittest.TestCase ):
"""simple docstring"""
def a__ ( self ) -> None:
UpperCAmelCase_ : str = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCAmelCase_ : Any = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCAmelCase_ : List[str] = np.array([[2, 1], [6, 3]] )
UpperCAmelCase_ : Tuple = schur_complement(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = np.block([[a, b], [b.T, c]] )
UpperCAmelCase_ : List[Any] = np.linalg.det(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = np.linalg.det(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = np.linalg.det(_SCREAMING_SNAKE_CASE )
self.assertAlmostEqual(_SCREAMING_SNAKE_CASE ,det_a * det_s )
def a__ ( self ) -> None:
UpperCAmelCase_ : str = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCAmelCase_ : Optional[int] = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCAmelCase_ : Optional[int] = np.array([[2, 1], [6, 3]] )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
schur_complement(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> None:
UpperCAmelCase_ : Optional[int] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCAmelCase_ : Optional[Any] = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCAmelCase_ : int = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
schur_complement(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main() | 30 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class a ( a__ ):
snake_case__ = '''glpn'''
def __init__( self , _snake_case=3 , _snake_case=4 , _snake_case=[2, 2, 2, 2] , _snake_case=[8, 4, 2, 1] , _snake_case=[32, 64, 1_60, 2_56] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[1, 2, 5, 8] , _snake_case=[4, 4, 4, 4] , _snake_case="gelu" , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=0.1 , _snake_case=1E-6 , _snake_case=64 , _snake_case=10 , _snake_case=-1 , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
lowerCAmelCase = num_channels
lowerCAmelCase = num_encoder_blocks
lowerCAmelCase = depths
lowerCAmelCase = sr_ratios
lowerCAmelCase = hidden_sizes
lowerCAmelCase = patch_sizes
lowerCAmelCase = strides
lowerCAmelCase = mlp_ratios
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = initializer_range
lowerCAmelCase = drop_path_rate
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = decoder_hidden_size
lowerCAmelCase = max_depth
lowerCAmelCase = head_in_index
| 4 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
lowerCamelCase__ : int = None
lowerCamelCase__ : Any = logging.get_logger(__name__)
lowerCamelCase__ : str = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
lowerCamelCase__ : Union[str, Any] = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
),
},
}
lowerCamelCase__ : Optional[Any] = {
'facebook/nllb-large-en-ro': 1_024,
'facebook/nllb-200-distilled-600M': 1_024,
}
# fmt: off
lowerCamelCase__ : List[Any] = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = ["input_ids", "attention_mask"]
lowercase_ = NllbTokenizer
lowercase_ = []
lowercase_ = []
def __init__( self : Tuple , _lowerCAmelCase : Union[str, Any]=None , _lowerCAmelCase : Optional[Any]=None , _lowerCAmelCase : Union[str, Any]="<s>" , _lowerCAmelCase : Tuple="</s>" , _lowerCAmelCase : Any="</s>" , _lowerCAmelCase : Union[str, Any]="<s>" , _lowerCAmelCase : Dict="<unk>" , _lowerCAmelCase : int="<pad>" , _lowerCAmelCase : Optional[int]="<mask>" , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Any=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Union[str, Any]=False , **_lowerCAmelCase : List[str] , ):
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE_ = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else mask_token
SCREAMING_SNAKE_CASE_ = legacy_behaviour
super().__init__(
vocab_file=_lowerCAmelCase , tokenizer_file=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , src_lang=_lowerCAmelCase , tgt_lang=_lowerCAmelCase , additional_special_tokens=_lowerCAmelCase , legacy_behaviour=_lowerCAmelCase , **_lowerCAmelCase , )
SCREAMING_SNAKE_CASE_ = vocab_file
SCREAMING_SNAKE_CASE_ = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE_ = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
SCREAMING_SNAKE_CASE_ = {
lang_code: self.convert_tokens_to_ids(_lowerCAmelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE_ = src_lang if src_lang is not None else 'eng_Latn'
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE_ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def lowerCAmelCase_ ( self : Tuple ):
return self._src_lang
@src_lang.setter
def lowerCAmelCase_ ( self : Dict , _lowerCAmelCase : str ):
SCREAMING_SNAKE_CASE_ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowerCAmelCase_ ( self : Any , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE_ = [self.sep_token_id]
SCREAMING_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 + sep + token_ids_a + sep ) * [0]
def lowerCAmelCase_ ( self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] , _lowerCAmelCase : Optional[str] , **_lowerCAmelCase : Optional[Any] ):
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
SCREAMING_SNAKE_CASE_ = src_lang
SCREAMING_SNAKE_CASE_ = self(_lowerCAmelCase , add_special_tokens=_lowerCAmelCase , return_tensors=_lowerCAmelCase , **_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tgt_lang_id
return inputs
def lowerCAmelCase_ ( self : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : str = "eng_Latn" , _lowerCAmelCase : Optional[List[str]] = None , _lowerCAmelCase : str = "fra_Latn" , **_lowerCAmelCase : List[str] , ):
SCREAMING_SNAKE_CASE_ = src_lang
SCREAMING_SNAKE_CASE_ = tgt_lang
return super().prepare_seqaseq_batch(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
return self.set_src_lang_special_tokens(self.src_lang )
def lowerCAmelCase_ ( self : Any ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowerCAmelCase_ ( self : Dict , _lowerCAmelCase : List[Any] ):
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(_lowerCAmelCase )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE_ = [self.cur_lang_code]
SCREAMING_SNAKE_CASE_ = [self.eos_token_id]
SCREAMING_SNAKE_CASE_ = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE_ = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE_ = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowerCAmelCase_ ( self : Optional[Any] , _lowerCAmelCase : str ):
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(_lowerCAmelCase )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE_ = [self.cur_lang_code]
SCREAMING_SNAKE_CASE_ = [self.eos_token_id]
SCREAMING_SNAKE_CASE_ = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE_ = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE_ = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowerCAmelCase_ ( self : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ):
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
SCREAMING_SNAKE_CASE_ = 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,) | 31 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , _snake_case=10_00 , ):
"""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
lowerCAmelCase = range_bbox
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase = bbox[i, j, 3]
lowerCAmelCase = bbox[i, j, 1]
lowerCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase = bbox[i, j, 2]
lowerCAmelCase = bbox[i, j, 0]
lowerCAmelCase = t
lowerCAmelCase = tf.convert_to_tensor(_snake_case )
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 = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForMaskedLM(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForSequenceClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForTokenClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
snake_case__ = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ = False
snake_case__ = True
snake_case__ = 1_0
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = TFLayoutLMModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@unittest.skip('Onnx compliancy broke with TF 2.10' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def _SCREAMING_SNAKE_CASE ():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowerCAmelCase = tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
lowerCAmelCase = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the sequence output on [0, :3, :3]
lowerCAmelCase = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , _snake_case , atol=1E-3 ) )
# test the pooled output on [1, :3]
lowerCAmelCase = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , _snake_case , atol=1E-3 ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=2 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
lowerCAmelCase = outputs.loss
lowerCAmelCase = (2,)
self.assertEqual(loss.shape , _snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = (2, 2)
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=13 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the shape of the logits
lowerCAmelCase = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , _snake_case )
self.assertEqual(outputs.end_logits.shape , _snake_case )
| 4 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
UpperCAmelCase_ = "\\n Text data.\n Second line of data."
UpperCAmelCase_ = "file"
@pytest.fixture(scope='''session''' )
def A__ ( SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_UpperCAmelCase = bytes(SCREAMING_SNAKE_CASE_ , '''utf-8''' )
with zstd.open(SCREAMING_SNAKE_CASE_ , '''wb''' ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture
def A__ ( SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[Any]:
"""simple docstring"""
with open(os.path.join(tmpfs.local_root_dir , SCREAMING_SNAKE_CASE_ ) , '''w''' ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def A__ ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any ) -> str:
"""simple docstring"""
_UpperCAmelCase = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_UpperCAmelCase = input_paths[compression_format]
_UpperCAmelCase = tmp_path / '''cache'''
_UpperCAmelCase = DownloadConfig(cache_dir=SCREAMING_SNAKE_CASE_ , extract_compressed_file=SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = cached_path(SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ ) as f:
_UpperCAmelCase = f.read()
with open(SCREAMING_SNAKE_CASE_ ) as f:
_UpperCAmelCase = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def A__ ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = '''custom_cache'''
_UpperCAmelCase = '''custom_extracted_dir'''
_UpperCAmelCase = tmp_path / '''custom_extracted_path'''
if default_extracted:
_UpperCAmelCase = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , SCREAMING_SNAKE_CASE_ )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(SCREAMING_SNAKE_CASE_ ) )
_UpperCAmelCase = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_UpperCAmelCase = xz_file
_UpperCAmelCase = (
DownloadConfig(extract_compressed_file=SCREAMING_SNAKE_CASE_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=SCREAMING_SNAKE_CASE_ )
)
_UpperCAmelCase = cached_path(SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
assert Path(SCREAMING_SNAKE_CASE_ ).parent.parts[-2:] == expected
def A__ ( SCREAMING_SNAKE_CASE_ : Any ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = str(Path(SCREAMING_SNAKE_CASE_ ).resolve() )
assert cached_path(SCREAMING_SNAKE_CASE_ ) == text_file
# relative path
_UpperCAmelCase = str(Path(SCREAMING_SNAKE_CASE_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(SCREAMING_SNAKE_CASE_ ) == text_file
def A__ ( SCREAMING_SNAKE_CASE_ : List[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path(SCREAMING_SNAKE_CASE_ )
# relative path
_UpperCAmelCase = '''./__missing_file__.txt'''
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path(SCREAMING_SNAKE_CASE_ )
def A__ ( SCREAMING_SNAKE_CASE_ : List[Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase = get_from_cache(F'''tmp://{tmpfs_file}''' )
with open(SCREAMING_SNAKE_CASE_ ) as f:
_UpperCAmelCase = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , SCREAMING_SNAKE_CASE_ )
def A__ ( ) -> Any:
"""simple docstring"""
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , SCREAMING_SNAKE_CASE_ )
def A__ ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_get('''https://huggingface.co''' , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , SCREAMING_SNAKE_CASE_ )
def A__ ( SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
ftp_get('''ftp://huggingface.co''' , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , SCREAMING_SNAKE_CASE_ )
def A__ ( SCREAMING_SNAKE_CASE_ : Tuple ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
fsspec_get('''s3://huggingface.co''' , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
fsspec_head('''s3://huggingface.co''' ) | 32 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__UpperCamelCase : Union[str, Any] = '''examples/'''
__UpperCamelCase : str = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
'''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
__UpperCamelCase : List[str] = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__UpperCamelCase : Optional[int] = '''README.md'''
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : Any ):
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase ,lowerCAmelCase = REPLACE_PATTERNS[pattern]
lowerCAmelCase = replace.replace('VERSION' , _UpperCAmelCase )
lowerCAmelCase = re_pattern.sub(_UpperCAmelCase , _UpperCAmelCase )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, Any] ):
for folder, directories, fnames in os.walk(_UpperCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase , pattern='examples' )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if not patch:
update_version_in_examples(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = '🤗 Transformers currently provides the following architectures'
lowerCAmelCase = '1. Want to contribute a new model?'
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.readlines()
# Find the start of the list.
lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
lowerCAmelCase = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
with open(REPLACE_FILES['init'] , 'r' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase = REPLACE_PATTERNS['init'][0].search(_UpperCAmelCase ).groups()[0]
return packaging.version.parse(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple=False ):
lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
lowerCAmelCase = default_version.base_version
elif patch:
lowerCAmelCase = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
lowerCAmelCase = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
lowerCAmelCase = input(F'Which version are you releasing? [{default_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = default_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase , patch=_UpperCAmelCase )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = get_version()
lowerCAmelCase = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
lowerCAmelCase = input(F'Which version are we developing now? [{dev_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = dev_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 4 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : int = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""JukeboxPriorConfig""",
"""JukeboxVQVAEConfig""",
],
"""tokenization_jukebox""": ["""JukeboxTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : Dict = [
"""JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""JukeboxModel""",
"""JukeboxPreTrainedModel""",
"""JukeboxVQVAE""",
"""JukeboxPrior""",
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
lowerCamelCase__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 33 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
__UpperCamelCase : Optional[int] = pytest.mark.integration
@require_faiss
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(_snake_case ) for x in np.arange(30 ).tolist()]} )
return dset
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
lowerCAmelCase = dset.map(
lambda _snake_case , _snake_case : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=_snake_case , keep_in_memory=_snake_case )
lowerCAmelCase = dset.add_faiss_index('vecs' , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT , )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_snake_case ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(_snake_case , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
lowerCAmelCase = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCAmelCase = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
lowerCAmelCase = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=_snake_case )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertRaises(_snake_case , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
lowerCAmelCase = np.eye(5 , dtype=np.floataa )[::-1]
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case )
self.assertRaises(_snake_case , index.search_batch , queries[0] )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , _snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
lowerCAmelCase = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(_snake_case ):
lowerCAmelCase = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = faiss.IndexFlat(5 )
lowerCAmelCase = FaissIndex(custom_index=_snake_case )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_snake_case ) as tmp_file:
index.save(tmp_file.name )
lowerCAmelCase = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Dict ):
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
lowerCAmelCase = 'index.faiss'
lowerCAmelCase = F'mock://{index_name}'
index.save(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCAmelCase = FaissIndex.load(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCAmelCase = Elasticsearch()
lowerCAmelCase = {'acknowledged': True}
lowerCAmelCase = ElasticSearchIndex(es_client=_snake_case )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
lowerCAmelCase = 'foo'
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
lowerCAmelCase = 'foo'
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
lowerCAmelCase = ['foo', 'bar', 'foobar']
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , _snake_case )
# batched queries with timeout
lowerCAmelCase = ['foo', 'bar', 'foobar']
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case , request_timeout=30 )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , _snake_case )
| 4 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
SCREAMING_SNAKE_CASE_ = {
'tokenizer_file': {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
},
}
SCREAMING_SNAKE_CASE_ = {
'gpt-neox-20b': 2048,
}
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = ['''input_ids''', '''attention_mask''']
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_="<|endoftext|>" , lowerCamelCase_="<|endoftext|>" , lowerCamelCase_="<|endoftext|>" , lowerCamelCase_=False , **lowerCamelCase_ , ) -> List[str]:
super().__init__(
lowerCamelCase_ , lowerCamelCase_ , tokenizer_file=lowerCamelCase_ , unk_token=lowerCamelCase_ , bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , add_prefix_space=lowerCamelCase_ , **lowerCamelCase_ , )
UpperCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get('''add_prefix_space''' , lowerCamelCase_) != add_prefix_space:
UpperCamelCase = getattr(lowerCamelCase_ , pre_tok_state.pop('''type'''))
UpperCamelCase = add_prefix_space
UpperCamelCase = pre_tok_class(**lowerCamelCase_)
UpperCamelCase = add_prefix_space
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = None) -> Tuple[str]:
UpperCamelCase = self._tokenizer.model.save(lowerCamelCase_ , name=lowerCamelCase_)
return tuple(lowerCamelCase_)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> List[int]:
UpperCamelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_) + [self.eos_token_id])
if len(lowerCamelCase_) > self.model_max_length:
UpperCamelCase = input_ids[-self.model_max_length :]
return input_ids | 34 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = IFInpaintingPipeline
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._get_dummy_components()
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_save_load_local()
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 4 | 0 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
SCREAMING_SNAKE_CASE__ : int = parser.add_subparsers(help='''diffusers-cli command helpers''' )
# Register commands
EnvironmentCommand.register_subcommand(A__ )
# Let's go
SCREAMING_SNAKE_CASE__ : Any = parser.parse_args()
if not hasattr(A__ , '''func''' ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE__ : List[str] = args.func(A__ )
service.run()
if __name__ == "__main__":
main()
| 35 |
"""simple docstring"""
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 a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
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
lowerCAmelCase = self.vocab_size - 1
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
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 = 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 , )
lowerCAmelCase = 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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , head_mask=_snake_case )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTDoubleHeadsModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = OpenAIGPTForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class a ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
snake_case__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
snake_case__ = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case=False ):
"""simple docstring"""
lowerCAmelCase = super()._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_snake_case )
return inputs_dict
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , n_embd=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = OpenAIGPTModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@require_torch
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(_snake_case )
lowerCAmelCase = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=_snake_case ) # the president is
lowerCAmelCase = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowerCAmelCase = model.generate(_snake_case , do_sample=_snake_case )
self.assertListEqual(output_ids[0].tolist() , _snake_case )
| 4 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : torch.FloatTensor
__lowerCamelCase : torch.FloatTensor
class _A ( snake_case , snake_case ):
'''simple docstring'''
__lowerCamelCase : List[str] = 1
@register_to_config
def __init__( self ,SCREAMING_SNAKE_CASE_ = 2000 ,SCREAMING_SNAKE_CASE_ = 0.15 ,SCREAMING_SNAKE_CASE_ = 0.01 ,SCREAMING_SNAKE_CASE_ = 13_48.0 ,SCREAMING_SNAKE_CASE_ = 1E-5 ,SCREAMING_SNAKE_CASE_ = 1 ,):
'''simple docstring'''
# standard deviation of the initial noise distribution
snake_case : Union[str, Any] = sigma_max
# setable values
snake_case : Any = None
self.set_sigmas(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
return sample
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : Any = sampling_eps if sampling_eps is not None else self.config.sampling_eps
snake_case : str = torch.linspace(1 ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = None ):
'''simple docstring'''
snake_case : List[Any] = sigma_min if sigma_min is not None else self.config.sigma_min
snake_case : str = sigma_max if sigma_max is not None else self.config.sigma_max
snake_case : Any = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
snake_case : str = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
snake_case : List[Any] = torch.exp(torch.linspace(math.log(SCREAMING_SNAKE_CASE_ ) ,math.log(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ) )
snake_case : Optional[Any] = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return torch.where(
timesteps == 0 ,torch.zeros_like(t.to(timesteps.device ) ) ,self.discrete_sigmas[timesteps - 1].to(timesteps.device ) ,)
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = True ,):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
snake_case : Optional[Any] = timestep * torch.ones(
sample.shape[0] ,device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
snake_case : List[str] = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
snake_case : Dict = timesteps.to(self.discrete_sigmas.device )
snake_case : Any = self.discrete_sigmas[timesteps].to(sample.device )
snake_case : str = self.get_adjacent_sigma(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ).to(sample.device )
snake_case : Tuple = torch.zeros_like(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
snake_case : Tuple = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
snake_case : Dict = diffusion.unsqueeze(-1 )
snake_case : List[Any] = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
snake_case : Tuple = randn_tensor(
sample.shape ,layout=sample.layout ,generator=SCREAMING_SNAKE_CASE_ ,device=sample.device ,dtype=sample.dtype )
snake_case : Tuple = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
snake_case : List[str] = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_ ,prev_sample_mean=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = None ,SCREAMING_SNAKE_CASE_ = True ,):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
snake_case : Dict = randn_tensor(sample.shape ,layout=sample.layout ,generator=SCREAMING_SNAKE_CASE_ ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
snake_case : List[str] = torch.norm(model_output.reshape(model_output.shape[0] ,-1 ) ,dim=-1 ).mean()
snake_case : List[Any] = torch.norm(noise.reshape(noise.shape[0] ,-1 ) ,dim=-1 ).mean()
snake_case : str = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
snake_case : int = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
snake_case : Optional[int] = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
snake_case : Union[str, Any] = step_size.unsqueeze(-1 )
snake_case : Optional[Any] = sample + step_size * model_output
snake_case : Any = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,):
'''simple docstring'''
# Make sure sigmas and timesteps have the same device and dtype as original_samples
snake_case : Optional[Any] = timesteps.to(original_samples.device )
snake_case : Any = self.discrete_sigmas.to(original_samples.device )[timesteps]
snake_case : Tuple = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(SCREAMING_SNAKE_CASE_ ) * sigmas[:, None, None, None]
)
snake_case : Optional[Any] = noise + original_samples
return noisy_samples
def __len__( self ):
'''simple docstring'''
return self.config.num_train_timesteps
| 36 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase : str = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=_UpperCAmelCase , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=_UpperCAmelCase , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=_UpperCAmelCase , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=_UpperCAmelCase , default='data/dump' , help='The dump file prefix.' )
lowerCAmelCase = parser.parse_args()
logger.info(F'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
lowerCAmelCase = BertTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
lowerCAmelCase = RobertaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `<s>`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
lowerCAmelCase = GPTaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
lowerCAmelCase = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(F'Loading text from {args.file_path}' )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
lowerCAmelCase = fp.readlines()
logger.info('Start encoding' )
logger.info(F'{len(_UpperCAmelCase )} examples to process.' )
lowerCAmelCase = []
lowerCAmelCase = 0
lowerCAmelCase = 1_0000
lowerCAmelCase = time.time()
for text in data:
lowerCAmelCase = F'{bos} {text.strip()} {sep}'
lowerCAmelCase = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
rslt.append(_UpperCAmelCase )
iter += 1
if iter % interval == 0:
lowerCAmelCase = time.time()
logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
lowerCAmelCase = time.time()
logger.info('Finished binarization' )
logger.info(F'{len(_UpperCAmelCase )} examples processed.' )
lowerCAmelCase = F'{args.dump_file}.{args.tokenizer_name}.pickle'
lowerCAmelCase = tokenizer.vocab_size
if vocab_size < (1 << 16):
lowerCAmelCase = [np.uintaa(_UpperCAmelCase ) for d in rslt]
else:
lowerCAmelCase = [np.intaa(_UpperCAmelCase ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F'Dump to {dp_file}' )
with open(_UpperCAmelCase , 'wb' ) as handle:
pickle.dump(rslt_ , _UpperCAmelCase , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 4 | 0 |
def UpperCamelCase_ ( __a , __a ) -> str:
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a__ : Optional[Any] = str(bin(__a ) )[2:] # remove the leading "0b"
a__ : Optional[int] = str(bin(__a ) )[2:] # remove the leading "0b"
a__ : Optional[Any] = max(len(__a ) , len(__a ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__a ) , b_binary.zfill(__a ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
'''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
'''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
),
'''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''bert'''
def __init__( self , _snake_case=3_05_22 , _snake_case=7_68 , _snake_case=12 , _snake_case=12 , _snake_case=30_72 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , _snake_case=None , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
lowerCAmelCase = classifier_dropout
class a ( a__ ):
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 4 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : int = 10 ) -> str:
'''simple docstring'''
if not isinstance(__magic_name__ , __magic_name__ ) or n < 0:
raise ValueError("""Invalid input""" )
snake_case__ : List[Any] = 10**n
snake_case__ : Tuple = 2_84_33 * (pow(2 , 7_83_04_57 , __magic_name__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'{solution(10) = }')
| 38 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class a ( a__ , unittest.TestCase ):
snake_case__ = DanceDiffusionPipeline
snake_case__ = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
snake_case__ = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
snake_case__ = False
snake_case__ = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=5_12 , sample_rate=1_60_00 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_snake_case , use_timestep_embedding=_snake_case , time_embedding_type='fourier' , mid_block_type='UNetMidBlock1D' , down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') , up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') , )
lowerCAmelCase = IPNDMScheduler()
lowerCAmelCase = {
'unet': unet,
'scheduler': scheduler,
}
return components
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 4,
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = DanceDiffusionPipeline(**_snake_case )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = self.get_dummy_inputs(_snake_case )
lowerCAmelCase = pipe(**_snake_case )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
lowerCAmelCase = np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_attention_slicing_forward_pass()
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = torch_device
lowerCAmelCase = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(generator=_snake_case , num_inference_steps=1_00 , audio_length_in_s=4.096 )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase = np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = torch_device
lowerCAmelCase = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' , torch_dtype=torch.floataa )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(generator=_snake_case , num_inference_steps=1_00 , audio_length_in_s=4.096 )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase = np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 4 | 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 __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = filter(lambda SCREAMING_SNAKE_CASE__ : p.requires_grad , model.parameters() )
snake_case_ = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowerCAmelCase_ = logging.getLogger(__name__)
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if metric == "rouge2":
snake_case_ = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
snake_case_ = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
snake_case_ = '''{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.''' )
snake_case_ = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE__ , filename=SCREAMING_SNAKE_CASE__ , monitor=F'''val_{metric}''' , mode='''max''' , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return EarlyStopping(
monitor=F'''val_{metric}''' , mode='''min''' if '''loss''' in metric else '''max''' , patience=SCREAMING_SNAKE_CASE__ , verbose=SCREAMING_SNAKE_CASE__ , )
class snake_case_ ( pl.Callback ):
'''simple docstring'''
def snake_case__( self : Dict , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[str] ) ->List[Any]:
snake_case_ = {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_UpperCamelCase )
@rank_zero_only
def snake_case__( self : Union[str, Any] , _UpperCamelCase : pl.Trainer , _UpperCamelCase : pl.LightningModule , _UpperCamelCase : str , _UpperCamelCase : List[str]=True ) ->None:
logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
snake_case_ = 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
snake_case_ = Path(pl_module.hparams.output_dir )
if type_path == "test":
snake_case_ = od / '''test_results.txt'''
snake_case_ = 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.
snake_case_ = od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
snake_case_ = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=_UpperCamelCase )
generations_file.parent.mkdir(exist_ok=_UpperCamelCase )
with open(_UpperCamelCase , '''a+''' ) as writer:
for key in sorted(_UpperCamelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
snake_case_ = metrics[key]
if isinstance(_UpperCamelCase , torch.Tensor ):
snake_case_ = val.item()
snake_case_ = f'''{key}: {val:.6f}\n'''
writer.write(_UpperCamelCase )
if not save_generations:
return
if "preds" in metrics:
snake_case_ = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(_UpperCamelCase )
@rank_zero_only
def snake_case__( self : List[str] , _UpperCamelCase : int , _UpperCamelCase : str ) ->Optional[Any]:
try:
snake_case_ = pl_module.model.model.num_parameters()
except AttributeError:
snake_case_ = pl_module.model.num_parameters()
snake_case_ = count_trainable_parameters(_UpperCamelCase )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6} )
@rank_zero_only
def snake_case__( self : Tuple , _UpperCamelCase : pl.Trainer , _UpperCamelCase : pl.LightningModule ) ->str:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_UpperCamelCase , _UpperCamelCase , '''test''' )
@rank_zero_only
def snake_case__( self : Tuple , _UpperCamelCase : pl.Trainer , _UpperCamelCase : Tuple ) ->Dict:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid") | 39 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=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 UpperCamelCase__ ( 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 UpperCamelCase__ ( 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=_snake_case , initializer_range=self.initializer_range , use_stable_embedding=_snake_case , )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = OpenLlamaModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , )
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , )
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
# first forward pass
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , use_cache=_snake_case , )
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(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , output_hidden_states=_snake_case , )['hidden_states'][0]
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , past_key_values=_snake_case , output_hidden_states=_snake_case , )['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(_snake_case , _snake_case , atol=1E-3 ) )
def UpperCamelCase__ ( 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 ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
snake_case__ = (OpenLlamaForCausalLM,) if is_torch_available() else ()
snake_case__ = (
{
'''feature-extraction''': OpenLlamaModel,
'''text-classification''': OpenLlamaForSequenceClassification,
'''text-generation''': OpenLlamaForCausalLM,
'''zero-shot''': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case__ = False
snake_case__ = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( 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(*_snake_case )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
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 UpperCamelCase__ ( self ):
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase ,lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = ids_tensor([1, 10] , config.vocab_size )
lowerCAmelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase = OpenLlamaModel(_snake_case )
original_model.to(_snake_case )
original_model.eval()
lowerCAmelCase = original_model(_snake_case ).last_hidden_state
lowerCAmelCase = original_model(_snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase = {'type': scaling_type, 'factor': 10.0}
lowerCAmelCase = OpenLlamaModel(_snake_case )
scaled_model.to(_snake_case )
scaled_model.eval()
lowerCAmelCase = scaled_model(_snake_case ).last_hidden_state
lowerCAmelCase = scaled_model(_snake_case ).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(_snake_case , _snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1E-5 ) )
| 4 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : List[str] = "decision_transformer"
UpperCAmelCase__ : Union[str, Any] = ["past_key_values"]
UpperCAmelCase__ : Optional[int] = {
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self, SCREAMING_SNAKE_CASE_=17, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=128, SCREAMING_SNAKE_CASE_=4096, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=1024, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_="relu", SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=1e-5, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=5_0256, SCREAMING_SNAKE_CASE_=5_0256, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=False, **SCREAMING_SNAKE_CASE_, ) -> str:
UpperCamelCase : Optional[int] = state_dim
UpperCamelCase : Optional[int] = act_dim
UpperCamelCase : Tuple = hidden_size
UpperCamelCase : int = max_ep_len
UpperCamelCase : List[str] = action_tanh
UpperCamelCase : Dict = vocab_size
UpperCamelCase : Optional[int] = n_positions
UpperCamelCase : List[str] = n_layer
UpperCamelCase : Optional[int] = n_head
UpperCamelCase : Optional[Any] = n_inner
UpperCamelCase : Any = activation_function
UpperCamelCase : Any = resid_pdrop
UpperCamelCase : List[str] = embd_pdrop
UpperCamelCase : str = attn_pdrop
UpperCamelCase : List[str] = layer_norm_epsilon
UpperCamelCase : Tuple = initializer_range
UpperCamelCase : int = scale_attn_weights
UpperCamelCase : Optional[int] = use_cache
UpperCamelCase : Dict = scale_attn_by_inverse_layer_idx
UpperCamelCase : Tuple = reorder_and_upcast_attn
UpperCamelCase : Union[str, Any] = bos_token_id
UpperCamelCase : Optional[int] = eos_token_id
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
| 40 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = data
lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return F'Node({self.data})'
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = None
def __iter__( self ):
"""simple docstring"""
lowerCAmelCase = self.head
while node:
yield node.data
lowerCAmelCase = node.next
def __len__( self ):
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self ):
"""simple docstring"""
return "->".join([str(_snake_case ) for item in self] )
def __getitem__( self , _snake_case ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , _snake_case , _snake_case ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
lowerCAmelCase = self.head
for _ in range(_snake_case ):
lowerCAmelCase = current.next
lowerCAmelCase = data
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
self.insert_nth(len(self ) , _snake_case )
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
self.insert_nth(0 , _snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
if not 0 <= index <= len(self ):
raise IndexError('list index out of range' )
lowerCAmelCase = Node(_snake_case )
if self.head is None:
lowerCAmelCase = new_node
elif index == 0:
lowerCAmelCase = self.head # link new_node to head
lowerCAmelCase = new_node
else:
lowerCAmelCase = self.head
for _ in range(index - 1 ):
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next
lowerCAmelCase = new_node
def UpperCamelCase__ ( self ): # print every node data
"""simple docstring"""
print(self )
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.delete_nth(0 )
def UpperCamelCase__ ( self ): # delete from tail
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def UpperCamelCase__ ( self , _snake_case = 0 ):
"""simple docstring"""
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError('List index out of range.' )
lowerCAmelCase = self.head # default first node
if index == 0:
lowerCAmelCase = self.head.next
else:
lowerCAmelCase = self.head
for _ in range(index - 1 ):
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next.next
return delete_node.data
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.head is None
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = None
lowerCAmelCase = self.head
while current:
# Store the current node's next node.
lowerCAmelCase = current.next
# Make the current node's next point backwards
lowerCAmelCase = prev
# Make the previous node be the current node
lowerCAmelCase = current
# Make the current node the next node (to progress iteration)
lowerCAmelCase = next_node
# Return prev in order to put the head at the end
lowerCAmelCase = prev
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = LinkedList()
assert linked_list.is_empty() is True
assert str(_UpperCAmelCase ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(_UpperCAmelCase ) == i
linked_list.insert_nth(_UpperCAmelCase , i + 1 )
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(_UpperCAmelCase ) == 9
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
lowerCAmelCase = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(-8 , 1 ) )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = [
-9,
100,
Node(7734_5112 ),
'dlrow olleH',
7,
5555,
0,
-192.5_5555,
'Hello, world!',
77.9,
Node(10 ),
None,
None,
12.20,
]
lowerCAmelCase = LinkedList()
for i in test_input:
linked_list.insert_tail(_UpperCAmelCase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(_UpperCAmelCase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
lowerCAmelCase = linked_list.delete_head()
assert result == -9
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
lowerCAmelCase = linked_list.delete_tail()
assert result == 12.2
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
lowerCAmelCase = linked_list.delete_nth(10 )
assert result is None
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node('Hello again, world!' ) )
assert (
str(_UpperCAmelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(_UpperCAmelCase )
assert (
str(_UpperCAmelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(_UpperCAmelCase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def _SCREAMING_SNAKE_CASE ():
from doctest import testmod
testmod()
lowerCAmelCase = LinkedList()
linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
print('\nDelete head' )
linked_list.delete_head()
print('Delete tail' )
linked_list.delete_tail()
print('\nPrint list:' )
linked_list.print_list()
print('\nReverse linked list' )
linked_list.reverse()
print('\nPrint list:' )
linked_list.print_list()
print('\nString representation of linked list:' )
print(_UpperCAmelCase )
print('\nReading/changing Node data using indexing:' )
print(F'Element at Position 1: {linked_list[1]}' )
lowerCAmelCase = input('Enter New Value: ' ).strip()
print('New list:' )
print(_UpperCAmelCase )
print(F'length of linked_list is : {len(_UpperCAmelCase )}' )
if __name__ == "__main__":
main()
| 4 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 41 |
"""simple docstring"""
from __future__ import annotations
import requests
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(_UpperCAmelCase ).json()
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 10 ):
lowerCAmelCase = 'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
lowerCAmelCase = requests.get(_UpperCAmelCase ).json()[:max_stories]
return [get_hackernews_story(_UpperCAmelCase ) for story_id in story_ids]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 10 ):
lowerCAmelCase = hackernews_top_stories(_UpperCAmelCase )
return "\n".join('* [{title}]({url})'.format(**_UpperCAmelCase ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 42 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any ):
lowerCAmelCase = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCAmelCase = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
lowerCAmelCase = 4
lowerCAmelCase = 48
lowerCAmelCase = 'pixelshuffle_aux'
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCAmelCase = [6, 6, 6, 6]
lowerCAmelCase = 60
lowerCAmelCase = [6, 6, 6, 6]
lowerCAmelCase = 'pixelshuffledirect'
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCAmelCase = 4
lowerCAmelCase = 'nearest+conv'
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
lowerCAmelCase = 1
lowerCAmelCase = 1
lowerCAmelCase = 126
lowerCAmelCase = 7
lowerCAmelCase = 255.0
lowerCAmelCase = ''
return config
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] ):
if "patch_embed.proj" in name and "layers" not in name:
lowerCAmelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
lowerCAmelCase = name.replace('patch_embed.norm' , 'embeddings.patch_embeddings.layernorm' )
if "layers" in name:
lowerCAmelCase = name.replace('layers' , 'encoder.stages' )
if "residual_group.blocks" in name:
lowerCAmelCase = name.replace('residual_group.blocks' , 'layers' )
if "attn.proj" in name:
lowerCAmelCase = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
lowerCAmelCase = name.replace('attn' , 'attention.self' )
if "norm1" in name:
lowerCAmelCase = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowerCAmelCase = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowerCAmelCase = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCAmelCase = name.replace('mlp.fc2' , 'output.dense' )
if "q_bias" in name:
lowerCAmelCase = name.replace('q_bias' , 'query.bias' )
if "k_bias" in name:
lowerCAmelCase = name.replace('k_bias' , 'key.bias' )
if "v_bias" in name:
lowerCAmelCase = name.replace('v_bias' , 'value.bias' )
if "cpb_mlp" in name:
lowerCAmelCase = name.replace('cpb_mlp' , 'continuous_position_bias_mlp' )
if "patch_embed.proj" in name:
lowerCAmelCase = name.replace('patch_embed.proj' , 'patch_embed.projection' )
if name == "norm.weight":
lowerCAmelCase = 'layernorm.weight'
if name == "norm.bias":
lowerCAmelCase = 'layernorm.bias'
if "conv_first" in name:
lowerCAmelCase = name.replace('conv_first' , 'first_convolution' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
lowerCAmelCase = name.replace('conv_last' , 'final_convolution' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
lowerCAmelCase = name.replace('conv_before_upsample.0' , 'conv_before_upsample' )
if "upsample.0" in name:
lowerCAmelCase = name.replace('upsample.0' , 'upsample.convolution_0' )
if "upsample.2" in name:
lowerCAmelCase = name.replace('upsample.2' , 'upsample.convolution_1' )
lowerCAmelCase = 'upsample.' + name
elif config.upsampler == "pixelshuffledirect":
lowerCAmelCase = name.replace('upsample.0.weight' , 'upsample.conv.weight' )
lowerCAmelCase = name.replace('upsample.0.bias' , 'upsample.conv.bias' )
else:
pass
else:
lowerCAmelCase = 'swin2sr.' + name
return name
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict ):
for key in orig_state_dict.copy().keys():
lowerCAmelCase = orig_state_dict.pop(_UpperCAmelCase )
if "qkv" in key:
lowerCAmelCase = key.split('.' )
lowerCAmelCase = int(key_split[1] )
lowerCAmelCase = int(key_split[4] )
lowerCAmelCase = config.embed_dim
if "weight" in key:
lowerCAmelCase = val[:dim, :]
lowerCAmelCase = val[dim : dim * 2, :]
lowerCAmelCase = val[-dim:, :]
else:
lowerCAmelCase = val[:dim]
lowerCAmelCase = val[dim : dim * 2]
lowerCAmelCase = val[-dim:]
pass
else:
lowerCAmelCase = val
return orig_state_dict
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple ):
lowerCAmelCase = get_config(_UpperCAmelCase )
lowerCAmelCase = SwinaSRForImageSuperResolution(_UpperCAmelCase )
model.eval()
lowerCAmelCase = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location='cpu' )
lowerCAmelCase = convert_state_dict(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase ,lowerCAmelCase = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
raise ValueError('Missing keys when converting: {}'.format(_UpperCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F'Unexpected key {key} in state_dict' )
# verify values
lowerCAmelCase = 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
lowerCAmelCase = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('RGB' )
lowerCAmelCase = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
lowerCAmelCase = 126 if 'Jpeg' in checkpoint_url else 256
lowerCAmelCase = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
lowerCAmelCase = transforms(_UpperCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
lowerCAmelCase = pixel_values[:, 0, :, :].unsqueeze(1 )
lowerCAmelCase = model(_UpperCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 512, 512] )
lowerCAmelCase = torch.tensor(
[[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 512, 512] )
lowerCAmelCase = torch.tensor(
[[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] )
assert (
outputs.reconstruction.shape == expected_shape
), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , _UpperCAmelCase , atol=1e-3 )
print('Looks ok!' )
lowerCAmelCase = {
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
'swin2SR-classical-sr-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
'swin2SR-classical-sr-x4-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
'swin2SR-compressed-sr-x4-48'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
'swin2SR-lightweight-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
),
}
lowerCAmelCase = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_UpperCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
model.push_to_hub(F'caidas/{model_name}' )
processor.push_to_hub(F'caidas/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
type=str,
help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 4 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class _a ( UpperCamelCase__ ):
_lowercase : Any = '''big_bird'''
def __init__( self: List[str] , UpperCamelCase_: List[Any]=50_358 , UpperCamelCase_: Dict=768 , UpperCamelCase_: Tuple=12 , UpperCamelCase_: Union[str, Any]=12 , UpperCamelCase_: Optional[int]=3_072 , UpperCamelCase_: Tuple="gelu_new" , UpperCamelCase_: List[str]=0.1 , UpperCamelCase_: Any=0.1 , UpperCamelCase_: Optional[int]=4_096 , UpperCamelCase_: List[Any]=2 , UpperCamelCase_: Tuple=0.02 , UpperCamelCase_: int=1E-1_2 , UpperCamelCase_: Dict=True , UpperCamelCase_: Tuple=0 , UpperCamelCase_: Any=1 , UpperCamelCase_: Tuple=2 , UpperCamelCase_: List[str]=66 , UpperCamelCase_: Any="block_sparse" , UpperCamelCase_: int=True , UpperCamelCase_: Dict=False , UpperCamelCase_: List[Any]=64 , UpperCamelCase_: Union[str, Any]=3 , UpperCamelCase_: Optional[int]=None , **UpperCamelCase_: Union[str, Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(
pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , sep_token_id=UpperCamelCase_ , **UpperCamelCase_ , )
lowercase__ = vocab_size
lowercase__ = max_position_embeddings
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = initializer_range
lowercase__ = type_vocab_size
lowercase__ = layer_norm_eps
lowercase__ = use_cache
lowercase__ = rescale_embeddings
lowercase__ = attention_type
lowercase__ = use_bias
lowercase__ = block_size
lowercase__ = num_random_blocks
lowercase__ = classifier_dropout
class _a ( UpperCamelCase__ ):
@property
def lowerCamelCase_ ( self: str ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase__ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 43 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''megatron-bert'''
def __init__( self , _snake_case=2_90_56 , _snake_case=10_24 , _snake_case=24 , _snake_case=16 , _snake_case=40_96 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
| 4 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase__ :
def __init__( self : List[Any],__A : str,__A : List[str]=1_3,__A : str=3_2,__A : Tuple=2,__A : Any=3,__A : Dict=1_6,__A : Dict=[3_2, 6_4, 1_2_8],__A : List[str]=[1, 2, 1],__A : str=[2, 2, 4],__A : Optional[int]=2,__A : Dict=2.0,__A : str=True,__A : Tuple=0.0,__A : int=0.0,__A : List[str]=0.1,__A : Any="gelu",__A : List[Any]=False,__A : Optional[Any]=True,__A : List[str]=0.02,__A : Tuple=1e-5,__A : Any=True,__A : Tuple=None,__A : Tuple=True,__A : Tuple=1_0,__A : List[Any]=8,__A : Optional[int]=["stage1", "stage2"],__A : int=[1, 2],):
_lowerCamelCase : List[Any] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Optional[int] = image_size
_lowerCamelCase : int = patch_size
_lowerCamelCase : Optional[Any] = num_channels
_lowerCamelCase : int = embed_dim
_lowerCamelCase : int = hidden_sizes
_lowerCamelCase : List[Any] = depths
_lowerCamelCase : Any = num_heads
_lowerCamelCase : List[str] = window_size
_lowerCamelCase : str = mlp_ratio
_lowerCamelCase : Any = qkv_bias
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : List[str] = drop_path_rate
_lowerCamelCase : str = hidden_act
_lowerCamelCase : Union[str, Any] = use_absolute_embeddings
_lowerCamelCase : List[Any] = patch_norm
_lowerCamelCase : Tuple = layer_norm_eps
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Optional[int] = is_training
_lowerCamelCase : Tuple = scope
_lowerCamelCase : List[Any] = use_labels
_lowerCamelCase : int = type_sequence_label_size
_lowerCamelCase : Tuple = encoder_stride
_lowerCamelCase : Any = out_features
_lowerCamelCase : Any = out_indices
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = None
if self.use_labels:
_lowerCamelCase : str = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Union[str, Any] ):
return FocalNetConfig(
image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,embed_dim=self.embed_dim,hidden_sizes=self.hidden_sizes,depths=self.depths,num_heads=self.num_heads,window_size=self.window_size,mlp_ratio=self.mlp_ratio,qkv_bias=self.qkv_bias,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,drop_path_rate=self.drop_path_rate,hidden_act=self.hidden_act,use_absolute_embeddings=self.use_absolute_embeddings,path_norm=self.patch_norm,layer_norm_eps=self.layer_norm_eps,initializer_range=self.initializer_range,encoder_stride=self.encoder_stride,out_features=self.out_features,out_indices=self.out_indices,)
def lowerCamelCase_ ( self : int,__A : Union[str, Any],__A : Tuple,__A : List[Any] ):
_lowerCamelCase : Optional[Any] = FocalNetModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(__A )
_lowerCamelCase : Optional[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_lowerCamelCase : Union[str, Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, expected_seq_len, expected_dim) )
def lowerCamelCase_ ( self : int,__A : Optional[int],__A : int,__A : Optional[int] ):
_lowerCamelCase : Any = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ),len(config.out_features ) )
self.parent.assertListEqual(model.channels,config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
_lowerCamelCase : List[str] = None
_lowerCamelCase : List[str] = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ),1 )
self.parent.assertListEqual(model.channels,[config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : Optional[int],__A : Optional[int],__A : Dict,__A : Dict ):
_lowerCamelCase : List[Any] = FocalNetForMaskedImageModeling(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
self.parent.assertEqual(
result.reconstruction.shape,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_lowerCamelCase : Dict = 1
_lowerCamelCase : Any = FocalNetForMaskedImageModeling(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : Optional[int] = model(__A )
self.parent.assertEqual(result.reconstruction.shape,(self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase_ ( self : List[Any],__A : Union[str, Any],__A : List[Any],__A : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = self.type_sequence_label_size
_lowerCamelCase : Optional[Any] = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[int] = model(__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCamelCase : str = 1
_lowerCamelCase : str = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = model(__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : int = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Union[str, Any] = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[int] = FocalNetModelTester(self )
_lowerCamelCase : int = ConfigTester(self,config_class=__A,embed_dim=3_7,has_text_modality=__A )
def lowerCamelCase_ ( self : Union[str, 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 lowerCamelCase_ ( self : List[str] ):
return
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def lowerCamelCase_ ( self : Optional[int] ):
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def lowerCamelCase_ ( self : List[str] ):
pass
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase , _lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : str = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(),(nn.Module) )
_lowerCamelCase : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A,nn.Linear ) )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : Union[str, Any] = model_class(__A )
_lowerCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase : int = [*signature.parameters.keys()]
_lowerCamelCase : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1],__A )
def lowerCamelCase_ ( self : Tuple,__A : Any,__A : List[Any],__A : str,__A : Any ):
_lowerCamelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
_lowerCamelCase : Optional[int] = model(**self._prepare_for_class(__A,__A ) )
_lowerCamelCase : Optional[int] = outputs.hidden_states
_lowerCamelCase : int = getattr(
self.model_tester,"expected_num_hidden_layers",len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__A ),__A )
# FocalNet has a different seq_length
_lowerCamelCase : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : List[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
_lowerCamelCase : Any = outputs.reshaped_hidden_states
self.assertEqual(len(__A ),__A )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Tuple = reshaped_hidden_states[0].shape
_lowerCamelCase : List[str] = (
reshaped_hidden_states[0].view(__A,__A,height * width ).permute(0,2,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase , _lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase , _lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Tuple = 3
_lowerCamelCase : Optional[int] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_lowerCamelCase : Tuple = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : Any = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_lowerCamelCase : int = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : Optional[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
@slow
def lowerCamelCase_ ( self : Tuple ):
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = FocalNetModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase , _lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = _config_zero_init(__A )
for model_class in self.all_model_classes:
_lowerCamelCase : Any = model_class(config=__A )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),[0.0, 1.0],msg=f'Parameter {name} of model {model_class} seems not properly initialized',)
@require_vision
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : Union[str, Any] ):
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(__A )
_lowerCamelCase : int = self.default_image_processor
_lowerCamelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_lowerCamelCase : Dict = image_processor(images=__A,return_tensors="pt" ).to(__A )
# forward pass
with torch.no_grad():
_lowerCamelCase : Dict = model(**__A )
# verify the logits
_lowerCamelCase : List[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape,__A )
_lowerCamelCase : List[str] = torch.tensor([0.2166, -0.4368, 0.2191] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3],__A,atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item(),2_8_1 )
@require_torch
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = (FocalNetBackbone,) if is_torch_available() else ()
lowerCAmelCase_ = FocalNetConfig
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = FocalNetModelTester(self ) | 44 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
def A ( lowercase__ : int ) -> str:
if number > 0:
raise ValueError("""input must be a negative integer""" )
UpperCamelCase__ :List[Any] = len(bin(lowercase__ )[3:] )
UpperCamelCase__ :str = bin(abs(lowercase__ ) - (1 << binary_number_length) )[3:]
UpperCamelCase__ :List[Any] = (
(
"""1"""
+ """0""" * (binary_number_length - len(lowercase__ ))
+ twos_complement_number
)
if number < 0
else """0"""
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod() | 45 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class a ( a__ ):
snake_case__ = 42
class a ( a__ , a__ ):
@register_to_config
def __init__( self , _snake_case = 3 , _snake_case = 3 , _snake_case = ("DownEncoderBlock2D",) , _snake_case = ("UpDecoderBlock2D",) , _snake_case = (64,) , _snake_case = 1 , _snake_case = "silu" , _snake_case = 3 , _snake_case = 32 , _snake_case = 2_56 , _snake_case = 32 , _snake_case = None , _snake_case = 0.18_215 , _snake_case = "group" , ):
"""simple docstring"""
super().__init__()
# pass init params to Encoder
lowerCAmelCase = Encoder(
in_channels=_snake_case , out_channels=_snake_case , down_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , double_z=_snake_case , )
lowerCAmelCase = vq_embed_dim if vq_embed_dim is not None else latent_channels
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
lowerCAmelCase = VectorQuantizer(_snake_case , _snake_case , beta=0.25 , remap=_snake_case , sane_index_shape=_snake_case )
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
# pass init params to Decoder
lowerCAmelCase = Decoder(
in_channels=_snake_case , out_channels=_snake_case , up_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , norm_type=_snake_case , )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = self.encoder(_snake_case )
lowerCAmelCase = self.quant_conv(_snake_case )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_snake_case )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = False , _snake_case = True ):
"""simple docstring"""
if not force_not_quantize:
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = self.quantize(_snake_case )
else:
lowerCAmelCase = h
lowerCAmelCase = self.post_quant_conv(_snake_case )
lowerCAmelCase = self.decoder(_snake_case , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = sample
lowerCAmelCase = self.encode(_snake_case ).latents
lowerCAmelCase = self.decode(_snake_case ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
| 4 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : Any = {
'''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
'''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
'''processing_whisper''': ['''WhisperProcessor'''],
'''tokenization_whisper''': ['''WhisperTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Optional[int] = ['''WhisperTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Any = [
'''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WhisperForConditionalGeneration''',
'''WhisperModel''',
'''WhisperPreTrainedModel''',
'''WhisperForAudioClassification''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Any = [
'''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWhisperForConditionalGeneration''',
'''TFWhisperModel''',
'''TFWhisperPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[str] = [
'''FlaxWhisperForConditionalGeneration''',
'''FlaxWhisperModel''',
'''FlaxWhisperPreTrainedModel''',
'''FlaxWhisperForAudioClassification''',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 46 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = vertices
lowerCAmelCase = {
(min(_snake_case ), max(_snake_case )): weight for edge, weight in edges.items()
}
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
lowerCAmelCase = weight
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Graph({min(self.vertices )} , {} )
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
lowerCAmelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
lowerCAmelCase = edge
lowerCAmelCase = weight
subgraph.add_edge(_snake_case , _snake_case )
return subgraph
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "p107_network.txt" ):
lowerCAmelCase = os.path.abspath(os.path.dirname(_UpperCAmelCase ) )
lowerCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = {}
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
with open(_UpperCAmelCase ) as f:
lowerCAmelCase = f.read().strip().split('\n' )
lowerCAmelCase = [line.split(',' ) for line in data]
for edgea in range(1 , len(_UpperCAmelCase ) ):
for edgea in range(_UpperCAmelCase ):
if adjaceny_matrix[edgea][edgea] != "-":
lowerCAmelCase = int(adjaceny_matrix[edgea][edgea] )
lowerCAmelCase = Graph(set(range(len(_UpperCAmelCase ) ) ) , _UpperCAmelCase )
lowerCAmelCase = graph.prims_algorithm()
lowerCAmelCase = sum(graph.edges.values() )
lowerCAmelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 4 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def UpperCAmelCase__ ( lowerCamelCase_ : str , lowerCamelCase_ : List[str]=1_0 ):
__a : Optional[Any] = []
for _ in range(lowerCamelCase_ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def UpperCAmelCase__ ( lowerCamelCase_ : Any , lowerCamelCase_ : Tuple=1_0 ):
__a : Union[str, Any] = []
for step in range(lowerCamelCase_ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
__a : List[Any] = os.path.join(lowerCamelCase_ , 'schedule.bin' )
torch.save(scheduler.state_dict() , lowerCamelCase_ )
__a : Tuple = torch.load(lowerCamelCase_ )
scheduler.load_state_dict(lowerCamelCase_ )
return lrs
@require_torch
class _UpperCamelCase( unittest.TestCase ):
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , len(SCREAMING_SNAKE_CASE__ ) )
for a, b in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self.assertAlmostEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , delta=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
__a : Union[str, Any] = torch.tensor([0.1, -0.2, -0.1] , requires_grad=SCREAMING_SNAKE_CASE__ )
__a : List[str] = torch.tensor([0.4, 0.2, -0.5] )
__a : List[Any] = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
__a : List[Any] = AdamW(params=[w] , lr=2e-1 , weight_decay=0.0 )
for _ in range(1_0_0 ):
__a : int = criterion(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
def __lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
__a : List[str] = torch.tensor([0.1, -0.2, -0.1] , requires_grad=SCREAMING_SNAKE_CASE__ )
__a : Union[str, Any] = torch.tensor([0.4, 0.2, -0.5] )
__a : Union[str, Any] = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
__a : int = Adafactor(
params=[w] , lr=1e-2 , eps=(1e-30, 1e-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=SCREAMING_SNAKE_CASE__ , weight_decay=0.0 , relative_step=SCREAMING_SNAKE_CASE__ , scale_parameter=SCREAMING_SNAKE_CASE__ , warmup_init=SCREAMING_SNAKE_CASE__ , )
for _ in range(1_0_0_0 ):
__a : str = criterion(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
@require_torch
class _UpperCamelCase( unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Linear(50 , 50 ) if is_torch_available() else None
__SCREAMING_SNAKE_CASE : str = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
__SCREAMING_SNAKE_CASE : str = 10
def __lowerCAmelCase ( self : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Dict=None ):
'''simple docstring'''
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , len(SCREAMING_SNAKE_CASE__ ) )
for a, b in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self.assertAlmostEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , delta=SCREAMING_SNAKE_CASE__ , msg=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Any ):
'''simple docstring'''
__a : str = {'num_warmup_steps': 2, 'num_training_steps': 1_0}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
__a : Any = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'num_warmup_steps': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, 'num_cycles': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, 'power': 2.0, 'lr_end': 1e-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{'num_warmup_steps': 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
__a , __a : str = data
__a : Tuple = scheduler_func(self.optimizer , **SCREAMING_SNAKE_CASE__ )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
__a : List[Any] = unwrap_schedule(SCREAMING_SNAKE_CASE__ , self.num_steps )
self.assertListAlmostEqual(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , tol=1e-2 , msg=f'''failed for {scheduler_func} in normal scheduler''' , )
__a : Dict = scheduler_func(self.optimizer , **SCREAMING_SNAKE_CASE__ )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(SCREAMING_SNAKE_CASE__ ) # wrap to test picklability of the schedule
__a : Optional[int] = unwrap_and_save_reload_schedule(SCREAMING_SNAKE_CASE__ , self.num_steps )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , msg=f'''failed for {scheduler_func} in save and reload''' )
class _UpperCamelCase:
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
__a : str = fn
def __call__( self : int , *SCREAMING_SNAKE_CASE__ : Dict , **SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
'''simple docstring'''
return self.fn(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@classmethod
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
__a : List[Any] = list(map(self , scheduler.lr_lambdas ) )
| 47 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase )] )
lowerCAmelCase = np.array(_UpperCAmelCase )
lowerCAmelCase = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , _UpperCAmelCase ) ) , x.transpose() ) , _UpperCAmelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = (1, 2, 1)
lowerCAmelCase = (1, 1, 0, 7)
lowerCAmelCase = SARIMAX(
_UpperCAmelCase , exog=_UpperCAmelCase , order=_UpperCAmelCase , seasonal_order=_UpperCAmelCase )
lowerCAmelCase = model.fit(disp=_UpperCAmelCase , maxiter=600 , method='nm' )
lowerCAmelCase = model_fit.predict(1 , len(_UpperCAmelCase ) , exog=[test_match] )
return result[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = SVR(kernel='rbf' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = regressor.predict(_UpperCAmelCase )
return y_pred[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list ):
train_user.sort()
lowerCAmelCase = np.percentile(_UpperCAmelCase , 25 )
lowerCAmelCase = np.percentile(_UpperCAmelCase , 75 )
lowerCAmelCase = qa - qa
lowerCAmelCase = qa - (iqr * 0.1)
return low_lim
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : float ):
lowerCAmelCase = 0
lowerCAmelCase = 0
for i in list_vote:
if i > actual_result:
lowerCAmelCase = not_safe + 1
else:
if abs(abs(_UpperCAmelCase ) - abs(_UpperCAmelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
__UpperCamelCase : Optional[Any] = [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
__UpperCamelCase : Any = pd.DataFrame(
data_input, columns=['''total_user''', '''total_even''', '''days''']
)
__UpperCamelCase : Dict = Normalizer().fit_transform(data_input_df.values)
# split data
__UpperCamelCase : Dict = normalize_df[:, 2].tolist()
__UpperCamelCase : Union[str, Any] = normalize_df[:, 0].tolist()
__UpperCamelCase : List[str] = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
__UpperCamelCase : Optional[int] = normalize_df[:, [1, 2]].tolist()
__UpperCamelCase : Tuple = x[: len(x) - 1]
__UpperCamelCase : Any = x[len(x) - 1 :]
# for linear regression & sarimax
__UpperCamelCase : str = total_date[: len(total_date) - 1]
__UpperCamelCase : Union[str, Any] = total_user[: len(total_user) - 1]
__UpperCamelCase : List[Any] = total_match[: len(total_match) - 1]
__UpperCamelCase : Optional[Any] = total_date[len(total_date) - 1 :]
__UpperCamelCase : str = total_user[len(total_user) - 1 :]
__UpperCamelCase : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
__UpperCamelCase : Any = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
__UpperCamelCase : List[str] = '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
print('''Today\'s data is {not_str}safe.''')
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class A :
def __init__( self : Dict , __magic_name__ : Collection[float] | None = None ):
"""simple docstring"""
if components is None:
lowerCAmelCase__ = []
lowerCAmelCase__ = list(__magic_name__ )
def __len__( self : Optional[Any] ):
"""simple docstring"""
return len(self.__components )
def __str__( self : Optional[Any] ):
"""simple docstring"""
return "(" + ",".join(map(__magic_name__ , self.__components ) ) + ")"
def __add__( self : Dict , __magic_name__ : Vector ):
"""simple docstring"""
lowerCAmelCase__ = len(self )
if size == len(__magic_name__ ):
lowerCAmelCase__ = [self.__components[i] + other.component(__magic_name__ ) for i in range(__magic_name__ )]
return Vector(__magic_name__ )
else:
raise Exception("must have the same size" )
def __sub__( self : Tuple , __magic_name__ : Vector ):
"""simple docstring"""
lowerCAmelCase__ = len(self )
if size == len(__magic_name__ ):
lowerCAmelCase__ = [self.__components[i] - other.component(__magic_name__ ) for i in range(__magic_name__ )]
return Vector(__magic_name__ )
else: # error case
raise Exception("must have the same size" )
@overload
def __mul__( self : Optional[Any] , __magic_name__ : float ):
"""simple docstring"""
...
@overload
def __mul__( self : Union[str, Any] , __magic_name__ : Vector ):
"""simple docstring"""
...
def __mul__( self : Union[str, Any] , __magic_name__ : float | Vector ):
"""simple docstring"""
if isinstance(__magic_name__ , (float, int) ):
lowerCAmelCase__ = [c * other for c in self.__components]
return Vector(__magic_name__ )
elif isinstance(__magic_name__ , __magic_name__ ) and len(self ) == len(__magic_name__ ):
lowerCAmelCase__ = len(self )
lowerCAmelCase__ = [self.__components[i] * other.component(__magic_name__ ) for i in range(__magic_name__ )]
return sum(__magic_name__ )
else: # error case
raise Exception("invalid operand!" )
def __SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
return Vector(self.__components )
def __SCREAMING_SNAKE_CASE ( self : Dict , __magic_name__ : int ):
"""simple docstring"""
if isinstance(__magic_name__ , __magic_name__ ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception("index out of range" )
def __SCREAMING_SNAKE_CASE ( self : int , __magic_name__ : int , __magic_name__ : float ):
"""simple docstring"""
assert -len(self.__components ) <= pos < len(self.__components )
lowerCAmelCase__ = value
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
if len(self.__components ) == 0:
raise Exception("Vector is empty" )
lowerCAmelCase__ = [c**2 for c in self.__components]
return math.sqrt(sum(__magic_name__ ) )
def __SCREAMING_SNAKE_CASE ( self : List[Any] , __magic_name__ : Vector , __magic_name__ : bool = False ):
"""simple docstring"""
lowerCAmelCase__ = self * other
lowerCAmelCase__ = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def A ( UpperCamelCase_ : int ) -> Vector:
'''simple docstring'''
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
return Vector([0] * dimension )
def A ( UpperCamelCase_ : int , UpperCamelCase_ : int ) -> Vector:
'''simple docstring'''
assert isinstance(UpperCamelCase_ , UpperCamelCase_ ) and (isinstance(UpperCamelCase_ , UpperCamelCase_ ))
lowerCAmelCase__ = [0] * dimension
lowerCAmelCase__ = 1
return Vector(UpperCamelCase_ )
def A ( UpperCamelCase_ : float , UpperCamelCase_ : Vector , UpperCamelCase_ : Vector ) -> Vector:
'''simple docstring'''
assert (
isinstance(UpperCamelCase_ , UpperCamelCase_ )
and isinstance(UpperCamelCase_ , UpperCamelCase_ )
and (isinstance(UpperCamelCase_ , (int, float) ))
)
return x * scalar + y
def A ( UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> Vector:
'''simple docstring'''
random.seed(UpperCamelCase_ )
lowerCAmelCase__ = [random.randint(UpperCamelCase_ , UpperCamelCase_ ) for _ in range(UpperCamelCase_ )]
return Vector(UpperCamelCase_ )
class A :
def __init__( self : Dict , __magic_name__ : list[list[float]] , __magic_name__ : int , __magic_name__ : int ):
"""simple docstring"""
lowerCAmelCase__ = matrix
lowerCAmelCase__ = w
lowerCAmelCase__ = h
def __str__( self : str ):
"""simple docstring"""
lowerCAmelCase__ = ""
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self : int , __magic_name__ : Matrix ):
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
lowerCAmelCase__ = []
for i in range(self.__height ):
lowerCAmelCase__ = [
self.__matrix[i][j] + other.component(__magic_name__ , __magic_name__ )
for j in range(self.__width )
]
matrix.append(__magic_name__ )
return Matrix(__magic_name__ , self.__width , self.__height )
else:
raise Exception("matrix must have the same dimension!" )
def __sub__( self : Dict , __magic_name__ : Matrix ):
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
lowerCAmelCase__ = []
for i in range(self.__height ):
lowerCAmelCase__ = [
self.__matrix[i][j] - other.component(__magic_name__ , __magic_name__ )
for j in range(self.__width )
]
matrix.append(__magic_name__ )
return Matrix(__magic_name__ , self.__width , self.__height )
else:
raise Exception("matrices must have the same dimension!" )
@overload
def __mul__( self : Optional[Any] , __magic_name__ : float ):
"""simple docstring"""
...
@overload
def __mul__( self : Optional[Any] , __magic_name__ : Vector ):
"""simple docstring"""
...
def __mul__( self : Optional[int] , __magic_name__ : float | Vector ):
"""simple docstring"""
if isinstance(__magic_name__ , __magic_name__ ): # matrix-vector
if len(__magic_name__ ) == self.__width:
lowerCAmelCase__ = zero_vector(self.__height )
for i in range(self.__height ):
lowerCAmelCase__ = [
self.__matrix[i][j] * other.component(__magic_name__ )
for j in range(self.__width )
]
ans.change_component(__magic_name__ , sum(__magic_name__ ) )
return ans
else:
raise Exception(
"vector must have the same size as the "
"number of columns of the matrix!" )
elif isinstance(__magic_name__ , (int, float) ): # matrix-scalar
lowerCAmelCase__ = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(__magic_name__ , self.__width , self.__height )
return None
def __SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
return self.__height
def __SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
return self.__width
def __SCREAMING_SNAKE_CASE ( self : Optional[int] , __magic_name__ : int , __magic_name__ : int ):
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("change_component: indices out of bounds" )
def __SCREAMING_SNAKE_CASE ( self : Dict , __magic_name__ : int , __magic_name__ : int , __magic_name__ : float ):
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
lowerCAmelCase__ = value
else:
raise Exception("change_component: indices out of bounds" )
def __SCREAMING_SNAKE_CASE ( self : Dict , __magic_name__ : int , __magic_name__ : int ):
"""simple docstring"""
if self.__height != self.__width:
raise Exception("Matrix is not square" )
lowerCAmelCase__ = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(__magic_name__ ) ):
lowerCAmelCase__ = minor[i][:y] + minor[i][y + 1 :]
return Matrix(__magic_name__ , self.__width - 1 , self.__height - 1 ).determinant()
def __SCREAMING_SNAKE_CASE ( self : Any , __magic_name__ : int , __magic_name__ : int ):
"""simple docstring"""
if self.__height != self.__width:
raise Exception("Matrix is not square" )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(__magic_name__ , __magic_name__ )
else:
raise Exception("Indices out of bounds" )
def __SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
if self.__height != self.__width:
raise Exception("Matrix is not square" )
if self.__height < 1:
raise Exception("Matrix has no element" )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
lowerCAmelCase__ = [
self.__matrix[0][y] * self.cofactor(0 , __magic_name__ ) for y in range(self.__width )
]
return sum(__magic_name__ )
def A ( UpperCamelCase_ : int ) -> Matrix:
'''simple docstring'''
lowerCAmelCase__ = [[0] * n for _ in range(UpperCamelCase_ )]
return Matrix(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def A ( UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> Matrix:
'''simple docstring'''
random.seed(UpperCamelCase_ )
lowerCAmelCase__ = [
[random.randint(UpperCamelCase_ , UpperCamelCase_ ) for _ in range(UpperCamelCase_ )] for _ in range(UpperCamelCase_ )
]
return Matrix(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
| 48 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'-m' , '--pretrained_model_name_or_path' , type=_UpperCAmelCase , default=_UpperCAmelCase , required=_UpperCAmelCase , help='Path to pretrained model or model identifier from huggingface.co/models.' , )
parser.add_argument(
'-c' , '--caption' , type=_UpperCAmelCase , default='robotic cat with wings' , help='Text used to generate images.' , )
parser.add_argument(
'-n' , '--images_num' , type=_UpperCAmelCase , default=4 , help='How much images to generate.' , )
parser.add_argument(
'-s' , '--seed' , type=_UpperCAmelCase , default=42 , help='Seed for random process.' , )
parser.add_argument(
'-ci' , '--cuda_id' , type=_UpperCAmelCase , default=0 , help='cuda_id.' , )
lowerCAmelCase = parser.parse_args()
return args
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] ):
if not len(_UpperCAmelCase ) == rows * cols:
raise ValueError('The specified number of rows and columns are not correct.' )
lowerCAmelCase ,lowerCAmelCase = imgs[0].size
lowerCAmelCase = Image.new('RGB' , size=(cols * w, rows * h) )
lowerCAmelCase ,lowerCAmelCase = grid.size
for i, img in enumerate(_UpperCAmelCase ):
grid.paste(_UpperCAmelCase , box=(i % cols * w, i // cols * h) )
return grid
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any]="robotic cat with wings" , _UpperCAmelCase : Optional[int]=7.5 , _UpperCAmelCase : Dict=50 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : int=42 , ):
lowerCAmelCase = torch.Generator(pipeline.device ).manual_seed(_UpperCAmelCase )
lowerCAmelCase = pipeline(
_UpperCAmelCase , guidance_scale=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , generator=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , ).images
lowerCAmelCase = int(math.sqrt(_UpperCAmelCase ) )
lowerCAmelCase = image_grid(_UpperCAmelCase , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__UpperCamelCase : Optional[Any] = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase : List[Any] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__UpperCamelCase : str = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__UpperCamelCase : Optional[int] = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__UpperCamelCase : List[str] = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')
__UpperCamelCase : Tuple = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase : Union[str, Any] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
__UpperCamelCase : Dict = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, '''unet''', unet)
else:
__UpperCamelCase : Dict = unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase : Optional[Any] = pipeline.to(unet.device)
__UpperCamelCase ,__UpperCamelCase : List[Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
__UpperCamelCase : int = os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))
| 4 | 0 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self : Tuple ):
__UpperCAmelCase = ''''''
__UpperCAmelCase = ''''''
__UpperCAmelCase = []
__UpperCAmelCase = 0
__UpperCAmelCase = 2_56
__UpperCAmelCase = 0
__UpperCAmelCase = 0
__UpperCAmelCase = 0
__UpperCAmelCase = 0
def a ( self : List[Any] , _lowercase : List[Any] ):
__UpperCAmelCase = cva.imread(_lowercase , 0 )
__UpperCAmelCase = copy.deepcopy(self.img )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = plt.hist(self.img.ravel() , 2_56 , [0, 2_56] , label='''x''' )
__UpperCAmelCase = np.sum(_lowercase )
for i in range(len(_lowercase ) ):
__UpperCAmelCase = x[i] / self.k
self.sk += prk
__UpperCAmelCase = (self.L - 1) * self.sk
if self.rem != 0:
__UpperCAmelCase = int(last % last )
__UpperCAmelCase = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(_lowercase )
__UpperCAmelCase = int(np.ma.count(self.img ) / self.img[1].size )
__UpperCAmelCase = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
__UpperCAmelCase = self.img[j][i]
if num != self.last_list[num]:
__UpperCAmelCase = self.last_list[num]
cva.imwrite('''output_data/output.jpg''' , self.img )
def a ( self : Tuple ):
plt.hist(self.img.ravel() , 2_56 , [0, 2_56] )
def a ( self : Union[str, Any] ):
cva.imshow('''Output-Image''' , self.img )
cva.imshow('''Input-Image''' , self.original_image )
cva.waitKey(50_00 )
cva.destroyAllWindows()
if __name__ == "__main__":
_lowercase : Optional[int] = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
_lowercase : Union[str, Any] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 49 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : List[int] ):
lowerCAmelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(_UpperCAmelCase ) == len(_UpperCAmelCase ), F'{len(_UpperCAmelCase )} != {len(_UpperCAmelCase )}'
dest_layers.load_state_dict(layers_to_copy.state_dict() )
__UpperCamelCase : Optional[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCamelCase : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] ):
try:
lowerCAmelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
F' {n_student}' )
return list(range(_UpperCAmelCase ) )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ):
if n_student > n_teacher:
raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}' )
elif n_teacher == n_student:
return list(range(_UpperCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, PreTrainedModel] , _UpperCAmelCase : Union[str, Path] = "student" , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Optional[Any]=None , **_UpperCAmelCase : str , ):
lowerCAmelCase = 'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
assert (e is not None) or (d is not None), _msg
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
AutoTokenizer.from_pretrained(_UpperCAmelCase ).save_pretrained(_UpperCAmelCase ) # purely for convenience
lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).eval()
else:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ), F'teacher must be a model or string got type {type(_UpperCAmelCase )}'
lowerCAmelCase = teacher.config.to_diff_dict()
try:
lowerCAmelCase ,lowerCAmelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
init_kwargs.update({'encoder_layers': e, 'decoder_layers': d} )
except AttributeError: # T5
if hasattr(teacher.config , 'num_encoder_layers' ):
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
if hasattr(teacher.config , 'num_encoder_layers' ):
init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d} )
else:
init_kwargs.update({'num_layers': e, 'num_decoder_layers': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(_UpperCAmelCase )
# Copy weights
lowerCAmelCase = teacher.config_class(**_UpperCAmelCase )
lowerCAmelCase = AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
lowerCAmelCase = student.load_state_dict(teacher.state_dict() , strict=_UpperCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
lowerCAmelCase ,lowerCAmelCase = list(range(_UpperCAmelCase ) ), list(range(_UpperCAmelCase ) )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
F' {save_path}' )
student.save_pretrained(_UpperCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
if d_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
try:
if hasattr(
_UpperCAmelCase , 'prophetnet' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _UpperCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _UpperCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _UpperCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , _UpperCAmelCase )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' )
lowerCAmelCase = {
'teacher_type': teacher.config.model_type,
'copied_encoder_layers': e_layers_to_copy,
'copied_decoder_layers': d_layers_to_copy,
}
student.save_pretrained(_UpperCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 4 | 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_squeezebert import SqueezeBertTokenizer
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase : int = {
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase : Tuple = {
'squeezebert/squeezebert-uncased': 5_12,
'squeezebert/squeezebert-mnli': 5_12,
'squeezebert/squeezebert-mnli-headless': 5_12,
}
UpperCamelCase : Dict = {
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = VOCAB_FILES_NAMES
_UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
_UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase = SqueezeBertTokenizer
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase="[UNK]" ,_lowerCAmelCase="[SEP]" ,_lowerCAmelCase="[PAD]" ,_lowerCAmelCase="[CLS]" ,_lowerCAmelCase="[MASK]" ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
super().__init__(
_lowerCAmelCase ,tokenizer_file=_lowerCAmelCase ,do_lower_case=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,mask_token=_lowerCAmelCase ,tokenize_chinese_chars=_lowerCAmelCase ,strip_accents=_lowerCAmelCase ,**_lowerCAmelCase ,)
lowerCamelCase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,_lowerCAmelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,_lowerCAmelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,_lowerCAmelCase ) != tokenize_chinese_chars
):
lowerCamelCase__ = getattr(_lowerCAmelCase ,normalizer_state.pop("""type""" ) )
lowerCamelCase__ = do_lower_case
lowerCamelCase__ = strip_accents
lowerCamelCase__ = tokenize_chinese_chars
lowerCamelCase__ = normalizer_class(**_lowerCAmelCase )
lowerCamelCase__ = do_lower_case
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ):
lowerCamelCase__ = [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 ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = [self.sep_token_id]
lowerCamelCase__ = [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 ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = self._tokenizer.model.save(_lowerCAmelCase ,name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 50 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Dict = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = ['''LayoutXLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = ['''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
__UpperCamelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 4 | 0 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase =VQModel
_lowerCamelCase ="sample"
@property
def __snake_case ( self : List[str] , a__ : Optional[int]=(32, 32) ):
UpperCAmelCase = 4
UpperCAmelCase = 3
UpperCAmelCase = floats_tensor((batch_size, num_channels) + sizes ).to(a__ )
return {"sample": image}
@property
def __snake_case ( self : Any ):
return (3, 32, 32)
@property
def __snake_case ( self : Dict ):
return (3, 32, 32)
def __snake_case ( self : Dict ):
UpperCAmelCase = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 3,
}
UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def __snake_case ( self : List[str] ):
pass
def __snake_case ( self : List[Any] ):
pass
def __snake_case ( self : int ):
UpperCAmelCase, UpperCAmelCase = VQModel.from_pretrained('''fusing/vqgan-dummy''' , output_loading_info=a__ )
self.assertIsNotNone(a__ )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(a__ )
UpperCAmelCase = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def __snake_case ( self : List[str] ):
UpperCAmelCase = VQModel.from_pretrained('''fusing/vqgan-dummy''' )
model.to(a__ ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
UpperCAmelCase = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
UpperCAmelCase = image.to(a__ )
with torch.no_grad():
UpperCAmelCase = model(a__ ).sample
UpperCAmelCase = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
UpperCAmelCase = torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143] )
# fmt: on
self.assertTrue(torch.allclose(a__ , a__ , atol=1e-3 ) )
| 51 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase = F'Resistor at index {index} has a negative or zero value!'
raise ValueError(_UpperCAmelCase )
first_sum += 1 / float(_UpperCAmelCase )
index += 1
return 1 / first_sum
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowerCAmelCase = F'Resistor at index {index} has a negative value!'
raise ValueError(_UpperCAmelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
A = random.Random()
def __A ( a_ :List[str] , a_ :int=1.0 , a_ :Optional[Any]=None , a_ :int=None) -> List[Any]:
if rng is None:
__a : int = global_rng
__a : Optional[Any] = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
@require_torch
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=7 , _UpperCAmelCase=400 , _UpperCAmelCase=2000 , _UpperCAmelCase=1 , _UpperCAmelCase=0.0 , _UpperCAmelCase=16000 , _UpperCAmelCase=True , _UpperCAmelCase=80 , _UpperCAmelCase=16 , _UpperCAmelCase=64 , _UpperCAmelCase="hann_window" , _UpperCAmelCase=80 , _UpperCAmelCase=7600 , _UpperCAmelCase=1e-1_0 , _UpperCAmelCase=True , ):
__a : Optional[Any] = parent
__a : int = batch_size
__a : Optional[int] = min_seq_length
__a : Any = max_seq_length
__a : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__a : Union[str, Any] = feature_size
__a : Optional[int] = padding_value
__a : int = sampling_rate
__a : str = do_normalize
__a : int = num_mel_bins
__a : Dict = hop_length
__a : Dict = win_length
__a : Dict = win_function
__a : Optional[Any] = fmin
__a : Union[str, Any] = fmax
__a : Tuple = mel_floor
__a : Optional[Any] = return_attention_mask
def _lowerCamelCase ( self ):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def _lowerCamelCase ( self , _UpperCAmelCase=False , _UpperCAmelCase=False ):
def _flatten(_UpperCAmelCase ):
return list(itertools.chain(*_UpperCAmelCase ) )
if equal_length:
__a : Optional[int] = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
__a : Optional[int] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__a : Any = [np.asarray(_UpperCAmelCase ) for x in speech_inputs]
return speech_inputs
def _lowerCamelCase ( self , _UpperCAmelCase=False , _UpperCAmelCase=False ):
if equal_length:
__a : int = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
__a : Optional[int] = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__a : List[Any] = [np.asarray(_UpperCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
class __lowercase ( _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = SpeechTaFeatureExtractor
def _lowerCamelCase ( self ):
__a : Union[str, Any] = SpeechTaFeatureExtractionTester(self )
def _lowerCamelCase ( self , _UpperCAmelCase ):
self.assertTrue(np.all(np.mean(_UpperCAmelCase , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase , axis=0 ) - 1 ) < 1e-3 ) )
def _lowerCamelCase ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
__a : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__a : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__a : Union[str, Any] = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
# Test not batched input
__a : str = feat_extract(speech_inputs[0] , return_tensors='''np''' ).input_values
__a : Dict = feat_extract(np_speech_inputs[0] , return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3 ) )
# Test batched
__a : Any = feat_extract(_UpperCAmelCase , return_tensors='''np''' ).input_values
__a : List[Any] = feat_extract(_UpperCAmelCase , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3 ) )
def _lowerCamelCase ( self ):
__a : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__a : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__a : Optional[Any] = ['''longest''', '''max_length''', '''do_not_pad''']
__a : Tuple = [None, 1600, None]
for max_length, padding in zip(_UpperCAmelCase , _UpperCAmelCase ):
__a : Union[str, Any] = feat_extract(_UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , return_tensors='''np''' )
__a : str = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def _lowerCamelCase ( self ):
__a : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__a : Any = range(800 , 1400 , 200 )
__a : Dict = [floats_list((1, x) )[0] for x in lengths]
__a : int = ['''longest''', '''max_length''', '''do_not_pad''']
__a : Any = [None, 1600, None]
for max_length, padding in zip(_UpperCAmelCase , _UpperCAmelCase ):
__a : int = feat_extract(_UpperCAmelCase , max_length=_UpperCAmelCase , padding=_UpperCAmelCase )
__a : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def _lowerCamelCase ( self ):
__a : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__a : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__a : int = feat_extract(
_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=1000 , padding='''max_length''' , return_tensors='''np''' )
__a : List[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def _lowerCamelCase ( self ):
__a : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__a : Optional[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__a : List[str] = feat_extract(
_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=1000 , padding='''longest''' , return_tensors='''np''' )
__a : List[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
__a : List[str] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__a : Optional[int] = feat_extract(
_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=2000 , padding='''longest''' , return_tensors='''np''' )
__a : Dict = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def _lowerCamelCase ( self ):
__a : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__a : Optional[int] = np.random.rand(100 ).astype(np.floataa )
__a : Any = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__a : Any = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
__a : str = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def _lowerCamelCase ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
__a : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__a : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__a : Tuple = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
# Test feature size
__a : Union[str, Any] = feature_extractor(audio_target=_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='''np''' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
__a : Tuple = feature_extractor(speech_inputs[0] , return_tensors='''np''' ).input_values
__a : int = feature_extractor(np_speech_inputs[0] , return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3 ) )
# Test batched
__a : Union[str, Any] = feature_extractor(_UpperCAmelCase , return_tensors='''np''' ).input_values
__a : Union[str, Any] = feature_extractor(_UpperCAmelCase , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
__a : Any = [floats_list((1, x) )[0] for x in (800, 800, 800)]
__a : str = np.asarray(_UpperCAmelCase )
__a : List[str] = feature_extractor(_UpperCAmelCase , return_tensors='''np''' ).input_values
__a : str = feature_extractor(_UpperCAmelCase , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3 ) )
def _lowerCamelCase ( self ):
__a : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_target()
__a : int = self.feature_extraction_class(**self.feat_extract_dict )
__a : Tuple = feat_extract.model_input_names[0]
__a : int = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) for x, y in zip(_UpperCAmelCase , processed_features[input_name] ) ) )
__a : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=_UpperCAmelCase )
__a : List[str] = BatchFeature({input_name: speech_inputs} , tensor_type='''np''' )
__a : Any = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__a : str = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def _lowerCamelCase ( self ):
__a : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=_UpperCAmelCase )
__a : Dict = self.feature_extraction_class(**self.feat_extract_dict )
__a : List[str] = feat_extract.model_input_names[0]
__a : Dict = BatchFeature({input_name: speech_inputs} , tensor_type='''pt''' )
__a : List[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__a : List[str] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def _lowerCamelCase ( self ):
__a : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
__a : str = self.feat_extract_tester.prepare_inputs_for_target()
__a : Tuple = feat_extract.model_input_names[0]
__a : Optional[int] = BatchFeature({input_name: speech_inputs} )
__a : int = feat_extract.num_mel_bins # hack!
__a : Any = feat_extract.pad(_UpperCAmelCase , padding='''longest''' , return_tensors='''np''' )[input_name]
__a : Optional[int] = feat_extract.pad(_UpperCAmelCase , padding='''longest''' , return_tensors='''pt''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _lowerCamelCase ( self ):
__a : Union[str, Any] = self.feat_extract_dict
__a : str = True
__a : Dict = self.feature_extraction_class(**_UpperCAmelCase )
__a : int = self.feat_extract_tester.prepare_inputs_for_target()
__a : Optional[Any] = [len(_UpperCAmelCase ) for x in speech_inputs]
__a : Any = feat_extract.model_input_names[0]
__a : Dict = BatchFeature({input_name: speech_inputs} )
__a : Any = feat_extract.num_mel_bins # hack!
__a : Tuple = feat_extract.pad(_UpperCAmelCase , padding='''longest''' , return_tensors='''np''' )
self.assertIn('''attention_mask''' , _UpperCAmelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : Any = self.feat_extract_dict
__a : Dict = True
__a : List[str] = self.feature_extraction_class(**_UpperCAmelCase )
__a : str = self.feat_extract_tester.prepare_inputs_for_target()
__a : Optional[int] = [len(_UpperCAmelCase ) for x in speech_inputs]
__a : Tuple = feat_extract.model_input_names[0]
__a : Optional[Any] = BatchFeature({input_name: speech_inputs} )
__a : Tuple = min(_UpperCAmelCase )
__a : str = feat_extract.num_mel_bins # hack!
__a : Dict = feat_extract.pad(
_UpperCAmelCase , padding='''max_length''' , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors='''np''' )
self.assertIn('''attention_mask''' , _UpperCAmelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def _lowerCamelCase ( self , _UpperCAmelCase ):
from datasets import load_dataset
__a : Dict = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
__a : Tuple = ds.sort('''id''' ).select(range(_UpperCAmelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def _lowerCamelCase ( self ):
# fmt: off
__a : List[Any] = torch.tensor(
[2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3] )
# fmt: on
__a : Union[str, Any] = self._load_datasamples(1 )
__a : str = SpeechTaFeatureExtractor()
__a : List[str] = feature_extractor(_UpperCAmelCase , return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape , (1, 93680) )
self.assertTrue(torch.allclose(input_values[0, :30] , _UpperCAmelCase , atol=1e-6 ) )
def _lowerCamelCase ( self ):
# fmt: off
__a : Tuple = torch.tensor(
[-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
-3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
-3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
-3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8] )
# fmt: on
__a : Dict = self._load_datasamples(1 )
__a : Any = SpeechTaFeatureExtractor()
__a : List[Any] = feature_extractor(audio_target=_UpperCAmelCase , return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , _UpperCAmelCase , atol=1e-4 ) ) | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class a ( a__ ):
snake_case__ = '''glpn'''
def __init__( self , _snake_case=3 , _snake_case=4 , _snake_case=[2, 2, 2, 2] , _snake_case=[8, 4, 2, 1] , _snake_case=[32, 64, 1_60, 2_56] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[1, 2, 5, 8] , _snake_case=[4, 4, 4, 4] , _snake_case="gelu" , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=0.1 , _snake_case=1E-6 , _snake_case=64 , _snake_case=10 , _snake_case=-1 , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
lowerCAmelCase = num_channels
lowerCAmelCase = num_encoder_blocks
lowerCAmelCase = depths
lowerCAmelCase = sr_ratios
lowerCAmelCase = hidden_sizes
lowerCAmelCase = patch_sizes
lowerCAmelCase = strides
lowerCAmelCase = mlp_ratios
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = initializer_range
lowerCAmelCase = drop_path_rate
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = decoder_hidden_size
lowerCAmelCase = max_depth
lowerCAmelCase = head_in_index
| 4 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = field(default="""question-answering-extractive""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
a_ = Features({"""question""": Value("""string""" ), """context""": Value("""string""" )} )
a_ = Features(
{
"""answers""": Sequence(
{
"""text""": Value("""string""" ),
"""answer_start""": Value("""int32""" ),
} )
} )
a_ = "question"
a_ = "context"
a_ = "answers"
@property
def lowercase ( self : Any ) -> Dict[str, str]:
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 53 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , _snake_case=10_00 , ):
"""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
lowerCAmelCase = range_bbox
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase = bbox[i, j, 3]
lowerCAmelCase = bbox[i, j, 1]
lowerCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase = bbox[i, j, 2]
lowerCAmelCase = bbox[i, j, 0]
lowerCAmelCase = t
lowerCAmelCase = tf.convert_to_tensor(_snake_case )
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 = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForMaskedLM(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForSequenceClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForTokenClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
snake_case__ = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ = False
snake_case__ = True
snake_case__ = 1_0
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = TFLayoutLMModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@unittest.skip('Onnx compliancy broke with TF 2.10' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def _SCREAMING_SNAKE_CASE ():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowerCAmelCase = tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
lowerCAmelCase = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the sequence output on [0, :3, :3]
lowerCAmelCase = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , _snake_case , atol=1E-3 ) )
# test the pooled output on [1, :3]
lowerCAmelCase = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , _snake_case , atol=1E-3 ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=2 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
lowerCAmelCase = outputs.loss
lowerCAmelCase = (2,)
self.assertEqual(loss.shape , _snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = (2, 2)
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=13 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the shape of the logits
lowerCAmelCase = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , _snake_case )
self.assertEqual(outputs.end_logits.shape , _snake_case )
| 4 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def a__ ( ):
'''simple docstring'''
UpperCAmelCase_ =HfArgumentParser(lowercase__ )
UpperCAmelCase_ =parser.parse_args_into_dataclasses()[0]
UpperCAmelCase_ =TensorFlowBenchmark(args=lowercase__ )
try:
UpperCAmelCase_ =parser.parse_args_into_dataclasses()[0]
except ValueError as e:
UpperCAmelCase_ ="Arg --no_{0} is no longer used, please use --no-{0} instead."
UpperCAmelCase_ =" ".join(str(lowercase__ ).split(" " )[:-1] )
UpperCAmelCase_ =""
UpperCAmelCase_ =eval(str(lowercase__ ).split(" " )[-1] )
UpperCAmelCase_ =[]
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(lowercase__ )
if len(lowercase__ ) > 0:
UpperCAmelCase_ =full_error_msg + begin_error_msg + str(lowercase__ )
raise ValueError(lowercase__ )
benchmark.run()
if __name__ == "__main__":
main()
| 54 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__UpperCamelCase : Union[str, Any] = '''examples/'''
__UpperCamelCase : str = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
'''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
__UpperCamelCase : List[str] = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__UpperCamelCase : Optional[int] = '''README.md'''
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : Any ):
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase ,lowerCAmelCase = REPLACE_PATTERNS[pattern]
lowerCAmelCase = replace.replace('VERSION' , _UpperCAmelCase )
lowerCAmelCase = re_pattern.sub(_UpperCAmelCase , _UpperCAmelCase )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, Any] ):
for folder, directories, fnames in os.walk(_UpperCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase , pattern='examples' )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if not patch:
update_version_in_examples(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = '🤗 Transformers currently provides the following architectures'
lowerCAmelCase = '1. Want to contribute a new model?'
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.readlines()
# Find the start of the list.
lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
lowerCAmelCase = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
with open(REPLACE_FILES['init'] , 'r' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase = REPLACE_PATTERNS['init'][0].search(_UpperCAmelCase ).groups()[0]
return packaging.version.parse(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple=False ):
lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
lowerCAmelCase = default_version.base_version
elif patch:
lowerCAmelCase = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
lowerCAmelCase = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
lowerCAmelCase = input(F'Which version are you releasing? [{default_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = default_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase , patch=_UpperCAmelCase )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = get_version()
lowerCAmelCase = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
lowerCAmelCase = input(F'Which version are we developing now? [{dev_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = dev_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 4 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = ShapEPipeline
snake_case_ = ["prompt"]
snake_case_ = ["prompt"]
snake_case_ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
snake_case_ = False
@property
def UpperCamelCase_ ( self : List[Any] ):
return 32
@property
def UpperCamelCase_ ( self : Optional[int] ):
return 32
@property
def UpperCamelCase_ ( self : Tuple ):
return self.time_input_dim * 4
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return 8
@property
def UpperCamelCase_ ( self : Tuple ):
__A = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def UpperCamelCase_ ( self : int ):
torch.manual_seed(0 )
__A = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=self.text_embedder_hidden_size ,projection_dim=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=10_00 ,)
return CLIPTextModelWithProjection(A )
@property
def UpperCamelCase_ ( self : List[Any] ):
torch.manual_seed(0 )
__A = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
__A = PriorTransformer(**A )
return model
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
__A = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
__A = ShapERenderer(**A )
return model
def UpperCamelCase_ ( self : List[str] ):
__A = self.dummy_prior
__A = self.dummy_text_encoder
__A = self.dummy_tokenizer
__A = self.dummy_renderer
__A = HeunDiscreteScheduler(
beta_schedule="exp" ,num_train_timesteps=10_24 ,prediction_type="sample" ,use_karras_sigmas=A ,clip_sample=A ,clip_sample_range=1.0 ,)
__A = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def UpperCamelCase_ ( self : Dict ,A : Optional[Any] ,A : Any=0 ):
if str(A ).startswith("mps" ):
__A = torch.manual_seed(A )
else:
__A = torch.Generator(device=A ).manual_seed(A )
__A = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def UpperCamelCase_ ( self : List[str] ):
__A = "cpu"
__A = self.get_dummy_components()
__A = self.pipeline_class(**A )
__A = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
__A = pipe(**self.get_dummy_inputs(A ) )
__A = output.images[0]
__A = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__A = np.array(
[
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase_ ( self : Optional[Any] ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase_ ( self : int ):
__A = torch_device == "cpu"
__A = True
self._test_inference_batch_single_identical(
batch_size=2 ,test_max_difference=A ,relax_max_difference=A ,)
def UpperCamelCase_ ( self : Optional[Any] ):
__A = self.get_dummy_components()
__A = self.pipeline_class(**A )
__A = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
__A = 1
__A = 2
__A = self.get_dummy_inputs(A )
for key in inputs.keys():
if key in self.batch_params:
__A = batch_size * [inputs[key]]
__A = pipe(**A ,num_images_per_prompt=A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : Dict ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : Optional[int] ):
__A = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy" )
__A = ShapEPipeline.from_pretrained("openai/shap-e" )
__A = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
__A = torch.Generator(device=A ).manual_seed(0 )
__A = pipe(
"a shark" ,generator=A ,guidance_scale=15.0 ,num_inference_steps=64 ,frame_size=64 ,output_type="np" ,).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(A ,A )
| 55 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
__UpperCamelCase : Optional[int] = pytest.mark.integration
@require_faiss
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(_snake_case ) for x in np.arange(30 ).tolist()]} )
return dset
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
lowerCAmelCase = dset.map(
lambda _snake_case , _snake_case : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=_snake_case , keep_in_memory=_snake_case )
lowerCAmelCase = dset.add_faiss_index('vecs' , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT , )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_snake_case ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(_snake_case , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
lowerCAmelCase = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCAmelCase = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
lowerCAmelCase = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=_snake_case )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertRaises(_snake_case , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
lowerCAmelCase = np.eye(5 , dtype=np.floataa )[::-1]
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case )
self.assertRaises(_snake_case , index.search_batch , queries[0] )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , _snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
lowerCAmelCase = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(_snake_case ):
lowerCAmelCase = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = faiss.IndexFlat(5 )
lowerCAmelCase = FaissIndex(custom_index=_snake_case )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_snake_case ) as tmp_file:
index.save(tmp_file.name )
lowerCAmelCase = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Dict ):
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
lowerCAmelCase = 'index.faiss'
lowerCAmelCase = F'mock://{index_name}'
index.save(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCAmelCase = FaissIndex.load(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCAmelCase = Elasticsearch()
lowerCAmelCase = {'acknowledged': True}
lowerCAmelCase = ElasticSearchIndex(es_client=_snake_case )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
lowerCAmelCase = 'foo'
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
lowerCAmelCase = 'foo'
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
lowerCAmelCase = ['foo', 'bar', 'foobar']
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , _snake_case )
# batched queries with timeout
lowerCAmelCase = ['foo', 'bar', 'foobar']
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case , request_timeout=30 )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , _snake_case )
| 4 | 0 |
'''simple docstring'''
def _a (lowercase__ : int , lowercase__ : int ) -> float:
"""simple docstring"""
return base * power(lowercase__ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("Raise base to the power of exponent using recursion...")
_a : Union[str, Any] = int(input("Enter the base: ").strip())
_a : Any = int(input("Enter the exponent: ").strip())
_a : List[str] = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
_a : List[Any] = 1 / result
print(f'''{base} to the power of {exponent} is {result}''')
| 56 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = IFInpaintingPipeline
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._get_dummy_components()
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_save_load_local()
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 4 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
A_ : str = logging.getLogger(__name__)
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self ):
UpperCamelCase_: Optional[int] = False
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if not self.initialized:
UpperCamelCase_: Optional[Any] = RagRetriever(
_lowerCamelCase , question_encoder_tokenizer=_lowerCamelCase , generator_tokenizer=_lowerCamelCase , index=_lowerCamelCase , init_retrieval=_lowerCamelCase , )
UpperCamelCase_: str = True
def _a ( self ):
self.retriever.index.init_index()
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_ ,UpperCamelCase_: Any = self.retriever._main_retrieve(_lowerCamelCase , _lowerCamelCase )
return doc_ids, retrieved_doc_embeds
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None ):
if index is not None and index.is_initialized() and len(_lowerCamelCase ) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
super().__init__(
_lowerCamelCase , question_encoder_tokenizer=_lowerCamelCase , generator_tokenizer=_lowerCamelCase , index=_lowerCamelCase , init_retrieval=_lowerCamelCase , )
UpperCamelCase_: List[str] = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
for worker in self.retrieval_workers
] )
def _a ( self ):
logger.info('initializing retrieval' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
UpperCamelCase_: Union[str, Any] = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
UpperCamelCase_ ,UpperCamelCase_: str = ray.get(random_worker.retrieve.remote(_lowerCamelCase , _lowerCamelCase ) )
else:
UpperCamelCase_ ,UpperCamelCase_: Dict = self._main_retrieve(_lowerCamelCase , _lowerCamelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCamelCase )
@classmethod
def _a ( cls , _lowerCamelCase , _lowerCamelCase=None , **_lowerCamelCase ):
return super(_lowerCamelCase , cls ).get_tokenizers(_lowerCamelCase , _lowerCamelCase , **_lowerCamelCase )
@classmethod
def _a ( cls , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , **_lowerCamelCase ):
UpperCamelCase_: List[str] = kwargs.pop('config' , _lowerCamelCase ) or RagConfig.from_pretrained(_lowerCamelCase , **_lowerCamelCase )
UpperCamelCase_: Optional[int] = RagTokenizer.from_pretrained(_lowerCamelCase , config=_lowerCamelCase )
UpperCamelCase_: List[str] = rag_tokenizer.question_encoder
UpperCamelCase_: List[Any] = rag_tokenizer.generator
if indexed_dataset is not None:
UpperCamelCase_: Union[str, Any] = 'custom'
UpperCamelCase_: int = CustomHFIndex(config.retrieval_vector_size , _lowerCamelCase )
else:
UpperCamelCase_: str = cls._build_index(_lowerCamelCase )
return cls(
_lowerCamelCase , question_encoder_tokenizer=_lowerCamelCase , generator_tokenizer=_lowerCamelCase , retrieval_workers=_lowerCamelCase , index=_lowerCamelCase , ) | 57 |
"""simple docstring"""
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 a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
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
lowerCAmelCase = self.vocab_size - 1
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
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 = 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 , )
lowerCAmelCase = 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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , head_mask=_snake_case )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTDoubleHeadsModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = OpenAIGPTForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class a ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
snake_case__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
snake_case__ = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case=False ):
"""simple docstring"""
lowerCAmelCase = super()._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_snake_case )
return inputs_dict
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , n_embd=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = OpenAIGPTModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@require_torch
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(_snake_case )
lowerCAmelCase = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=_snake_case ) # the president is
lowerCAmelCase = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowerCAmelCase = model.generate(_snake_case , do_sample=_snake_case )
self.assertListEqual(output_ids[0].tolist() , _snake_case )
| 4 | 0 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase = "cpu" , _lowercase = "openai/clip-vit-large-patch14" ) -> None:
'''simple docstring'''
snake_case_ : Optional[Any] = device
snake_case_ : List[Any] = CLIPTokenizerFast.from_pretrained(_lowercase )
snake_case_ : Optional[int] = [0.4814_5466, 0.457_8275, 0.4082_1073]
snake_case_ : Union[str, Any] = [0.2686_2954, 0.2613_0258, 0.2757_7711]
snake_case_ : str = torchvision.transforms.Normalize(self.image_mean , self.image_std )
snake_case_ : Any = torchvision.transforms.Resize(2_2_4 )
snake_case_ : str = torchvision.transforms.CenterCrop(2_2_4 )
def UpperCAmelCase__ ( self , _lowercase ) -> Dict:
'''simple docstring'''
snake_case_ : int = self.resize(_lowercase )
snake_case_ : int = self.center_crop(_lowercase )
snake_case_ : Union[str, Any] = self.normalize(_lowercase )
return images
def __call__( self , _lowercase=None , _lowercase=None , **_lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = self.tokenizer(text=_lowercase , **_lowercase )
snake_case_ : Optional[int] = self.preprocess_img(_lowercase )
snake_case_ : Optional[Any] = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase=1_0 , _lowercase=0.01 , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=False , _lowercase=True , _lowercase="image" , _lowercase=True , _lowercase=False , _lowercase=False , _lowercase=False , ) -> None:
'''simple docstring'''
super().__init__()
snake_case_ : List[Any] = None
snake_case_ : List[str] = device if device else get_device()
if vqgan:
snake_case_ : str = vqgan
else:
snake_case_ : Any = load_vqgan(self.device , conf_path=_lowercase , ckpt_path=_lowercase )
self.vqgan.eval()
if clip:
snake_case_ : int = clip
else:
snake_case_ : List[str] = CLIPModel.from_pretrained("""openai/clip-vit-base-patch32""" )
self.clip.to(self.device )
snake_case_ : Tuple = ProcessorGradientFlow(device=self.device )
snake_case_ : int = iterations
snake_case_ : str = lr
snake_case_ : int = log
snake_case_ : List[Any] = make_grid
snake_case_ : Tuple = return_val
snake_case_ : Any = quantize
snake_case_ : Optional[int] = self.vqgan.decoder.z_shape
def UpperCAmelCase__ ( self , _lowercase=None , _lowercase=None , _lowercase=5 , _lowercase=True ) -> int:
'''simple docstring'''
snake_case_ : Tuple = []
if output_path is None:
snake_case_ : List[Any] = """./animation.gif"""
if input_path is None:
snake_case_ : Dict = self.save_path
snake_case_ : Optional[Any] = sorted(glob(input_path + """/*""" ) )
if not len(_lowercase ):
raise ValueError(
"""No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
""" function?)""" )
if len(_lowercase ) == 1:
print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)""" )
snake_case_ : List[Any] = total_duration / len(_lowercase )
snake_case_ : List[str] = [frame_duration] * len(_lowercase )
if extend_frames:
snake_case_ : Dict = 1.5
snake_case_ : Union[str, Any] = 3
for file_name in paths:
if file_name.endswith(""".png""" ):
images.append(imageio.imread(_lowercase ) )
imageio.mimsave(_lowercase , _lowercase , duration=_lowercase )
print(f'gif saved to {output_path}' )
def UpperCAmelCase__ ( self , _lowercase=None , _lowercase=None ) -> Tuple:
'''simple docstring'''
if not (path or img):
raise ValueError("""Input either path or tensor""" )
if img is not None:
raise NotImplementedError
snake_case_ : Any = preprocess(Image.open(_lowercase ) , target_image_size=2_5_6 ).to(self.device )
snake_case_ : Dict = preprocess_vqgan(_lowercase )
snake_case_ , *snake_case_ : str = self.vqgan.encode(_lowercase )
return z
def UpperCAmelCase__ ( self , _lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = self.latent.detach().requires_grad_()
snake_case_ : Union[str, Any] = base_latent + transform_vector
if self.quantize:
snake_case_ , *snake_case_ : Optional[int] = self.vqgan.quantize(_lowercase )
else:
snake_case_ : List[str] = trans_latent
return self.vqgan.decode(_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase=None ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.clip_preprocessor(text=_lowercase , images=_lowercase , return_tensors="""pt""" , padding=_lowercase )
snake_case_ : Union[str, Any] = self.clip(**_lowercase )
snake_case_ : List[Any] = clip_outputs.logits_per_image
if weights is not None:
snake_case_ : int = similarity_logits * weights
return similarity_logits.sum()
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self._get_clip_similarity(pos_prompts["""prompts"""] , _lowercase , weights=(1 / pos_prompts["""weights"""]) )
if neg_prompts:
snake_case_ : Tuple = self._get_clip_similarity(neg_prompts["""prompts"""] , _lowercase , weights=neg_prompts["""weights"""] )
else:
snake_case_ : Any = torch.tensor([1] , device=self.device )
snake_case_ : Any = -torch.log(_lowercase ) + torch.log(_lowercase )
return loss
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = torch.randn_like(self.latent , requires_grad=_lowercase , device=self.device )
snake_case_ : Dict = torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
snake_case_ : List[str] = self._add_vector(_lowercase )
snake_case_ : Union[str, Any] = loop_post_process(_lowercase )
snake_case_ : int = self._get_CLIP_loss(_lowercase , _lowercase , _lowercase )
print("""CLIP loss""" , _lowercase )
if self.log:
wandb.log({"""CLIP Loss""": clip_loss} )
clip_loss.backward(retain_graph=_lowercase )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
'''simple docstring'''
wandb.init(reinit=_lowercase , project="""face-editor""" )
wandb.config.update({"""Positive Prompts""": positive_prompts} )
wandb.config.update({"""Negative Prompts""": negative_prompts} )
wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations} )
if image_path:
snake_case_ : Tuple = Image.open(_lowercase )
snake_case_ : str = image.resize((2_5_6, 2_5_6) )
wandb.log("""Original Image""" , wandb.Image(_lowercase ) )
def UpperCAmelCase__ ( self , _lowercase ) -> Any:
'''simple docstring'''
if not prompts:
return []
snake_case_ : List[str] = []
snake_case_ : str = []
if isinstance(_lowercase , _lowercase ):
snake_case_ : Union[str, Any] = [prompt.strip() for prompt in prompts.split("""|""" )]
for prompt in prompts:
if isinstance(_lowercase , (tuple, list) ):
snake_case_ : Union[str, Any] = prompt[0]
snake_case_ : Dict = float(prompt[1] )
elif ":" in prompt:
snake_case_ , snake_case_ : Tuple = prompt.split(""":""" )
snake_case_ : Optional[Any] = float(_lowercase )
else:
snake_case_ : Dict = prompt
snake_case_ : Optional[Any] = 1.0
processed_prompts.append(_lowercase )
weights.append(_lowercase )
return {
"prompts": processed_prompts,
"weights": torch.tensor(_lowercase , device=self.device ),
}
def UpperCAmelCase__ ( self , _lowercase , _lowercase=None , _lowercase=None , _lowercase=True , _lowercase=False , _lowercase=True , _lowercase=True , _lowercase=None , ) -> Union[str, Any]:
'''simple docstring'''
if image_path:
snake_case_ : Tuple = self._get_latent(_lowercase )
else:
snake_case_ : Dict = torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(_lowercase , _lowercase , _lowercase )
assert pos_prompts, "You must provide at least one positive prompt."
snake_case_ : Tuple = self.process_prompts(_lowercase )
snake_case_ : int = self.process_prompts(_lowercase )
if save_final and save_path is None:
snake_case_ : Optional[Any] = os.path.join("""./outputs/""" , """_""".join(pos_prompts["""prompts"""] ) )
if not os.path.exists(_lowercase ):
os.makedirs(_lowercase )
else:
snake_case_ : List[str] = save_path + """_""" + get_timestamp()
os.makedirs(_lowercase )
snake_case_ : List[str] = save_path
snake_case_ : Dict = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print("""Original Image""" )
show_pil(custom_to_pil(_lowercase ) )
snake_case_ : Tuple = loop_post_process(_lowercase )
for iter, transformed_img in enumerate(self._optimize_CLIP(_lowercase , _lowercase , _lowercase ) ):
if show_intermediate:
show_pil(_lowercase )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , f'iter_{iter:03d}.png' ) )
if self.log:
wandb.log({"""Image""": wandb.Image(_lowercase )} )
if show_final:
show_pil(_lowercase )
if save_final:
transformed_img.save(os.path.join(self.save_path , f'iter_{iter:03d}_final.png' ) )
| 58 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase : str = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=_UpperCAmelCase , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=_UpperCAmelCase , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=_UpperCAmelCase , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=_UpperCAmelCase , default='data/dump' , help='The dump file prefix.' )
lowerCAmelCase = parser.parse_args()
logger.info(F'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
lowerCAmelCase = BertTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
lowerCAmelCase = RobertaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `<s>`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
lowerCAmelCase = GPTaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
lowerCAmelCase = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(F'Loading text from {args.file_path}' )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
lowerCAmelCase = fp.readlines()
logger.info('Start encoding' )
logger.info(F'{len(_UpperCAmelCase )} examples to process.' )
lowerCAmelCase = []
lowerCAmelCase = 0
lowerCAmelCase = 1_0000
lowerCAmelCase = time.time()
for text in data:
lowerCAmelCase = F'{bos} {text.strip()} {sep}'
lowerCAmelCase = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
rslt.append(_UpperCAmelCase )
iter += 1
if iter % interval == 0:
lowerCAmelCase = time.time()
logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
lowerCAmelCase = time.time()
logger.info('Finished binarization' )
logger.info(F'{len(_UpperCAmelCase )} examples processed.' )
lowerCAmelCase = F'{args.dump_file}.{args.tokenizer_name}.pickle'
lowerCAmelCase = tokenizer.vocab_size
if vocab_size < (1 << 16):
lowerCAmelCase = [np.uintaa(_UpperCAmelCase ) for d in rslt]
else:
lowerCAmelCase = [np.intaa(_UpperCAmelCase ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F'Dump to {dp_file}' )
with open(_UpperCAmelCase , 'wb' ) as handle:
pickle.dump(rslt_ , _UpperCAmelCase , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 4 | 0 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__A = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
" final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
" depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
"The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
" accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
" founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
" body.",
"Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
" state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
" world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
" punishment.",
]
__A = [
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
" Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
" had informed his Lufthansa training school of an episode of severe depression, airline says .",
"Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
" Israel and the United States opposed the move, which could open the door to war crimes investigations against"
" Israelis .",
"Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
" death . Organization claims that governments around the world are using the threat of terrorism to advance"
" executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
" sentences up by 28% .",
]
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =calculate_rouge(__a , __a , bootstrap_aggregation=__a , rouge_keys=["rouge2", "rougeL"] )
assert isinstance(__a , __a )
lowerCamelCase__: List[Any] =calculate_rouge(__a , __a , bootstrap_aggregation=__a , rouge_keys=["rouge2"] )
assert (
pd.DataFrame(no_aggregation["rouge2"] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["rouge2"] ).fmeasure.mean()
)
def lowerCAmelCase_ ( ) -> List[str]:
"""simple docstring"""
lowerCamelCase__: Optional[int] ="rougeLsum"
lowerCamelCase__: Tuple =calculate_rouge(__a , __a , newline_sep=__a , rouge_keys=[k] )[k]
lowerCamelCase__: Optional[Any] =calculate_rouge(__a , __a , newline_sep=__a , rouge_keys=[k] )[k]
assert score > score_no_sep
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: List[Any] =["rouge1", "rouge2", "rougeL"]
lowerCamelCase__: int =calculate_rouge(__a , __a , newline_sep=__a , rouge_keys=__a )
lowerCamelCase__: List[str] =calculate_rouge(__a , __a , newline_sep=__a , rouge_keys=__a )
assert score_sep == score_no_sep
def lowerCAmelCase_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase__: Dict =[
"Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
]
lowerCamelCase__: int =[
"Margot Frank, died in 1945, a month earlier than previously thought.",
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
" the final seconds on board Flight 9525.",
]
assert calculate_rouge(__a , __a , newline_sep=__a ) == calculate_rouge(__a , __a , newline_sep=__a )
def lowerCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: List[str] =[
"\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
]
lowerCamelCase__: List[Any] =[
" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
]
lowerCamelCase__: Dict =calculate_rouge(__a , __a , rouge_keys=["rougeLsum"] , newline_sep=__a )["rougeLsum"]
lowerCamelCase__: Tuple =calculate_rouge(__a , __a , rouge_keys=["rougeLsum"] )["rougeLsum"]
assert new_score > prev_score
def lowerCAmelCase_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =Path("examples/seq2seq/test_data/wmt_en_ro" )
lowerCamelCase__: Union[str, Any] =calculate_rouge_path(data_dir.joinpath("test.source" ) , data_dir.joinpath("test.target" ) )
assert isinstance(__a , __a )
lowerCamelCase__: Dict =calculate_rouge_path(
data_dir.joinpath("test.source" ) , data_dir.joinpath("test.target" ) , bootstrap_aggregation=__a )
assert isinstance(__a , __a )
| 59 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
'''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
'''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
),
'''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''bert'''
def __init__( self , _snake_case=3_05_22 , _snake_case=7_68 , _snake_case=12 , _snake_case=12 , _snake_case=30_72 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , _snake_case=None , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
lowerCAmelCase = classifier_dropout
class a ( a__ ):
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 4 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowerCAmelCase_ = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(4_2)
lowerCAmelCase_ = {'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}
lowerCAmelCase_ = '''zero2'''
lowerCAmelCase_ = '''zero3'''
lowerCAmelCase_ = [ZEROa, ZEROa]
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Tuple = parameterized.to_safe_name('''_'''.join(str(_UpperCamelCase ) for x in param.args ) )
return f'''{func.__name__}_{param_based_name}'''
# Cartesian-product of zero stages with models to test
lowerCAmelCase_ = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class __lowerCAmelCase ( _a ):
@parameterized.expand(__magic_name__ , name_func=__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> Dict:
'''simple docstring'''
self.run_and_check(
stage=__magic_name__ , model=__magic_name__ , distributed=__magic_name__ , fpaa=__magic_name__ , )
@require_torch_multi_gpu
@parameterized.expand(__magic_name__ , name_func=__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> int:
'''simple docstring'''
self.run_and_check(
stage=__magic_name__ , model=__magic_name__ , distributed=__magic_name__ , fpaa=__magic_name__ , )
@parameterized.expand(__magic_name__ , name_func=__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
self.run_and_check(
stage=__magic_name__ , model=__magic_name__ , distributed=__magic_name__ , fpaa=__magic_name__ , )
@require_torch_multi_gpu
@parameterized.expand(__magic_name__ , name_func=__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
self.run_and_check(
stage=__magic_name__ , model=__magic_name__ , distributed=__magic_name__ , fpaa=__magic_name__ , )
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
pass
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ = 10 , __magic_name__ = True , __magic_name__ = True , __magic_name__ = True , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = models[model]
snake_case_ : Optional[int] = self.run_trainer(
stage=__magic_name__ , model_name=__magic_name__ , eval_steps=__magic_name__ , num_train_epochs=1 , distributed=__magic_name__ , fpaa=__magic_name__ , )
self.do_checks(__magic_name__ )
return output_dir
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ = 10 , __magic_name__ = 1 , __magic_name__ = True , __magic_name__ = True , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.get_auto_remove_tmp_dir('''./xxx''' , after=__magic_name__ )
snake_case_ : Optional[Any] = F'''
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(__magic_name__ )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
'''.split()
if fpaa:
args.extend(['''--fp16'''] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
snake_case_ : Tuple = F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
snake_case_ : Dict = [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
snake_case_ : Optional[int] = self.get_launcher(__magic_name__ )
snake_case_ : List[Any] = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(__magic_name__ , env=self.get_env() )
return output_dir
def lowerCamelCase (self , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = min(2 , get_gpu_count() ) if distributed else 1
return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()
| 60 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class a ( a__ , unittest.TestCase ):
snake_case__ = DanceDiffusionPipeline
snake_case__ = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
snake_case__ = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
snake_case__ = False
snake_case__ = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=5_12 , sample_rate=1_60_00 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_snake_case , use_timestep_embedding=_snake_case , time_embedding_type='fourier' , mid_block_type='UNetMidBlock1D' , down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') , up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') , )
lowerCAmelCase = IPNDMScheduler()
lowerCAmelCase = {
'unet': unet,
'scheduler': scheduler,
}
return components
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 4,
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = DanceDiffusionPipeline(**_snake_case )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = self.get_dummy_inputs(_snake_case )
lowerCAmelCase = pipe(**_snake_case )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
lowerCAmelCase = np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_attention_slicing_forward_pass()
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = torch_device
lowerCAmelCase = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(generator=_snake_case , num_inference_steps=1_00 , audio_length_in_s=4.096 )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase = np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = torch_device
lowerCAmelCase = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' , torch_dtype=torch.floataa )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(generator=_snake_case , num_inference_steps=1_00 , audio_length_in_s=4.096 )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase = np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 4 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
UpperCamelCase = True
except ImportError:
UpperCamelCase = False
UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
def _A ( lowerCAmelCase_ : Namespace ):
"""simple docstring"""
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
@staticmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> int:
lowerCAmelCase__ = parser.add_parser("add-new-model" )
add_new_model_parser.add_argument("--testing" , action="store_true" , help="If in testing mode." )
add_new_model_parser.add_argument("--testing_file" , type=SCREAMING_SNAKE_CASE__ , help="Configuration file on which to run." )
add_new_model_parser.add_argument(
"--path" , type=SCREAMING_SNAKE_CASE__ , help="Path to cookiecutter. Should only be used for testing purposes." )
add_new_model_parser.set_defaults(func=SCREAMING_SNAKE_CASE__ )
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : bool , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , *SCREAMING_SNAKE_CASE__ : Tuple ) -> List[Any]:
lowerCAmelCase__ = testing
lowerCAmelCase__ = testing_file
lowerCAmelCase__ = path
def a ( self : Union[str, Any] ) -> Tuple:
warnings.warn(
"The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
"It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
"checks, you should use `transformers-cli add-new-model-like` instead." )
if not _has_cookiecutter:
raise ImportError(
"Model creation dependencies are required to use the `add_new_model` command. Install them by running "
"the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
lowerCAmelCase__ = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
if len(SCREAMING_SNAKE_CASE__ ) > 0:
raise ValueError(
"Several directories starting with `cookiecutter-template-` in current working directory. "
"Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
"change your working directory." )
lowerCAmelCase__ = (
Path(SCREAMING_SNAKE_CASE__ ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
lowerCAmelCase__ = path_to_transformer_root / "templates" / "adding_a_new_model"
# Execute cookiecutter
if not self._testing:
cookiecutter(str(SCREAMING_SNAKE_CASE__ ) )
else:
with open(self._testing_file , "r" ) as configuration_file:
lowerCAmelCase__ = json.load(SCREAMING_SNAKE_CASE__ )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=SCREAMING_SNAKE_CASE__ , extra_context=SCREAMING_SNAKE_CASE__ , )
lowerCAmelCase__ = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]
# Retrieve configuration
with open(directory + "/configuration.json" , "r" ) as configuration_file:
lowerCAmelCase__ = json.load(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = configuration["lowercase_modelname"]
lowerCAmelCase__ = configuration["generate_tensorflow_pytorch_and_flax"]
os.remove(f'{directory}/configuration.json' )
lowerCAmelCase__ = "PyTorch" in generate_tensorflow_pytorch_and_flax
lowerCAmelCase__ = "TensorFlow" in generate_tensorflow_pytorch_and_flax
lowerCAmelCase__ = "Flax" in generate_tensorflow_pytorch_and_flax
lowerCAmelCase__ = f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}' , exist_ok=SCREAMING_SNAKE_CASE__ )
# Tests require submodules as they have parent imports
with open(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' , "w" ):
pass
shutil.move(
f'{directory}/__init__.py' , f'{model_dir}/__init__.py' , )
shutil.move(
f'{directory}/configuration_{lowercase_model_name}.py' , f'{model_dir}/configuration_{lowercase_model_name}.py' , )
def remove_copy_lines(SCREAMING_SNAKE_CASE__ : Dict ):
with open(SCREAMING_SNAKE_CASE__ , "r" ) as f:
lowerCAmelCase__ = f.readlines()
with open(SCREAMING_SNAKE_CASE__ , "w" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(SCREAMING_SNAKE_CASE__ )
if output_pytorch:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_{lowercase_model_name}.py' , f'{model_dir}/modeling_{lowercase_model_name}.py' , )
shutil.move(
f'{directory}/test_modeling_{lowercase_model_name}.py' , f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' , )
else:
os.remove(f'{directory}/modeling_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_{lowercase_model_name}.py' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_tf_{lowercase_model_name}.py' , f'{model_dir}/modeling_tf_{lowercase_model_name}.py' , )
shutil.move(
f'{directory}/test_modeling_tf_{lowercase_model_name}.py' , f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' , )
else:
os.remove(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_tf_{lowercase_model_name}.py' )
if output_flax:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_flax_{lowercase_model_name}.py' , f'{model_dir}/modeling_flax_{lowercase_model_name}.py' , )
shutil.move(
f'{directory}/test_modeling_flax_{lowercase_model_name}.py' , f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' , )
else:
os.remove(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/{lowercase_model_name}.md' , f'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' , )
shutil.move(
f'{directory}/tokenization_{lowercase_model_name}.py' , f'{model_dir}/tokenization_{lowercase_model_name}.py' , )
shutil.move(
f'{directory}/tokenization_fast_{lowercase_model_name}.py' , f'{model_dir}/tokenization_{lowercase_model_name}_fast.py' , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[str] ):
# Create temp file
lowerCAmelCase__ , lowerCAmelCase__ = mkstemp()
lowerCAmelCase__ = False
with fdopen(SCREAMING_SNAKE_CASE__ , "w" ) as new_file:
with open(SCREAMING_SNAKE_CASE__ ) as old_file:
for line in old_file:
new_file.write(SCREAMING_SNAKE_CASE__ )
if line_to_copy_below in line:
lowerCAmelCase__ = True
for line_to_copy in lines_to_copy:
new_file.write(SCREAMING_SNAKE_CASE__ )
if not line_found:
raise ValueError(f'Line {line_to_copy_below} was not found in file.' )
# Copy the file permissions from the old file to the new file
copymode(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Remove original file
remove(SCREAMING_SNAKE_CASE__ )
# Move new file
move(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def skip_units(SCREAMING_SNAKE_CASE__ : List[Any] ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(SCREAMING_SNAKE_CASE__ : List[Any] ):
with open(SCREAMING_SNAKE_CASE__ ) as datafile:
lowerCAmelCase__ = []
lowerCAmelCase__ = False
lowerCAmelCase__ = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
lowerCAmelCase__ = line.split("\"" )[1]
lowerCAmelCase__ = skip_units(SCREAMING_SNAKE_CASE__ )
elif "# Below: " in line and "##" not in line:
lowerCAmelCase__ = line.split("\"" )[1]
lowerCAmelCase__ = skip_units(SCREAMING_SNAKE_CASE__ )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = []
elif "# Replace with" in line and "##" not in line:
lowerCAmelCase__ = []
elif "##" not in line:
lines_to_copy.append(SCREAMING_SNAKE_CASE__ )
remove(SCREAMING_SNAKE_CASE__ )
replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py' )
os.rmdir(SCREAMING_SNAKE_CASE__ )
| 61 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=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 UpperCamelCase__ ( 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 UpperCamelCase__ ( 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=_snake_case , initializer_range=self.initializer_range , use_stable_embedding=_snake_case , )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = OpenLlamaModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , )
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , )
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
# first forward pass
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , use_cache=_snake_case , )
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(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , output_hidden_states=_snake_case , )['hidden_states'][0]
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , past_key_values=_snake_case , output_hidden_states=_snake_case , )['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(_snake_case , _snake_case , atol=1E-3 ) )
def UpperCamelCase__ ( 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 ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
snake_case__ = (OpenLlamaForCausalLM,) if is_torch_available() else ()
snake_case__ = (
{
'''feature-extraction''': OpenLlamaModel,
'''text-classification''': OpenLlamaForSequenceClassification,
'''text-generation''': OpenLlamaForCausalLM,
'''zero-shot''': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case__ = False
snake_case__ = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( 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(*_snake_case )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
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 UpperCamelCase__ ( self ):
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase ,lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = ids_tensor([1, 10] , config.vocab_size )
lowerCAmelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase = OpenLlamaModel(_snake_case )
original_model.to(_snake_case )
original_model.eval()
lowerCAmelCase = original_model(_snake_case ).last_hidden_state
lowerCAmelCase = original_model(_snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase = {'type': scaling_type, 'factor': 10.0}
lowerCAmelCase = OpenLlamaModel(_snake_case )
scaled_model.to(_snake_case )
scaled_model.eval()
lowerCAmelCase = scaled_model(_snake_case ).last_hidden_state
lowerCAmelCase = scaled_model(_snake_case ).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(_snake_case , _snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1E-5 ) )
| 4 | 0 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
snake_case = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = '''sequence-classification'''
def __init__( self : Optional[int] , UpperCAmelCase_ : Tuple ):
if type(UpperCAmelCase_ ) == dict:
SCREAMING_SNAKE_CASE : List[str] = Namespace(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = glue_output_modes[hparams.task]
SCREAMING_SNAKE_CASE : Union[str, Any] = glue_tasks_num_labels[hparams.task]
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ , self.mode )
def _A ( self : List[str] , **UpperCAmelCase_ : str ):
return self.model(**UpperCAmelCase_ )
def _A ( self : int , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any] ):
SCREAMING_SNAKE_CASE : Any = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
SCREAMING_SNAKE_CASE : Union[str, Any] = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
SCREAMING_SNAKE_CASE : Union[str, Any] = self(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = outputs[0]
SCREAMING_SNAKE_CASE : List[Any] = self.trainer.lr_schedulers[0]["scheduler"]
SCREAMING_SNAKE_CASE : List[str] = {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def _A ( self : List[str] ):
SCREAMING_SNAKE_CASE : List[Any] = self.hparams
SCREAMING_SNAKE_CASE : Tuple = processors[args.task]()
SCREAMING_SNAKE_CASE : Optional[int] = processor.get_labels()
for mode in ["train", "dev"]:
SCREAMING_SNAKE_CASE : str = self._feature_file(UpperCAmelCase_ )
if os.path.exists(UpperCAmelCase_ ) and not args.overwrite_cache:
logger.info("Loading features from cached file %s" , UpperCAmelCase_ )
else:
logger.info("Creating features from dataset file at %s" , args.data_dir )
SCREAMING_SNAKE_CASE : Union[str, Any] = (
processor.get_dev_examples(args.data_dir )
if mode == "dev"
else processor.get_train_examples(args.data_dir )
)
SCREAMING_SNAKE_CASE : List[str] = convert_examples_to_features(
UpperCAmelCase_ , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info("Saving features into cached file %s" , UpperCAmelCase_ )
torch.save(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Optional[int] , UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : bool = False ):
SCREAMING_SNAKE_CASE : Dict = "dev" if mode == "test" else mode
SCREAMING_SNAKE_CASE : List[Any] = self._feature_file(UpperCAmelCase_ )
logger.info("Loading features from cached file %s" , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = torch.load(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
SCREAMING_SNAKE_CASE : Dict = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ) , batch_size=UpperCAmelCase_ , shuffle=UpperCAmelCase_ , )
def _A ( self : str , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : Dict = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
SCREAMING_SNAKE_CASE : List[str] = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
SCREAMING_SNAKE_CASE : str = self(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = outputs[:2]
SCREAMING_SNAKE_CASE : Union[str, Any] = logits.detach().cpu().numpy()
SCREAMING_SNAKE_CASE : Optional[int] = inputs["labels"].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _A ( self : List[Any] , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.stack([x["val_loss"] for x in outputs] ).mean().detach().cpu().item()
SCREAMING_SNAKE_CASE : Tuple = np.concatenate([x["pred"] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
SCREAMING_SNAKE_CASE : List[str] = np.argmax(UpperCAmelCase_ , axis=1 )
elif self.hparams.glue_output_mode == "regression":
SCREAMING_SNAKE_CASE : str = np.squeeze(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.concatenate([x["target"] for x in outputs] , axis=0 )
SCREAMING_SNAKE_CASE : Dict = [[] for _ in range(out_label_ids.shape[0] )]
SCREAMING_SNAKE_CASE : List[Any] = [[] for _ in range(out_label_ids.shape[0] )]
SCREAMING_SNAKE_CASE : Any = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task , UpperCAmelCase_ , UpperCAmelCase_ )}
SCREAMING_SNAKE_CASE : Union[str, Any] = dict(results.items() )
SCREAMING_SNAKE_CASE : Optional[Any] = results
return ret, preds_list, out_label_list
def _A ( self : Union[str, Any] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = self._eval_end(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = ret["log"]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _A ( self : List[str] , UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = self._eval_end(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = ret["log"]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _A ( UpperCAmelCase_ : str , UpperCAmelCase_ : int ):
BaseTransformer.add_model_specific_args(UpperCAmelCase_ , UpperCAmelCase_ )
parser.add_argument(
"--max_seq_length" , default=128 , type=UpperCAmelCase_ , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--task" , default="" , type=UpperCAmelCase_ , required=UpperCAmelCase_ , help="The GLUE task to run" , )
parser.add_argument(
"--gpus" , default=0 , type=UpperCAmelCase_ , help="The number of GPUs allocated for this, it is by default 0 meaning none" , )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
return parser
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = argparse.ArgumentParser()
add_generic_args(lowercase , os.getcwd() )
SCREAMING_SNAKE_CASE : int = GLUETransformer.add_model_specific_args(lowercase , os.getcwd() )
SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
"./results" , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
SCREAMING_SNAKE_CASE : Union[str, Any] = GLUETransformer(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = generic_train(lowercase , lowercase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
SCREAMING_SNAKE_CASE : Optional[Any] = sorted(glob.glob(os.path.join(args.output_dir , "checkpoint-epoch=*.ckpt" ) , recursive=lowercase ) )
SCREAMING_SNAKE_CASE : Dict = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(lowercase )
if __name__ == "__main__":
main()
| 62 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = data
lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return F'Node({self.data})'
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = None
def __iter__( self ):
"""simple docstring"""
lowerCAmelCase = self.head
while node:
yield node.data
lowerCAmelCase = node.next
def __len__( self ):
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self ):
"""simple docstring"""
return "->".join([str(_snake_case ) for item in self] )
def __getitem__( self , _snake_case ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , _snake_case , _snake_case ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
lowerCAmelCase = self.head
for _ in range(_snake_case ):
lowerCAmelCase = current.next
lowerCAmelCase = data
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
self.insert_nth(len(self ) , _snake_case )
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
self.insert_nth(0 , _snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
if not 0 <= index <= len(self ):
raise IndexError('list index out of range' )
lowerCAmelCase = Node(_snake_case )
if self.head is None:
lowerCAmelCase = new_node
elif index == 0:
lowerCAmelCase = self.head # link new_node to head
lowerCAmelCase = new_node
else:
lowerCAmelCase = self.head
for _ in range(index - 1 ):
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next
lowerCAmelCase = new_node
def UpperCamelCase__ ( self ): # print every node data
"""simple docstring"""
print(self )
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.delete_nth(0 )
def UpperCamelCase__ ( self ): # delete from tail
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def UpperCamelCase__ ( self , _snake_case = 0 ):
"""simple docstring"""
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError('List index out of range.' )
lowerCAmelCase = self.head # default first node
if index == 0:
lowerCAmelCase = self.head.next
else:
lowerCAmelCase = self.head
for _ in range(index - 1 ):
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next.next
return delete_node.data
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.head is None
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = None
lowerCAmelCase = self.head
while current:
# Store the current node's next node.
lowerCAmelCase = current.next
# Make the current node's next point backwards
lowerCAmelCase = prev
# Make the previous node be the current node
lowerCAmelCase = current
# Make the current node the next node (to progress iteration)
lowerCAmelCase = next_node
# Return prev in order to put the head at the end
lowerCAmelCase = prev
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = LinkedList()
assert linked_list.is_empty() is True
assert str(_UpperCAmelCase ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(_UpperCAmelCase ) == i
linked_list.insert_nth(_UpperCAmelCase , i + 1 )
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(_UpperCAmelCase ) == 9
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
lowerCAmelCase = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(-8 , 1 ) )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = [
-9,
100,
Node(7734_5112 ),
'dlrow olleH',
7,
5555,
0,
-192.5_5555,
'Hello, world!',
77.9,
Node(10 ),
None,
None,
12.20,
]
lowerCAmelCase = LinkedList()
for i in test_input:
linked_list.insert_tail(_UpperCAmelCase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(_UpperCAmelCase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
lowerCAmelCase = linked_list.delete_head()
assert result == -9
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
lowerCAmelCase = linked_list.delete_tail()
assert result == 12.2
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
lowerCAmelCase = linked_list.delete_nth(10 )
assert result is None
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node('Hello again, world!' ) )
assert (
str(_UpperCAmelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(_UpperCAmelCase )
assert (
str(_UpperCAmelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(_UpperCAmelCase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def _SCREAMING_SNAKE_CASE ():
from doctest import testmod
testmod()
lowerCAmelCase = LinkedList()
linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
print('\nDelete head' )
linked_list.delete_head()
print('Delete tail' )
linked_list.delete_tail()
print('\nPrint list:' )
linked_list.print_list()
print('\nReverse linked list' )
linked_list.reverse()
print('\nPrint list:' )
linked_list.print_list()
print('\nString representation of linked list:' )
print(_UpperCAmelCase )
print('\nReading/changing Node data using indexing:' )
print(F'Element at Position 1: {linked_list[1]}' )
lowerCAmelCase = input('Enter New Value: ' ).strip()
print('New list:' )
print(_UpperCAmelCase )
print(F'length of linked_list is : {len(_UpperCAmelCase )}' )
if __name__ == "__main__":
main()
| 4 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class a ( lowercase__ ):
"""simple docstring"""
a : Union[str, Any] = 'timesformer'
def __init__( self : List[Any] , __lowercase : Optional[Any]=224 , __lowercase : List[Any]=16 , __lowercase : int=3 , __lowercase : Tuple=8 , __lowercase : str=768 , __lowercase : List[Any]=12 , __lowercase : List[Any]=12 , __lowercase : Dict=3072 , __lowercase : Dict="gelu" , __lowercase : Dict=0.0 , __lowercase : Dict=0.0 , __lowercase : int=0.02 , __lowercase : List[str]=1e-6 , __lowercase : Tuple=True , __lowercase : Tuple="divided_space_time" , __lowercase : Any=0 , **__lowercase : List[Any] , ) -> Tuple:
super().__init__(**__lowercase )
__UpperCAmelCase : Optional[int] = image_size
__UpperCAmelCase : Dict = patch_size
__UpperCAmelCase : Dict = num_channels
__UpperCAmelCase : str = num_frames
__UpperCAmelCase : Optional[int] = hidden_size
__UpperCAmelCase : Union[str, Any] = num_hidden_layers
__UpperCAmelCase : Dict = num_attention_heads
__UpperCAmelCase : Optional[Any] = intermediate_size
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : List[str] = hidden_dropout_prob
__UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
__UpperCAmelCase : Union[str, Any] = initializer_range
__UpperCAmelCase : Any = layer_norm_eps
__UpperCAmelCase : List[Any] = qkv_bias
__UpperCAmelCase : Union[str, Any] = attention_type
__UpperCAmelCase : Dict = drop_path_rate
| 63 |
"""simple docstring"""
from __future__ import annotations
import requests
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(_UpperCAmelCase ).json()
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 10 ):
lowerCAmelCase = 'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
lowerCAmelCase = requests.get(_UpperCAmelCase ).json()[:max_stories]
return [get_hackernews_story(_UpperCAmelCase ) for story_id in story_ids]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 10 ):
lowerCAmelCase = hackernews_top_stories(_UpperCAmelCase )
return "\n".join('* [{title}]({url})'.format(**_UpperCAmelCase ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 4 | 0 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
logging.set_verbosity_info()
def A__ ( snake_case_ : str , snake_case_ : str ):
if "xprophetnet" in prophetnet_checkpoint_path:
SCREAMING_SNAKE_CASE__: Dict= XLMProphetNetForConditionalGenerationOld.from_pretrained(snake_case_ )
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Any= XLMProphetNetForConditionalGeneration.from_pretrained(
snake_case_ , output_loading_info=snake_case_ )
else:
SCREAMING_SNAKE_CASE__: int= ProphetNetForConditionalGenerationOld.from_pretrained(snake_case_ )
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Dict= ProphetNetForConditionalGeneration.from_pretrained(
snake_case_ , output_loading_info=snake_case_ )
SCREAMING_SNAKE_CASE__: Tuple= ['''key_proj''', '''value_proj''', '''query_proj''']
SCREAMING_SNAKE_CASE__: Optional[int]= {
'''self_attn''': '''ngram_self_attn''',
'''cross_attn''': '''encoder_attn''',
'''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
'''feed_forward_layer_norm''': '''final_layer_norm''',
'''feed_forward''': '''''',
'''intermediate''': '''fc1''',
'''output''': '''fc2''',
'''key_proj''': '''k_proj''',
'''query_proj''': '''q_proj''',
'''value_proj''': '''v_proj''',
'''word_embeddings''': '''embed_tokens''',
'''embeddings_layer_norm''': '''emb_layer_norm''',
'''relative_pos_embeddings''': '''relative_linear''',
'''ngram_embeddings''': '''ngram_input_embed''',
'''position_embeddings''': '''embed_positions''',
}
for key in loading_info["missing_keys"]:
SCREAMING_SNAKE_CASE__: Optional[int]= key.split('''.''' )
if attributes[0] == "lm_head":
SCREAMING_SNAKE_CASE__: List[str]= prophet
SCREAMING_SNAKE_CASE__: List[Any]= prophet_old
else:
SCREAMING_SNAKE_CASE__: List[Any]= prophet.prophetnet
SCREAMING_SNAKE_CASE__: Dict= prophet_old.model
SCREAMING_SNAKE_CASE__: Tuple= False
for attribute in attributes:
if attribute in mapping:
SCREAMING_SNAKE_CASE__: Optional[Any]= mapping[attribute]
if not hasattr(snake_case_ , snake_case_ ) and len(snake_case_ ) > 0:
SCREAMING_SNAKE_CASE__: Optional[int]= attribute
elif hasattr(snake_case_ , snake_case_ ):
SCREAMING_SNAKE_CASE__: Optional[int]= attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
SCREAMING_SNAKE_CASE__: Tuple= old_model.weight
logger.info(F'{attribute} is initialized.' )
SCREAMING_SNAKE_CASE__: Optional[Any]= True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
SCREAMING_SNAKE_CASE__: Dict= old_model.bias
logger.info(F'{attribute} is initialized' )
SCREAMING_SNAKE_CASE__: Tuple= True
break
elif attribute in special_keys and hasattr(snake_case_ , '''in_proj_weight''' ):
SCREAMING_SNAKE_CASE__: Any= old_model.in_proj_weight.shape[0] // 3
SCREAMING_SNAKE_CASE__: List[str]= getattr(snake_case_ , snake_case_ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
SCREAMING_SNAKE_CASE__: Any= nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
SCREAMING_SNAKE_CASE__: Optional[Any]= nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
SCREAMING_SNAKE_CASE__: List[str]= nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
SCREAMING_SNAKE_CASE__: Dict= nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
SCREAMING_SNAKE_CASE__: Union[str, Any]= nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
SCREAMING_SNAKE_CASE__: str= nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
SCREAMING_SNAKE_CASE__: Union[str, Any]= True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
SCREAMING_SNAKE_CASE__: Dict= nn.Parameter(old_model.embed_positions.weight[:512, :] )
SCREAMING_SNAKE_CASE__: Any= True
break
if attribute.isdigit():
SCREAMING_SNAKE_CASE__: Tuple= model[int(snake_case_ )]
SCREAMING_SNAKE_CASE__: Tuple= old_model[int(snake_case_ )]
else:
SCREAMING_SNAKE_CASE__: List[str]= getattr(snake_case_ , snake_case_ )
if old_attribute == "":
SCREAMING_SNAKE_CASE__: List[Any]= old_model
else:
if not hasattr(snake_case_ , snake_case_ ):
raise ValueError(F'{old_model} does not have {old_attribute}' )
SCREAMING_SNAKE_CASE__: int= getattr(snake_case_ , snake_case_ )
if not is_key_init:
raise ValueError(F'{key} was not correctly initialized!' )
print(F'Saving model to {pytorch_dump_folder_path}' )
prophet.save_pretrained(snake_case_ )
if __name__ == "__main__":
lowercase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
lowercase_ : str = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 64 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any ):
lowerCAmelCase = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCAmelCase = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
lowerCAmelCase = 4
lowerCAmelCase = 48
lowerCAmelCase = 'pixelshuffle_aux'
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCAmelCase = [6, 6, 6, 6]
lowerCAmelCase = 60
lowerCAmelCase = [6, 6, 6, 6]
lowerCAmelCase = 'pixelshuffledirect'
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCAmelCase = 4
lowerCAmelCase = 'nearest+conv'
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
lowerCAmelCase = 1
lowerCAmelCase = 1
lowerCAmelCase = 126
lowerCAmelCase = 7
lowerCAmelCase = 255.0
lowerCAmelCase = ''
return config
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] ):
if "patch_embed.proj" in name and "layers" not in name:
lowerCAmelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
lowerCAmelCase = name.replace('patch_embed.norm' , 'embeddings.patch_embeddings.layernorm' )
if "layers" in name:
lowerCAmelCase = name.replace('layers' , 'encoder.stages' )
if "residual_group.blocks" in name:
lowerCAmelCase = name.replace('residual_group.blocks' , 'layers' )
if "attn.proj" in name:
lowerCAmelCase = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
lowerCAmelCase = name.replace('attn' , 'attention.self' )
if "norm1" in name:
lowerCAmelCase = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowerCAmelCase = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowerCAmelCase = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCAmelCase = name.replace('mlp.fc2' , 'output.dense' )
if "q_bias" in name:
lowerCAmelCase = name.replace('q_bias' , 'query.bias' )
if "k_bias" in name:
lowerCAmelCase = name.replace('k_bias' , 'key.bias' )
if "v_bias" in name:
lowerCAmelCase = name.replace('v_bias' , 'value.bias' )
if "cpb_mlp" in name:
lowerCAmelCase = name.replace('cpb_mlp' , 'continuous_position_bias_mlp' )
if "patch_embed.proj" in name:
lowerCAmelCase = name.replace('patch_embed.proj' , 'patch_embed.projection' )
if name == "norm.weight":
lowerCAmelCase = 'layernorm.weight'
if name == "norm.bias":
lowerCAmelCase = 'layernorm.bias'
if "conv_first" in name:
lowerCAmelCase = name.replace('conv_first' , 'first_convolution' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
lowerCAmelCase = name.replace('conv_last' , 'final_convolution' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
lowerCAmelCase = name.replace('conv_before_upsample.0' , 'conv_before_upsample' )
if "upsample.0" in name:
lowerCAmelCase = name.replace('upsample.0' , 'upsample.convolution_0' )
if "upsample.2" in name:
lowerCAmelCase = name.replace('upsample.2' , 'upsample.convolution_1' )
lowerCAmelCase = 'upsample.' + name
elif config.upsampler == "pixelshuffledirect":
lowerCAmelCase = name.replace('upsample.0.weight' , 'upsample.conv.weight' )
lowerCAmelCase = name.replace('upsample.0.bias' , 'upsample.conv.bias' )
else:
pass
else:
lowerCAmelCase = 'swin2sr.' + name
return name
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict ):
for key in orig_state_dict.copy().keys():
lowerCAmelCase = orig_state_dict.pop(_UpperCAmelCase )
if "qkv" in key:
lowerCAmelCase = key.split('.' )
lowerCAmelCase = int(key_split[1] )
lowerCAmelCase = int(key_split[4] )
lowerCAmelCase = config.embed_dim
if "weight" in key:
lowerCAmelCase = val[:dim, :]
lowerCAmelCase = val[dim : dim * 2, :]
lowerCAmelCase = val[-dim:, :]
else:
lowerCAmelCase = val[:dim]
lowerCAmelCase = val[dim : dim * 2]
lowerCAmelCase = val[-dim:]
pass
else:
lowerCAmelCase = val
return orig_state_dict
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple ):
lowerCAmelCase = get_config(_UpperCAmelCase )
lowerCAmelCase = SwinaSRForImageSuperResolution(_UpperCAmelCase )
model.eval()
lowerCAmelCase = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location='cpu' )
lowerCAmelCase = convert_state_dict(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase ,lowerCAmelCase = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
raise ValueError('Missing keys when converting: {}'.format(_UpperCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F'Unexpected key {key} in state_dict' )
# verify values
lowerCAmelCase = 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
lowerCAmelCase = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('RGB' )
lowerCAmelCase = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
lowerCAmelCase = 126 if 'Jpeg' in checkpoint_url else 256
lowerCAmelCase = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
lowerCAmelCase = transforms(_UpperCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
lowerCAmelCase = pixel_values[:, 0, :, :].unsqueeze(1 )
lowerCAmelCase = model(_UpperCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 512, 512] )
lowerCAmelCase = torch.tensor(
[[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 512, 512] )
lowerCAmelCase = torch.tensor(
[[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] )
assert (
outputs.reconstruction.shape == expected_shape
), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , _UpperCAmelCase , atol=1e-3 )
print('Looks ok!' )
lowerCAmelCase = {
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
'swin2SR-classical-sr-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
'swin2SR-classical-sr-x4-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
'swin2SR-compressed-sr-x4-48'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
'swin2SR-lightweight-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
),
}
lowerCAmelCase = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_UpperCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
model.push_to_hub(F'caidas/{model_name}' )
processor.push_to_hub(F'caidas/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
type=str,
help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 4 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaControlnetPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
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 __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : int ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.time_input_dim
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowercase ( self : Any ):
'''simple docstring'''
return 100
@property
def __lowercase ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : Tuple = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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__ : int = UNetaDConditionModel(**A )
return model
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : str = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.dummy_unet
UpperCAmelCase__ : List[Any] = self.dummy_movq
UpperCAmelCase__ : List[Any] = 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=A ,set_alpha_to_one=A ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=A ,)
UpperCAmelCase__ : Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def __lowercase ( self : str ,A : Optional[Any] ,A : Any=0 ):
'''simple docstring'''
UpperCAmelCase__ : str = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(A ) ).to(A )
UpperCAmelCase__ : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
A )
# create hint
UpperCAmelCase__ : int = floats_tensor((1, 3, 64, 64) ,rng=random.Random(A ) ).to(A )
if str(A ).startswith("""mps""" ):
UpperCAmelCase__ : Optional[int] = torch.manual_seed(A )
else:
UpperCAmelCase__ : Dict = torch.Generator(device=A ).manual_seed(A )
UpperCAmelCase__ : Dict = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """cpu"""
UpperCAmelCase__ : List[Any] = self.get_dummy_components()
UpperCAmelCase__ : Union[str, Any] = self.pipeline_class(**A )
UpperCAmelCase__ : Optional[int] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[int] = pipe(**self.get_dummy_inputs(A ) )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = pipe(
**self.get_dummy_inputs(A ) ,return_dict=A ,)[0]
UpperCAmelCase__ : Tuple = image[0, -3:, -3:, -1]
UpperCAmelCase__ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase__ : Optional[int] = np.array(
[0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] )
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 __lowercase ( unittest.TestCase ):
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
UpperCAmelCase__ : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
UpperCAmelCase__ : int = torch.from_numpy(np.array(A ) ).float() / 2_5_5.0
UpperCAmelCase__ : Union[str, Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
UpperCAmelCase__ : List[str] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(A )
UpperCAmelCase__ : List[Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
UpperCAmelCase__ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[Any] = """A robot, 4k photo"""
UpperCAmelCase__ : List[Any] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = pipe_prior(
A ,generator=A ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
UpperCAmelCase__ : List[str] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ : int = pipeline(
image_embeds=A ,negative_image_embeds=A ,hint=A ,generator=A ,num_inference_steps=100 ,output_type="""np""" ,)
UpperCAmelCase__ : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(A ,A )
| 65 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''megatron-bert'''
def __init__( self , _snake_case=2_90_56 , _snake_case=10_24 , _snake_case=24 , _snake_case=16 , _snake_case=40_96 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
| 4 | 0 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
UpperCamelCase = "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))
print("Googling.....")
UpperCamelCase = f'''https://www.google.com/search?q={query}&num=100'''
UpperCamelCase = requests.get(
url,
headers={"User-Agent": str(UserAgent().random)},
)
try:
UpperCamelCase = (
BeautifulSoup(res.text, "html.parser")
.find("div", attrs={"class": "yuRUbf"})
.find("a")
.get("href")
)
except AttributeError:
UpperCamelCase = parse_qs(
BeautifulSoup(res.text, "html.parser")
.find("div", attrs={"class": "kCrYT"})
.find("a")
.get("href")
)["url"][0]
webbrowser.open(link)
| 66 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case__ :Dict , snake_case__ :int , snake_case__ :List[str] , snake_case__ :int ) -> int: # noqa: E741
while r - l > 1:
_lowercase = (l + r) // 2
if v[m] >= key:
_lowercase = m
else:
_lowercase = m # noqa: E741
return r
def SCREAMING_SNAKE_CASE__ ( snake_case__ :list[int] ) -> int:
if len(snake_case__ ) == 0:
return 0
_lowercase = [0] * len(snake_case__ )
_lowercase = 1
_lowercase = v[0]
for i in range(1 , len(snake_case__ ) ):
if v[i] < tail[0]:
_lowercase = v[i]
elif v[i] > tail[length - 1]:
_lowercase = v[i]
length += 1
else:
_lowercase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod() | 67 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class a ( a__ ):
snake_case__ = 42
class a ( a__ , a__ ):
@register_to_config
def __init__( self , _snake_case = 3 , _snake_case = 3 , _snake_case = ("DownEncoderBlock2D",) , _snake_case = ("UpDecoderBlock2D",) , _snake_case = (64,) , _snake_case = 1 , _snake_case = "silu" , _snake_case = 3 , _snake_case = 32 , _snake_case = 2_56 , _snake_case = 32 , _snake_case = None , _snake_case = 0.18_215 , _snake_case = "group" , ):
"""simple docstring"""
super().__init__()
# pass init params to Encoder
lowerCAmelCase = Encoder(
in_channels=_snake_case , out_channels=_snake_case , down_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , double_z=_snake_case , )
lowerCAmelCase = vq_embed_dim if vq_embed_dim is not None else latent_channels
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
lowerCAmelCase = VectorQuantizer(_snake_case , _snake_case , beta=0.25 , remap=_snake_case , sane_index_shape=_snake_case )
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
# pass init params to Decoder
lowerCAmelCase = Decoder(
in_channels=_snake_case , out_channels=_snake_case , up_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , norm_type=_snake_case , )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = self.encoder(_snake_case )
lowerCAmelCase = self.quant_conv(_snake_case )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_snake_case )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = False , _snake_case = True ):
"""simple docstring"""
if not force_not_quantize:
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = self.quantize(_snake_case )
else:
lowerCAmelCase = h
lowerCAmelCase = self.post_quant_conv(_snake_case )
lowerCAmelCase = self.decoder(_snake_case , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = sample
lowerCAmelCase = self.encode(_snake_case ).latents
lowerCAmelCase = self.decode(_snake_case ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
| 4 | 0 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__A = "scheduler_config.json"
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : str = 1
lowerCamelCase : int = 2
lowerCamelCase : Any = 3
lowerCamelCase : Tuple = 4
lowerCamelCase : Dict = 5
lowerCamelCase : Optional[int] = 6
lowerCamelCase : Optional[Any] = 7
lowerCamelCase : Union[str, Any] = 8
lowerCamelCase : str = 9
lowerCamelCase : Union[str, Any] = 10
lowerCamelCase : Tuple = 11
lowerCamelCase : Dict = 12
lowerCamelCase : int = 13
lowerCamelCase : List[Any] = 14
@dataclass
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : torch.FloatTensor
class _A :
"""simple docstring"""
lowerCamelCase : Optional[Any] = SCHEDULER_CONFIG_NAME
lowerCamelCase : Dict = []
lowerCamelCase : Any = True
@classmethod
def _a ( cls : Any , __SCREAMING_SNAKE_CASE : Dict[str, Any] = None , __SCREAMING_SNAKE_CASE : Optional[str] = None , __SCREAMING_SNAKE_CASE : Dict=False , **__SCREAMING_SNAKE_CASE : Tuple , ) -> Any:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase =cls.load_config(
pretrained_model_name_or_path=__SCREAMING_SNAKE_CASE , subfolder=__SCREAMING_SNAKE_CASE , return_unused_kwargs=__SCREAMING_SNAKE_CASE , return_commit_hash=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
return cls.from_config(__SCREAMING_SNAKE_CASE , return_unused_kwargs=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , __SCREAMING_SNAKE_CASE : bool = False , **__SCREAMING_SNAKE_CASE : Tuple ) -> Tuple:
self.save_config(save_directory=__SCREAMING_SNAKE_CASE , push_to_hub=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@property
def _a ( self : Union[str, Any] ) -> Any:
return self._get_compatibles()
@classmethod
def _a ( cls : int ) -> Tuple:
__UpperCAmelCase =list(set([cls.__name__] + cls._compatibles ) )
__UpperCAmelCase =importlib.import_module(__name__.split(""".""" )[0] )
__UpperCAmelCase =[
getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for c in compatible_classes_str if hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
]
return compatible_classes
| 68 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = vertices
lowerCAmelCase = {
(min(_snake_case ), max(_snake_case )): weight for edge, weight in edges.items()
}
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
lowerCAmelCase = weight
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Graph({min(self.vertices )} , {} )
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
lowerCAmelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
lowerCAmelCase = edge
lowerCAmelCase = weight
subgraph.add_edge(_snake_case , _snake_case )
return subgraph
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "p107_network.txt" ):
lowerCAmelCase = os.path.abspath(os.path.dirname(_UpperCAmelCase ) )
lowerCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = {}
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
with open(_UpperCAmelCase ) as f:
lowerCAmelCase = f.read().strip().split('\n' )
lowerCAmelCase = [line.split(',' ) for line in data]
for edgea in range(1 , len(_UpperCAmelCase ) ):
for edgea in range(_UpperCAmelCase ):
if adjaceny_matrix[edgea][edgea] != "-":
lowerCAmelCase = int(adjaceny_matrix[edgea][edgea] )
lowerCAmelCase = Graph(set(range(len(_UpperCAmelCase ) ) ) , _UpperCAmelCase )
lowerCAmelCase = graph.prims_algorithm()
lowerCAmelCase = sum(graph.edges.values() )
lowerCAmelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 4 | 0 |
'''simple docstring'''
import numpy
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : numpy.ndarray , a_ : numpy.ndarray ):
"""simple docstring"""
__snake_case = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
__snake_case = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
__snake_case = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
__snake_case = numpy.random.rand(3 , 1 )
# Real output values provided.
__snake_case = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
__snake_case = numpy.zeros(output_array.shape )
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
__snake_case = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
__snake_case = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
__snake_case = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
__snake_case = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def A ( self : Union[str, Any] , a_ : numpy.ndarray , a_ : int , a_ : bool ):
"""simple docstring"""
for iteration in range(1 , iterations + 1 ):
__snake_case = self.feedforward()
self.back_propagation()
if give_loss:
__snake_case = numpy.mean(numpy.square(output - self.feedforward() ) )
print(f'''Iteration {iteration} Loss: {loss}''' )
def A ( self : Optional[Any] , a_ : numpy.ndarray ):
"""simple docstring"""
__snake_case = input_arr
__snake_case = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
__snake_case = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
__snake_case = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def __UpperCAmelCase ( _UpperCAmelCase : numpy.ndarray ) -> numpy.ndarray:
return 1 / (1 + numpy.exp(-value ))
def __UpperCAmelCase ( _UpperCAmelCase : numpy.ndarray ) -> numpy.ndarray:
return (value) * (1 - (value))
def __UpperCAmelCase ( ) -> int:
__snake_case = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
__snake_case = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
__snake_case = TwoHiddenLayerNeuralNetwork(
input_array=_UpperCAmelCase , output_array=_UpperCAmelCase )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_UpperCAmelCase , iterations=10 , give_loss=_UpperCAmelCase )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 69 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase )] )
lowerCAmelCase = np.array(_UpperCAmelCase )
lowerCAmelCase = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , _UpperCAmelCase ) ) , x.transpose() ) , _UpperCAmelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = (1, 2, 1)
lowerCAmelCase = (1, 1, 0, 7)
lowerCAmelCase = SARIMAX(
_UpperCAmelCase , exog=_UpperCAmelCase , order=_UpperCAmelCase , seasonal_order=_UpperCAmelCase )
lowerCAmelCase = model.fit(disp=_UpperCAmelCase , maxiter=600 , method='nm' )
lowerCAmelCase = model_fit.predict(1 , len(_UpperCAmelCase ) , exog=[test_match] )
return result[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = SVR(kernel='rbf' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = regressor.predict(_UpperCAmelCase )
return y_pred[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list ):
train_user.sort()
lowerCAmelCase = np.percentile(_UpperCAmelCase , 25 )
lowerCAmelCase = np.percentile(_UpperCAmelCase , 75 )
lowerCAmelCase = qa - qa
lowerCAmelCase = qa - (iqr * 0.1)
return low_lim
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : float ):
lowerCAmelCase = 0
lowerCAmelCase = 0
for i in list_vote:
if i > actual_result:
lowerCAmelCase = not_safe + 1
else:
if abs(abs(_UpperCAmelCase ) - abs(_UpperCAmelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
__UpperCamelCase : Optional[Any] = [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
__UpperCamelCase : Any = pd.DataFrame(
data_input, columns=['''total_user''', '''total_even''', '''days''']
)
__UpperCamelCase : Dict = Normalizer().fit_transform(data_input_df.values)
# split data
__UpperCamelCase : Dict = normalize_df[:, 2].tolist()
__UpperCamelCase : Union[str, Any] = normalize_df[:, 0].tolist()
__UpperCamelCase : List[str] = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
__UpperCamelCase : Optional[int] = normalize_df[:, [1, 2]].tolist()
__UpperCamelCase : Tuple = x[: len(x) - 1]
__UpperCamelCase : Any = x[len(x) - 1 :]
# for linear regression & sarimax
__UpperCamelCase : str = total_date[: len(total_date) - 1]
__UpperCamelCase : Union[str, Any] = total_user[: len(total_user) - 1]
__UpperCamelCase : List[Any] = total_match[: len(total_match) - 1]
__UpperCamelCase : Optional[Any] = total_date[len(total_date) - 1 :]
__UpperCamelCase : str = total_user[len(total_user) - 1 :]
__UpperCamelCase : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
__UpperCamelCase : Any = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
__UpperCamelCase : List[str] = '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
print('''Today\'s data is {not_str}safe.''')
| 4 | 0 |
from __future__ import annotations
from typing import TypedDict
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = 42
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
raise TypeError('The parameter s type must be str.' )
return [s[i:] + s[:i] for i in range(len(lowercase ) )]
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
raise TypeError('The parameter s type must be str.' )
if not s:
raise ValueError('The parameter s must not be empty.' )
lowerCamelCase_ = all_rotations(lowercase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
lowerCamelCase_ = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(lowercase ),
}
return response
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : int ):
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
raise TypeError('The parameter bwt_string type must be str.' )
if not bwt_string:
raise ValueError('The parameter bwt_string must not be empty.' )
try:
lowerCamelCase_ = int(lowercase )
except ValueError:
raise TypeError(
'The parameter idx_original_string type must be int or passive'
' of cast to int.' )
if idx_original_string < 0:
raise ValueError('The parameter idx_original_string must not be lower than 0.' )
if idx_original_string >= len(lowercase ):
raise ValueError(
'The parameter idx_original_string must be lower than' ' len(bwt_string).' )
lowerCamelCase_ = [''] * len(lowercase )
for _ in range(len(lowercase ) ):
for i in range(len(lowercase ) ):
lowerCamelCase_ = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
lowerCamelCase : Any = "Provide a string that I will generate its BWT transform: "
lowerCamelCase : Tuple = input(entry_msg).strip()
lowerCamelCase : Dict = bwt_transform(s)
print(
F"""Burrows Wheeler transform for string '{s}' results """
F"""in '{result['bwt_string']}'"""
)
lowerCamelCase : Dict = reverse_bwt(result["bwt_string"], result["idx_original_string"])
print(
F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
F"""we get original string '{original_string}'"""
)
| 70 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'-m' , '--pretrained_model_name_or_path' , type=_UpperCAmelCase , default=_UpperCAmelCase , required=_UpperCAmelCase , help='Path to pretrained model or model identifier from huggingface.co/models.' , )
parser.add_argument(
'-c' , '--caption' , type=_UpperCAmelCase , default='robotic cat with wings' , help='Text used to generate images.' , )
parser.add_argument(
'-n' , '--images_num' , type=_UpperCAmelCase , default=4 , help='How much images to generate.' , )
parser.add_argument(
'-s' , '--seed' , type=_UpperCAmelCase , default=42 , help='Seed for random process.' , )
parser.add_argument(
'-ci' , '--cuda_id' , type=_UpperCAmelCase , default=0 , help='cuda_id.' , )
lowerCAmelCase = parser.parse_args()
return args
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] ):
if not len(_UpperCAmelCase ) == rows * cols:
raise ValueError('The specified number of rows and columns are not correct.' )
lowerCAmelCase ,lowerCAmelCase = imgs[0].size
lowerCAmelCase = Image.new('RGB' , size=(cols * w, rows * h) )
lowerCAmelCase ,lowerCAmelCase = grid.size
for i, img in enumerate(_UpperCAmelCase ):
grid.paste(_UpperCAmelCase , box=(i % cols * w, i // cols * h) )
return grid
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any]="robotic cat with wings" , _UpperCAmelCase : Optional[int]=7.5 , _UpperCAmelCase : Dict=50 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : int=42 , ):
lowerCAmelCase = torch.Generator(pipeline.device ).manual_seed(_UpperCAmelCase )
lowerCAmelCase = pipeline(
_UpperCAmelCase , guidance_scale=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , generator=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , ).images
lowerCAmelCase = int(math.sqrt(_UpperCAmelCase ) )
lowerCAmelCase = image_grid(_UpperCAmelCase , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__UpperCamelCase : Optional[Any] = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase : List[Any] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__UpperCamelCase : str = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__UpperCamelCase : Optional[int] = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__UpperCamelCase : List[str] = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')
__UpperCamelCase : Tuple = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase : Union[str, Any] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
__UpperCamelCase : Dict = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, '''unet''', unet)
else:
__UpperCamelCase : Dict = unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase : Optional[Any] = pipeline.to(unet.device)
__UpperCamelCase ,__UpperCamelCase : List[Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
__UpperCamelCase : int = os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))
| 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
"""NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""NezhaForNextSentencePrediction""",
"""NezhaForMaskedLM""",
"""NezhaForPreTraining""",
"""NezhaForMultipleChoice""",
"""NezhaForQuestionAnswering""",
"""NezhaForSequenceClassification""",
"""NezhaForTokenClassification""",
"""NezhaModel""",
"""NezhaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 71 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : List[int] ):
lowerCAmelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(_UpperCAmelCase ) == len(_UpperCAmelCase ), F'{len(_UpperCAmelCase )} != {len(_UpperCAmelCase )}'
dest_layers.load_state_dict(layers_to_copy.state_dict() )
__UpperCamelCase : Optional[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCamelCase : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] ):
try:
lowerCAmelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
F' {n_student}' )
return list(range(_UpperCAmelCase ) )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ):
if n_student > n_teacher:
raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}' )
elif n_teacher == n_student:
return list(range(_UpperCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, PreTrainedModel] , _UpperCAmelCase : Union[str, Path] = "student" , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Optional[Any]=None , **_UpperCAmelCase : str , ):
lowerCAmelCase = 'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
assert (e is not None) or (d is not None), _msg
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
AutoTokenizer.from_pretrained(_UpperCAmelCase ).save_pretrained(_UpperCAmelCase ) # purely for convenience
lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).eval()
else:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ), F'teacher must be a model or string got type {type(_UpperCAmelCase )}'
lowerCAmelCase = teacher.config.to_diff_dict()
try:
lowerCAmelCase ,lowerCAmelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
init_kwargs.update({'encoder_layers': e, 'decoder_layers': d} )
except AttributeError: # T5
if hasattr(teacher.config , 'num_encoder_layers' ):
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
if hasattr(teacher.config , 'num_encoder_layers' ):
init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d} )
else:
init_kwargs.update({'num_layers': e, 'num_decoder_layers': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(_UpperCAmelCase )
# Copy weights
lowerCAmelCase = teacher.config_class(**_UpperCAmelCase )
lowerCAmelCase = AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
lowerCAmelCase = student.load_state_dict(teacher.state_dict() , strict=_UpperCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
lowerCAmelCase ,lowerCAmelCase = list(range(_UpperCAmelCase ) ), list(range(_UpperCAmelCase ) )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
F' {save_path}' )
student.save_pretrained(_UpperCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
if d_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
try:
if hasattr(
_UpperCAmelCase , 'prophetnet' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _UpperCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _UpperCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _UpperCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , _UpperCAmelCase )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' )
lowerCAmelCase = {
'teacher_type': teacher.config.model_type,
'copied_encoder_layers': e_layers_to_copy,
'copied_decoder_layers': d_layers_to_copy,
}
student.save_pretrained(_UpperCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 4 | 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'umt5'
UpperCamelCase__ = ['past_key_values']
def __init__( self , snake_case_=25_01_12 , snake_case_=5_12 , snake_case_=64 , snake_case_=10_24 , snake_case_=8 , snake_case_=None , snake_case_=6 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gated-gelu" , snake_case_=True , snake_case_=True , snake_case_="T5Tokenizer" , snake_case_=True , snake_case_=0 , snake_case_=1 , snake_case_=0 , **snake_case_ , ):
super().__init__(
is_encoder_decoder=snake_case_ , tokenizer_class=snake_case_ , tie_word_embeddings=snake_case_ , pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , **snake_case_ , )
lowercase =vocab_size
lowercase =d_model
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =(
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =feed_forward_proj
lowercase =use_cache
lowercase =self.feed_forward_proj.split('''-''' )
lowercase =act_info[-1]
lowercase =act_info[0] == '''gated'''
if len(snake_case_ ) > 1 and act_info[0] != "gated" or len(snake_case_ ) > 2:
raise ValueError(
f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
if feed_forward_proj == "gated-gelu":
lowercase ='''gelu_new'''
@property
def _A( self ):
return self.d_model
@property
def _A( self ):
return self.num_heads
@property
def _A( self ):
return self.num_layers
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def _A( self ):
lowercase ={
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
lowercase ='''past_encoder_sequence + sequence'''
lowercase ={0: '''batch'''}
lowercase ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def _A( self ):
return 13
@property
def _A( self ):
return 5E-4
| 72 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Dict = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = ['''LayoutXLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = ['''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
__UpperCamelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 4 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( A__ ):
_lowercase : Dict = ['''image_processor''', '''tokenizer''']
_lowercase : Optional[Any] = '''AutoImageProcessor'''
_lowercase : List[Any] = '''AutoTokenizer'''
def __init__( self , a , a) -> Any:
super().__init__(a , a)
SCREAMING_SNAKE_CASE = self.image_processor
def __call__( self , a=None , a=None , a=None , **a) -> Optional[int]:
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:
SCREAMING_SNAKE_CASE = self.tokenizer(a , return_tensors=a , **a)
if images is not None:
SCREAMING_SNAKE_CASE = self.image_processor(a , return_tensors=a , **a)
if text is not None and images is not None:
SCREAMING_SNAKE_CASE = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**a) , tensor_type=a)
def SCREAMING_SNAKE_CASE__ ( self , *a , **a) -> Tuple:
return self.tokenizer.batch_decode(*a , **a)
def SCREAMING_SNAKE_CASE__ ( self , *a , **a) -> List[Any]:
return self.tokenizer.decode(*a , **a)
@property
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
return ["input_ids", "attention_mask", "pixel_values"]
| 73 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase = F'Resistor at index {index} has a negative or zero value!'
raise ValueError(_UpperCAmelCase )
first_sum += 1 / float(_UpperCAmelCase )
index += 1
return 1 / first_sum
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowerCAmelCase = F'Resistor at index {index} has a negative value!'
raise ValueError(_UpperCAmelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
def a__ ( snake_case , snake_case ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__SCREAMING_SNAKE_CASE : Dict = str(bin(snake_case ) )[2:] # remove the leading "0b"
__SCREAMING_SNAKE_CASE : Optional[Any] = str(bin(snake_case ) )[2:] # remove the leading "0b"
__SCREAMING_SNAKE_CASE : str = max(len(snake_case ) , len(snake_case ) )
return "0b" + "".join(
str(int(char_a == '''1''' and char_b == '''1''' ) )
for char_a, char_b in zip(a_binary.zfill(snake_case ) , b_binary.zfill(snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 74 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class a ( a__ ):
snake_case__ = '''glpn'''
def __init__( self , _snake_case=3 , _snake_case=4 , _snake_case=[2, 2, 2, 2] , _snake_case=[8, 4, 2, 1] , _snake_case=[32, 64, 1_60, 2_56] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[1, 2, 5, 8] , _snake_case=[4, 4, 4, 4] , _snake_case="gelu" , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=0.1 , _snake_case=1E-6 , _snake_case=64 , _snake_case=10 , _snake_case=-1 , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
lowerCAmelCase = num_channels
lowerCAmelCase = num_encoder_blocks
lowerCAmelCase = depths
lowerCAmelCase = sr_ratios
lowerCAmelCase = hidden_sizes
lowerCAmelCase = patch_sizes
lowerCAmelCase = strides
lowerCAmelCase = mlp_ratios
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = initializer_range
lowerCAmelCase = drop_path_rate
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = decoder_hidden_size
lowerCAmelCase = max_depth
lowerCAmelCase = head_in_index
| 4 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = ['pixel_values']
def __init__( self : List[str] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : bool = True , _A : Dict[str, int] = None , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , _A : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **_A : int , ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase__ : Dict = size if size is not None else {'''shortest_edge''': 224}
UpperCAmelCase__ : int = get_size_dict(_A , default_to_square=_A )
UpperCAmelCase__ : Union[str, Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase__ : List[str] = get_size_dict(_A , param_name='''crop_size''' )
UpperCAmelCase__ : str = do_resize
UpperCAmelCase__ : List[Any] = size
UpperCAmelCase__ : int = resample
UpperCAmelCase__ : int = do_center_crop
UpperCAmelCase__ : List[str] = crop_size
UpperCAmelCase__ : Union[str, Any] = do_rescale
UpperCAmelCase__ : Optional[int] = rescale_factor
UpperCAmelCase__ : List[Any] = do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCAmelCase__ : Dict = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowercase_ ( self : str , _A : np.ndarray , _A : Dict[str, int] , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = get_size_dict(_A , default_to_square=_A )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
UpperCAmelCase__ : Tuple = int((256 / 224) * size['''shortest_edge'''] )
UpperCAmelCase__ : Tuple = get_resize_output_image_size(_A , size=_A , default_to_square=_A )
UpperCAmelCase__ : Dict = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
return resize(
_A , size=(size_dict['''height'''], size_dict['''width''']) , resample=_A , data_format=_A , **_A )
def lowercase_ ( self : Optional[Any] , _A : np.ndarray , _A : Dict[str, int] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(_A , size=(size['''height'''], size['''width''']) , data_format=_A , **_A )
def lowercase_ ( self : List[str] , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Dict , ):
'''simple docstring'''
return rescale(_A , scale=_A , data_format=_A , **_A )
def lowercase_ ( self : Dict , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Optional[int] , ):
'''simple docstring'''
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def lowercase_ ( self : Optional[Any] , _A : ImageInput , _A : Optional[bool] = None , _A : Optional[Dict[str, int]] = None , _A : PILImageResampling = None , _A : Optional[bool] = None , _A : Optional[Dict[str, int]] = None , _A : Optional[bool] = None , _A : Optional[float] = None , _A : Optional[bool] = None , _A : Optional[Union[float, Iterable[float]]] = None , _A : Optional[Union[float, Iterable[float]]] = None , _A : Optional[TensorType] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
'''simple docstring'''
UpperCAmelCase__ : str = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : Optional[int] = resample if resample is not None else self.resample
UpperCAmelCase__ : List[str] = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : Tuple = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : List[str] = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Tuple = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : List[str] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : Tuple = size if size is not None else self.size
UpperCAmelCase__ : int = get_size_dict(_A , default_to_square=_A )
UpperCAmelCase__ : Union[str, Any] = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : int = get_size_dict(_A , param_name='''crop_size''' )
UpperCAmelCase__ : Union[str, Any] = 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.''' )
# All transformations expect numpy arrays.
UpperCAmelCase__ : int = [to_numpy_array(_A ) for image in images]
if do_resize:
UpperCAmelCase__ : str = [self.resize(_A , _A , _A ) for image in images]
if do_center_crop:
UpperCAmelCase__ : Tuple = [self.center_crop(_A , _A ) for image in images]
if do_rescale:
UpperCAmelCase__ : Optional[int] = [self.rescale(_A , _A ) for image in images]
if do_normalize:
UpperCAmelCase__ : Any = [self.normalize(_A , _A , _A ) for image in images]
UpperCAmelCase__ : Tuple = [to_channel_dimension_format(_A , _A ) for image in images]
UpperCAmelCase__ : Dict = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
| 75 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , _snake_case=10_00 , ):
"""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
lowerCAmelCase = range_bbox
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase = bbox[i, j, 3]
lowerCAmelCase = bbox[i, j, 1]
lowerCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase = bbox[i, j, 2]
lowerCAmelCase = bbox[i, j, 0]
lowerCAmelCase = t
lowerCAmelCase = tf.convert_to_tensor(_snake_case )
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 = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForMaskedLM(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForSequenceClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForTokenClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
snake_case__ = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ = False
snake_case__ = True
snake_case__ = 1_0
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = TFLayoutLMModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@unittest.skip('Onnx compliancy broke with TF 2.10' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def _SCREAMING_SNAKE_CASE ():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowerCAmelCase = tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
lowerCAmelCase = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the sequence output on [0, :3, :3]
lowerCAmelCase = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , _snake_case , atol=1E-3 ) )
# test the pooled output on [1, :3]
lowerCAmelCase = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , _snake_case , atol=1E-3 ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=2 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
lowerCAmelCase = outputs.loss
lowerCAmelCase = (2,)
self.assertEqual(loss.shape , _snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = (2, 2)
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=13 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the shape of the logits
lowerCAmelCase = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , _snake_case )
self.assertEqual(outputs.end_logits.shape , _snake_case )
| 4 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Optional[Any] = 3_84
__lowercase : List[Any] = 7
if "tiny" in model_name:
__lowercase : List[str] = 96
__lowercase : Dict = (2, 2, 6, 2)
__lowercase : Tuple = (3, 6, 12, 24)
elif "small" in model_name:
__lowercase : Optional[Any] = 96
__lowercase : int = (2, 2, 18, 2)
__lowercase : List[str] = (3, 6, 12, 24)
elif "base" in model_name:
__lowercase : List[Any] = 1_28
__lowercase : List[str] = (2, 2, 18, 2)
__lowercase : Tuple = (4, 8, 16, 32)
__lowercase : Union[str, Any] = 12
__lowercase : Union[str, Any] = 5_12
elif "large" in model_name:
__lowercase : List[Any] = 1_92
__lowercase : Union[str, Any] = (2, 2, 18, 2)
__lowercase : Optional[int] = (6, 12, 24, 48)
__lowercase : Union[str, Any] = 12
__lowercase : Optional[Any] = 7_68
# set label information
__lowercase : Any = 1_50
__lowercase : List[str] = '''huggingface/label-files'''
__lowercase : int = '''ade20k-id2label.json'''
__lowercase : str = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='''dataset''' ) , '''r''' ) )
__lowercase : List[str] = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
__lowercase : Any = {v: k for k, v in idalabel.items()}
__lowercase : Any = SwinConfig(
embed_dim=__UpperCamelCase , depths=__UpperCamelCase , num_heads=__UpperCamelCase , window_size=__UpperCamelCase , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
__lowercase : List[str] = UperNetConfig(
backbone_config=__UpperCamelCase , auxiliary_in_channels=__UpperCamelCase , num_labels=__UpperCamelCase , idalabel=__UpperCamelCase , labelaid=__UpperCamelCase , )
return config
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : str = []
# fmt: off
# stem
rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
] )
# fmt: on
return rename_keys
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : List[Any] = dct.pop(__UpperCamelCase )
__lowercase : Optional[Any] = val
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
__lowercase : str = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__lowercase : List[str] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__lowercase : Optional[Any] = state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight""" )
__lowercase : int = state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowercase : Optional[Any] = in_proj_weight[:dim, :]
__lowercase : Union[str, Any] = in_proj_bias[: dim]
__lowercase : Any = in_proj_weight[
dim : dim * 2, :
]
__lowercase : Dict = in_proj_bias[
dim : dim * 2
]
__lowercase : List[Any] = in_proj_weight[
-dim :, :
]
__lowercase : Optional[Any] = in_proj_bias[-dim :]
# fmt: on
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase ,__lowercase : List[Any] = x.shape
__lowercase : Tuple = x.reshape(__UpperCamelCase , 4 , in_channel // 4 )
__lowercase : Union[str, Any] = x[:, [0, 2, 1, 3], :].transpose(1 , 2 ).reshape(__UpperCamelCase , __UpperCamelCase )
return x
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase ,__lowercase : List[Any] = x.shape
__lowercase : Tuple = x.reshape(__UpperCamelCase , in_channel // 4 , 4 )
__lowercase : int = x[:, :, [0, 2, 1, 3]].transpose(1 , 2 ).reshape(__UpperCamelCase , __UpperCamelCase )
return x
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Optional[int] = x.shape[0]
__lowercase : Dict = x.reshape(4 , in_channel // 4 )
__lowercase : int = x[[0, 2, 1, 3], :].transpose(0 , 1 ).reshape(__UpperCamelCase )
return x
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Dict = x.shape[0]
__lowercase : Any = x.reshape(in_channel // 4 , 4 )
__lowercase : Optional[Any] = x[:, [0, 2, 1, 3]].transpose(0 , 1 ).reshape(__UpperCamelCase )
return x
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : str = {
'''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
'''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
'''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
'''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
}
__lowercase : str = model_name_to_url[model_name]
__lowercase : List[Any] = torch.hub.load_state_dict_from_url(__UpperCamelCase , map_location='''cpu''' , file_name=__UpperCamelCase )[
'''state_dict'''
]
for name, param in state_dict.items():
print(__UpperCamelCase , param.shape )
__lowercase : Optional[int] = get_upernet_config(__UpperCamelCase )
__lowercase : Any = UperNetForSemanticSegmentation(__UpperCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__lowercase : Dict = state_dict.pop(__UpperCamelCase )
if "bn" in key:
__lowercase : Optional[Any] = key.replace('''bn''' , '''batch_norm''' )
__lowercase : Union[str, Any] = val
# rename keys
__lowercase : List[Any] = create_rename_keys(__UpperCamelCase )
for src, dest in rename_keys:
rename_key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
read_in_q_k_v(__UpperCamelCase , config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
__lowercase : List[Any] = reverse_correct_unfold_reduction_order(__UpperCamelCase )
if "norm" in key:
__lowercase : Optional[int] = reverse_correct_unfold_norm_order(__UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
# verify on image
__lowercase : Optional[Any] = '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
__lowercase : Union[str, Any] = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw ).convert('''RGB''' )
__lowercase : str = SegformerImageProcessor()
__lowercase : List[str] = processor(__UpperCamelCase , return_tensors='''pt''' ).pixel_values
with torch.no_grad():
__lowercase : Optional[int] = model(__UpperCamelCase )
__lowercase : Optional[int] = outputs.logits
print(logits.shape )
print('''First values of logits:''' , logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
__lowercase : Any = torch.tensor(
[[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]] )
elif model_name == "upernet-swin-small":
__lowercase : List[Any] = torch.tensor(
[[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]] )
elif model_name == "upernet-swin-base":
__lowercase : int = torch.tensor(
[[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]] )
elif model_name == "upernet-swin-large":
__lowercase : str = torch.tensor(
[[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]] )
print('''Logits:''' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __UpperCamelCase , atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCamelCase )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(__UpperCamelCase )
if push_to_hub:
print(f"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(f"""openmmlab/{model_name}""" )
processor.push_to_hub(f"""openmmlab/{model_name}""" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='upernet-swin-tiny',
type=str,
choices=[F"upernet-swin-{size}" for size in ['tiny', 'small', 'base', 'large']],
help='Name of the Swin + UperNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
a_ = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 76 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__UpperCamelCase : Union[str, Any] = '''examples/'''
__UpperCamelCase : str = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
'''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
__UpperCamelCase : List[str] = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__UpperCamelCase : Optional[int] = '''README.md'''
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : Any ):
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase ,lowerCAmelCase = REPLACE_PATTERNS[pattern]
lowerCAmelCase = replace.replace('VERSION' , _UpperCAmelCase )
lowerCAmelCase = re_pattern.sub(_UpperCAmelCase , _UpperCAmelCase )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, Any] ):
for folder, directories, fnames in os.walk(_UpperCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase , pattern='examples' )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if not patch:
update_version_in_examples(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = '🤗 Transformers currently provides the following architectures'
lowerCAmelCase = '1. Want to contribute a new model?'
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.readlines()
# Find the start of the list.
lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
lowerCAmelCase = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
with open(REPLACE_FILES['init'] , 'r' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase = REPLACE_PATTERNS['init'][0].search(_UpperCAmelCase ).groups()[0]
return packaging.version.parse(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple=False ):
lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
lowerCAmelCase = default_version.base_version
elif patch:
lowerCAmelCase = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
lowerCAmelCase = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
lowerCAmelCase = input(F'Which version are you releasing? [{default_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = default_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase , patch=_UpperCAmelCase )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = get_version()
lowerCAmelCase = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
lowerCAmelCase = input(F'Which version are we developing now? [{dev_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = dev_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 4 | 0 |
"""simple docstring"""
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:
A = None
A = logging.get_logger(__name__)
A = """▁"""
A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""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"""
},
}
A = {
"""google/pegasus-xsum""": 512,
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PegasusTokenizer
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : str , UpperCamelCase_ : str=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Any="<unk>" , UpperCamelCase_ : Tuple="<mask_2>" , UpperCamelCase_ : Any="<mask_1>" , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : str=103 , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCamelCase_ , UpperCamelCase_):
raise TypeError(
F"additional_special_tokens should be of type {type(UpperCamelCase_)}, but is"
F" {type(UpperCamelCase_)}")
__UpperCAmelCase : 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(UpperCamelCase_) , self.offset - 1)
]
if len(set(UpperCamelCase_)) != len(UpperCamelCase_):
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}.")
__UpperCAmelCase : str = additional_special_tokens_extended
else:
__UpperCAmelCase : Tuple = [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__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , pad_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , mask_token_sent=UpperCamelCase_ , offset=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = vocab_file
__UpperCAmelCase : List[str] = False if not self.vocab_file else True
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = 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 a_ ( self : Union[str, Any] , UpperCamelCase_ : List , UpperCamelCase_ : Optional[List] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(UpperCamelCase_)
elif token_ids_a is None:
return self._special_token_mask(UpperCamelCase_) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a) + [1]
def a_ ( self : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None):
"""simple docstring"""
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 a_ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
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(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[str] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
__UpperCamelCase : Optional[int] = pytest.mark.integration
@require_faiss
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(_snake_case ) for x in np.arange(30 ).tolist()]} )
return dset
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
lowerCAmelCase = dset.map(
lambda _snake_case , _snake_case : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=_snake_case , keep_in_memory=_snake_case )
lowerCAmelCase = dset.add_faiss_index('vecs' , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT , )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_snake_case ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(_snake_case , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
lowerCAmelCase = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCAmelCase = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
lowerCAmelCase = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=_snake_case )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertRaises(_snake_case , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
lowerCAmelCase = np.eye(5 , dtype=np.floataa )[::-1]
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case )
self.assertRaises(_snake_case , index.search_batch , queries[0] )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , _snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
lowerCAmelCase = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(_snake_case ):
lowerCAmelCase = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = faiss.IndexFlat(5 )
lowerCAmelCase = FaissIndex(custom_index=_snake_case )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_snake_case ) as tmp_file:
index.save(tmp_file.name )
lowerCAmelCase = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Dict ):
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
lowerCAmelCase = 'index.faiss'
lowerCAmelCase = F'mock://{index_name}'
index.save(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCAmelCase = FaissIndex.load(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCAmelCase = Elasticsearch()
lowerCAmelCase = {'acknowledged': True}
lowerCAmelCase = ElasticSearchIndex(es_client=_snake_case )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
lowerCAmelCase = 'foo'
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
lowerCAmelCase = 'foo'
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
lowerCAmelCase = ['foo', 'bar', 'foobar']
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , _snake_case )
# batched queries with timeout
lowerCAmelCase = ['foo', 'bar', 'foobar']
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case , request_timeout=30 )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , _snake_case )
| 4 | 0 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( snake_case_ : int ) -> bool:
'''simple docstring'''
assert isinstance(snake_case_ , snake_case_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
UpperCAmelCase_ = range(3 , int(math.sqrt(snake_case_ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def lowerCAmelCase_ ( snake_case_ : List[Any] , snake_case_ : Optional[int]=1 , **snake_case_ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ = factor * value
UpperCAmelCase_ = value
while not is_prime(snake_case_ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **snake_case_ )
return value
| 78 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = IFInpaintingPipeline
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._get_dummy_components()
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_save_load_local()
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 4 | 0 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {"""vocab_file""": """vocab.json"""}
SCREAMING_SNAKE_CASE__ : Tuple = {
"""vocab_file""": {
"""mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
}
}
SCREAMING_SNAKE_CASE__ : List[Any] = {"""mgp-str""": 27}
class UpperCAmelCase_ ( __lowerCamelCase ):
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _lowerCAmelCase , _lowerCAmelCase="[GO]" , _lowerCAmelCase="[GO]" , _lowerCAmelCase="[s]" , _lowerCAmelCase="[GO]" , **_lowerCAmelCase ):
super().__init__(
unk_token=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , **_lowerCAmelCase , )
with open(_lowerCAmelCase , encoding="""utf-8""" ) as vocab_handle:
UpperCAmelCase__ : Optional[int] = json.load(_lowerCAmelCase )
UpperCAmelCase__ : Any = {v: k for k, v in self.vocab.items()}
@property
def __UpperCAmelCase ( self ):
return len(self.vocab )
def __UpperCAmelCase ( self ):
return dict(self.vocab , **self.added_tokens_encoder )
def __UpperCAmelCase ( self , _lowerCAmelCase ):
UpperCAmelCase__ : Tuple = []
for s in text:
char_tokens.extend(_lowerCAmelCase )
return char_tokens
def __UpperCAmelCase ( self , _lowerCAmelCase ):
return self.vocab.get(_lowerCAmelCase , self.vocab.get(self.unk_token ) )
def __UpperCAmelCase ( self , _lowerCAmelCase ):
return self.decoder.get(_lowerCAmelCase )
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase = None ):
if not os.path.isdir(_lowerCAmelCase ):
logger.error("""Vocabulary path ({}) should be a directory""".format(_lowerCAmelCase ) )
return
UpperCAmelCase__ : Optional[Any] = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + """\n""" )
return (vocab_file,)
| 79 |
"""simple docstring"""
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 a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
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
lowerCAmelCase = self.vocab_size - 1
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
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 = 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 , )
lowerCAmelCase = 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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , head_mask=_snake_case )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTDoubleHeadsModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = OpenAIGPTForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class a ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
snake_case__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
snake_case__ = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case=False ):
"""simple docstring"""
lowerCAmelCase = super()._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_snake_case )
return inputs_dict
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , n_embd=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = OpenAIGPTModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@require_torch
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(_snake_case )
lowerCAmelCase = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=_snake_case ) # the president is
lowerCAmelCase = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowerCAmelCase = model.generate(_snake_case , do_sample=_snake_case )
self.assertListEqual(output_ids[0].tolist() , _snake_case )
| 4 | 0 |
class __UpperCamelCase :
def __init__( self : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = name
__lowercase = val
def __str__( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return F'{self.__class__.__name__}({self.name}, {self.val})'
def __lt__( self : Optional[int] , _lowerCAmelCase : int ) -> Any:
"""simple docstring"""
return self.val < other.val
class __UpperCamelCase :
def __init__( self : Union[str, Any] , _lowerCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = {}
__lowercase = {}
__lowercase = self.build_heap(_lowerCAmelCase )
def __getitem__( self : List[Any] , _lowerCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
return self.get_value(_lowerCAmelCase )
def _a ( self : str , _lowerCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return (idx - 1) // 2
def _a ( self : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
return idx * 2 + 1
def _a ( self : List[Any] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return idx * 2 + 2
def _a ( self : int , _lowerCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
return self.heap_dict[key]
def _a ( self : Optional[Any] , _lowerCAmelCase : Any ) -> Any:
"""simple docstring"""
__lowercase = len(_lowerCAmelCase ) - 1
__lowercase = self.get_parent_idx(_lowerCAmelCase )
for idx, i in enumerate(_lowerCAmelCase ):
__lowercase = idx
__lowercase = i.val
for i in range(_lowerCAmelCase , -1 , -1 ):
self.sift_down(_lowerCAmelCase , _lowerCAmelCase )
return array
def _a ( self : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> List[str]:
"""simple docstring"""
while True:
__lowercase = self.get_left_child_idx(_lowerCAmelCase ) # noqa: E741
__lowercase = self.get_right_child_idx(_lowerCAmelCase )
__lowercase = idx
if l < len(_lowerCAmelCase ) and array[l] < array[idx]:
__lowercase = l
if r < len(_lowerCAmelCase ) and array[r] < array[smallest]:
__lowercase = r
if smallest != idx:
__lowercase , __lowercase = array[smallest], array[idx]
(
(
__lowercase
) , (
__lowercase
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
__lowercase = smallest
else:
break
def _a ( self : int , _lowerCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
__lowercase = self.get_parent_idx(_lowerCAmelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
__lowercase , __lowercase = self.heap[idx], self.heap[p]
__lowercase , __lowercase = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
__lowercase = p
__lowercase = self.get_parent_idx(_lowerCAmelCase )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
return self.heap[0]
def _a ( self : str ) -> str:
"""simple docstring"""
__lowercase , __lowercase = self.heap[-1], self.heap[0]
__lowercase , __lowercase = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
__lowercase = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def _a ( self : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
self.heap.append(_lowerCAmelCase )
__lowercase = len(self.heap ) - 1
__lowercase = node.val
self.sift_up(len(self.heap ) - 1 )
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
return len(self.heap ) == 0
def _a ( self : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
__lowercase = new_value
__lowercase = new_value
self.sift_up(self.idx_of_element[node] )
__UpperCamelCase : Tuple = Node("""R""", -1)
__UpperCamelCase : Union[str, Any] = Node("""B""", 6)
__UpperCamelCase : Optional[Any] = Node("""A""", 3)
__UpperCamelCase : Union[str, Any] = Node("""X""", 1)
__UpperCamelCase : Any = Node("""E""", 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
__UpperCamelCase : Tuple = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
print(i)
print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase : str = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=_UpperCAmelCase , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=_UpperCAmelCase , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=_UpperCAmelCase , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=_UpperCAmelCase , default='data/dump' , help='The dump file prefix.' )
lowerCAmelCase = parser.parse_args()
logger.info(F'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
lowerCAmelCase = BertTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
lowerCAmelCase = RobertaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `<s>`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
lowerCAmelCase = GPTaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
lowerCAmelCase = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(F'Loading text from {args.file_path}' )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
lowerCAmelCase = fp.readlines()
logger.info('Start encoding' )
logger.info(F'{len(_UpperCAmelCase )} examples to process.' )
lowerCAmelCase = []
lowerCAmelCase = 0
lowerCAmelCase = 1_0000
lowerCAmelCase = time.time()
for text in data:
lowerCAmelCase = F'{bos} {text.strip()} {sep}'
lowerCAmelCase = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
rslt.append(_UpperCAmelCase )
iter += 1
if iter % interval == 0:
lowerCAmelCase = time.time()
logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
lowerCAmelCase = time.time()
logger.info('Finished binarization' )
logger.info(F'{len(_UpperCAmelCase )} examples processed.' )
lowerCAmelCase = F'{args.dump_file}.{args.tokenizer_name}.pickle'
lowerCAmelCase = tokenizer.vocab_size
if vocab_size < (1 << 16):
lowerCAmelCase = [np.uintaa(_UpperCAmelCase ) for d in rslt]
else:
lowerCAmelCase = [np.intaa(_UpperCAmelCase ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F'Dump to {dp_file}' )
with open(_UpperCAmelCase , 'wb' ) as handle:
pickle.dump(rslt_ , _UpperCAmelCase , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 4 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case : Optional[Any] = {"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : List[Any] = ["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
_snake_case : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 81 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
'''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
'''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
),
'''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''bert'''
def __init__( self , _snake_case=3_05_22 , _snake_case=7_68 , _snake_case=12 , _snake_case=12 , _snake_case=30_72 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , _snake_case=None , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
lowerCAmelCase = classifier_dropout
class a ( a__ ):
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 4 | 0 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCamelCase = numpy.array([0, 0])
lowerCamelCase = numpy.array([0.5, 0.8_660_254])
lowerCamelCase = numpy.array([1, 0])
lowerCamelCase = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = initial_vectors
for _ in range(lowerCAmelCase__ ):
UpperCAmelCase_ = iteration_step(lowerCAmelCase__ )
return vectors
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = []
for i, start_vector in enumerate(vectors[:-1] ):
UpperCAmelCase_ = vectors[i + 1]
new_vectors.append(lowerCAmelCase__ )
UpperCAmelCase_ = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = numpy.radians(lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = numpy.cos(lowerCAmelCase__ ), numpy.sin(lowerCAmelCase__ )
UpperCAmelCase_ = numpy.array(((c, -s), (s, c)) )
return numpy.dot(lowerCAmelCase__ , lowerCAmelCase__ )
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = plt.gca()
axes.set_aspect("equal" )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
UpperCAmelCase_ , UpperCAmelCase_ = zip(*lowerCAmelCase__ )
plt.plot(lowerCAmelCase__ , lowerCAmelCase__ )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 82 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class a ( a__ , unittest.TestCase ):
snake_case__ = DanceDiffusionPipeline
snake_case__ = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
snake_case__ = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
snake_case__ = False
snake_case__ = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=5_12 , sample_rate=1_60_00 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_snake_case , use_timestep_embedding=_snake_case , time_embedding_type='fourier' , mid_block_type='UNetMidBlock1D' , down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') , up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') , )
lowerCAmelCase = IPNDMScheduler()
lowerCAmelCase = {
'unet': unet,
'scheduler': scheduler,
}
return components
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 4,
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = DanceDiffusionPipeline(**_snake_case )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = self.get_dummy_inputs(_snake_case )
lowerCAmelCase = pipe(**_snake_case )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
lowerCAmelCase = np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_attention_slicing_forward_pass()
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = torch_device
lowerCAmelCase = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(generator=_snake_case , num_inference_steps=1_00 , audio_length_in_s=4.096 )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase = np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = torch_device
lowerCAmelCase = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' , torch_dtype=torch.floataa )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(generator=_snake_case , num_inference_steps=1_00 , audio_length_in_s=4.096 )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase = np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 4 | 0 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase__ = logging.get_logger(__name__)
class __snake_case ( _lowercase):
snake_case__ : Dict = ["audio_values", "audio_mask"]
def __init__( self : List[str] , __lowerCAmelCase : Optional[Any]=2_0_4_8 , __lowerCAmelCase : Optional[int]=1 , __lowerCAmelCase : Dict=[1_6, 1_6] , __lowerCAmelCase : Optional[Any]=1_2_8 , __lowerCAmelCase : Optional[int]=4_4_1_0_0 , __lowerCAmelCase : Optional[Any]=8_6 , __lowerCAmelCase : Dict=2_0_4_8 , __lowerCAmelCase : Tuple=0.0 , **__lowerCAmelCase : Dict , ):
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
_lowerCamelCase : str = spectrogram_length
_lowerCamelCase : List[str] = num_channels
_lowerCamelCase : List[str] = patch_size
_lowerCamelCase : Optional[Any] = feature_size // self.patch_size[1]
_lowerCamelCase : Any = n_fft
_lowerCamelCase : int = sampling_rate // hop_length_to_sampling_rate
_lowerCamelCase : Optional[int] = sampling_rate
_lowerCamelCase : Optional[Any] = padding_value
_lowerCamelCase : str = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_20_50.0 , sampling_rate=__lowerCAmelCase , norm='''slaney''' , mel_scale='''slaney''' , ).T
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : np.array ):
"""simple docstring"""
_lowerCamelCase : Tuple = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='''dB''' , db_range=80.0 , )
_lowerCamelCase : Union[str, Any] = log_spec[:, :-1]
_lowerCamelCase : Optional[Any] = log_spec - 20.0
_lowerCamelCase : List[Any] = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Optional[Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : List[str] , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'''This feature extractor is set to support sampling rate'''
f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
f''' with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
_lowerCamelCase : Union[str, Any] = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
_lowerCamelCase : List[str] = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCamelCase : str = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
_lowerCamelCase : Any = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCamelCase : Union[str, Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCamelCase : Tuple = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
_lowerCamelCase : int = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
_lowerCamelCase : Union[str, Any] = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
_lowerCamelCase : List[Any] = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
_lowerCamelCase : Any = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
_lowerCamelCase : str = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
_lowerCamelCase : List[str] = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
_lowerCamelCase : Optional[int] = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
_lowerCamelCase : int = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
_lowerCamelCase : List[str] = audio_features[i]
_lowerCamelCase : Optional[Any] = feature
# return as BatchFeature
if return_attention_mask:
_lowerCamelCase : Union[str, Any] = {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
else:
_lowerCamelCase : Any = {'''audio_values''': padded_audio_features}
_lowerCamelCase : Optional[int] = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 83 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=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 UpperCamelCase__ ( 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 UpperCamelCase__ ( 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=_snake_case , initializer_range=self.initializer_range , use_stable_embedding=_snake_case , )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = OpenLlamaModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , )
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , )
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
# first forward pass
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , use_cache=_snake_case , )
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(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , output_hidden_states=_snake_case , )['hidden_states'][0]
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , past_key_values=_snake_case , output_hidden_states=_snake_case , )['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(_snake_case , _snake_case , atol=1E-3 ) )
def UpperCamelCase__ ( 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 ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
snake_case__ = (OpenLlamaForCausalLM,) if is_torch_available() else ()
snake_case__ = (
{
'''feature-extraction''': OpenLlamaModel,
'''text-classification''': OpenLlamaForSequenceClassification,
'''text-generation''': OpenLlamaForCausalLM,
'''zero-shot''': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case__ = False
snake_case__ = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( 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(*_snake_case )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
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 UpperCamelCase__ ( self ):
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase ,lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = ids_tensor([1, 10] , config.vocab_size )
lowerCAmelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase = OpenLlamaModel(_snake_case )
original_model.to(_snake_case )
original_model.eval()
lowerCAmelCase = original_model(_snake_case ).last_hidden_state
lowerCAmelCase = original_model(_snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase = {'type': scaling_type, 'factor': 10.0}
lowerCAmelCase = OpenLlamaModel(_snake_case )
scaled_model.to(_snake_case )
scaled_model.eval()
lowerCAmelCase = scaled_model(_snake_case ).last_hidden_state
lowerCAmelCase = scaled_model(_snake_case ).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(_snake_case , _snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1E-5 ) )
| 4 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=2 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , snake_case=1000 , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
lowercase = range_bbox
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowercase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowercase = bbox[i, j, 3]
lowercase = bbox[i, j, 1]
lowercase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowercase = bbox[i, j, 2]
lowercase = bbox[i, j, 0]
lowercase = t
lowercase = tf.convert_to_tensor(snake_case )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = TFLayoutLMModel(config=snake_case )
lowercase = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case )
lowercase = model(snake_case , snake_case , token_type_ids=snake_case )
lowercase = model(snake_case , snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = TFLayoutLMForMaskedLM(config=snake_case )
lowercase = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = self.num_labels
lowercase = TFLayoutLMForSequenceClassification(config=snake_case )
lowercase = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = self.num_labels
lowercase = TFLayoutLMForTokenClassification(config=snake_case )
lowercase = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = TFLayoutLMForQuestionAnswering(config=snake_case )
lowercase = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class A_ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
_UpperCamelCase : Tuple = (
{
"""feature-extraction""": TFLayoutLMModel,
"""fill-mask""": TFLayoutLMForMaskedLM,
"""text-classification""": TFLayoutLMForSequenceClassification,
"""token-classification""": TFLayoutLMForTokenClassification,
"""zero-shot""": TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
_UpperCamelCase : List[str] = False
_UpperCamelCase : Union[str, Any] = True
_UpperCamelCase : Optional[int] = 10
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFLayoutLMModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = TFLayoutLMModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@unittest.skip('Onnx compliancy broke with TF 2.10' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def UpperCAmelCase_ ( ):
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowercase = tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] ) # noqa: E231
lowercase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
lowercase = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
lowercase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
lowercase = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )
lowercase , lowercase , lowercase , lowercase , lowercase = prepare_layoutlm_batch_inputs()
# forward pass
lowercase = model(input_ids=snake_case , bbox=snake_case , attention_mask=snake_case , token_type_ids=snake_case )
# test the sequence output on [0, :3, :3]
lowercase = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , snake_case , atol=1E-3 ) )
# test the pooled output on [1, :3]
lowercase = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , snake_case , atol=1E-3 ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
# initialize model with randomly initialized sequence classification head
lowercase = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=2 )
lowercase , lowercase , lowercase , lowercase , lowercase = prepare_layoutlm_batch_inputs()
# forward pass
lowercase = model(
input_ids=snake_case , bbox=snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
lowercase = outputs.loss
lowercase = (2,)
self.assertEqual(loss.shape , snake_case )
# test the shape of the logits
lowercase = outputs.logits
lowercase = (2, 2)
self.assertEqual(logits.shape , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
# initialize model with randomly initialized token classification head
lowercase = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=13 )
lowercase , lowercase , lowercase , lowercase , lowercase = prepare_layoutlm_batch_inputs()
# forward pass
lowercase = model(
input_ids=snake_case , bbox=snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
# test the shape of the logits
lowercase = outputs.logits
lowercase = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
# initialize model with randomly initialized token classification head
lowercase = TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )
lowercase , lowercase , lowercase , lowercase , lowercase = prepare_layoutlm_batch_inputs()
# forward pass
lowercase = model(input_ids=snake_case , bbox=snake_case , attention_mask=snake_case , token_type_ids=snake_case )
# test the shape of the logits
lowercase = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , snake_case )
self.assertEqual(outputs.end_logits.shape , snake_case )
| 84 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = data
lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return F'Node({self.data})'
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = None
def __iter__( self ):
"""simple docstring"""
lowerCAmelCase = self.head
while node:
yield node.data
lowerCAmelCase = node.next
def __len__( self ):
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self ):
"""simple docstring"""
return "->".join([str(_snake_case ) for item in self] )
def __getitem__( self , _snake_case ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , _snake_case , _snake_case ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
lowerCAmelCase = self.head
for _ in range(_snake_case ):
lowerCAmelCase = current.next
lowerCAmelCase = data
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
self.insert_nth(len(self ) , _snake_case )
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
self.insert_nth(0 , _snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
if not 0 <= index <= len(self ):
raise IndexError('list index out of range' )
lowerCAmelCase = Node(_snake_case )
if self.head is None:
lowerCAmelCase = new_node
elif index == 0:
lowerCAmelCase = self.head # link new_node to head
lowerCAmelCase = new_node
else:
lowerCAmelCase = self.head
for _ in range(index - 1 ):
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next
lowerCAmelCase = new_node
def UpperCamelCase__ ( self ): # print every node data
"""simple docstring"""
print(self )
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.delete_nth(0 )
def UpperCamelCase__ ( self ): # delete from tail
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def UpperCamelCase__ ( self , _snake_case = 0 ):
"""simple docstring"""
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError('List index out of range.' )
lowerCAmelCase = self.head # default first node
if index == 0:
lowerCAmelCase = self.head.next
else:
lowerCAmelCase = self.head
for _ in range(index - 1 ):
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next.next
return delete_node.data
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.head is None
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = None
lowerCAmelCase = self.head
while current:
# Store the current node's next node.
lowerCAmelCase = current.next
# Make the current node's next point backwards
lowerCAmelCase = prev
# Make the previous node be the current node
lowerCAmelCase = current
# Make the current node the next node (to progress iteration)
lowerCAmelCase = next_node
# Return prev in order to put the head at the end
lowerCAmelCase = prev
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = LinkedList()
assert linked_list.is_empty() is True
assert str(_UpperCAmelCase ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(_UpperCAmelCase ) == i
linked_list.insert_nth(_UpperCAmelCase , i + 1 )
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(_UpperCAmelCase ) == 9
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
lowerCAmelCase = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(-8 , 1 ) )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = [
-9,
100,
Node(7734_5112 ),
'dlrow olleH',
7,
5555,
0,
-192.5_5555,
'Hello, world!',
77.9,
Node(10 ),
None,
None,
12.20,
]
lowerCAmelCase = LinkedList()
for i in test_input:
linked_list.insert_tail(_UpperCAmelCase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(_UpperCAmelCase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
lowerCAmelCase = linked_list.delete_head()
assert result == -9
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
lowerCAmelCase = linked_list.delete_tail()
assert result == 12.2
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
lowerCAmelCase = linked_list.delete_nth(10 )
assert result is None
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node('Hello again, world!' ) )
assert (
str(_UpperCAmelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(_UpperCAmelCase )
assert (
str(_UpperCAmelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(_UpperCAmelCase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def _SCREAMING_SNAKE_CASE ():
from doctest import testmod
testmod()
lowerCAmelCase = LinkedList()
linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
print('\nDelete head' )
linked_list.delete_head()
print('Delete tail' )
linked_list.delete_tail()
print('\nPrint list:' )
linked_list.print_list()
print('\nReverse linked list' )
linked_list.reverse()
print('\nPrint list:' )
linked_list.print_list()
print('\nString representation of linked list:' )
print(_UpperCAmelCase )
print('\nReading/changing Node data using indexing:' )
print(F'Element at Position 1: {linked_list[1]}' )
lowerCAmelCase = input('Enter New Value: ' ).strip()
print('New list:' )
print(_UpperCAmelCase )
print(F'length of linked_list is : {len(_UpperCAmelCase )}' )
if __name__ == "__main__":
main()
| 4 | 0 |
def _a ( lowercase__ : int = 1_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = 0
SCREAMING_SNAKE_CASE__ : str = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 85 |
"""simple docstring"""
from __future__ import annotations
import requests
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(_UpperCAmelCase ).json()
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 10 ):
lowerCAmelCase = 'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
lowerCAmelCase = requests.get(_UpperCAmelCase ).json()[:max_stories]
return [get_hackernews_story(_UpperCAmelCase ) for story_id in story_ids]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 10 ):
lowerCAmelCase = hackernews_top_stories(_UpperCAmelCase )
return "\n".join('* [{title}]({url})'.format(**_UpperCAmelCase ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 4 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def __snake_case ( __UpperCamelCase : Dict ):
"""simple docstring"""
A_ , A_ = image.size
A_ , A_ = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
A_ = image.resize((w, h) ,resample=PIL_INTERPOLATION["lanczos"] )
A_ = np.array(__UpperCamelCase ).astype(np.floataa ) / 255.0
A_ = image[None].transpose(0 ,3 ,1 ,2 )
A_ = torch.from_numpy(__UpperCamelCase )
return 2.0 * image - 1.0
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : VQModel , UpperCAmelCase : UNetaDModel , UpperCAmelCase : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=UpperCAmelCase , unet=UpperCAmelCase , scheduler=UpperCAmelCase )
@torch.no_grad()
def __call__( self : int , UpperCAmelCase : Union[torch.Tensor, PIL.Image.Image] = None , UpperCAmelCase : Optional[int] = 1 , UpperCAmelCase : Optional[int] = 100 , UpperCAmelCase : Optional[float] = 0.0 , UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCAmelCase : Optional[str] = "pil" , UpperCAmelCase : bool = True , ):
if isinstance(UpperCAmelCase , PIL.Image.Image ):
A_ = 1
elif isinstance(UpperCAmelCase , torch.Tensor ):
A_ = image.shape[0]
else:
raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCAmelCase )}''' )
if isinstance(UpperCAmelCase , PIL.Image.Image ):
A_ = preprocess(UpperCAmelCase )
A_ , A_ = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
A_ = (batch_size, self.unet.config.in_channels // 2, height, width)
A_ = next(self.unet.parameters() ).dtype
A_ = randn_tensor(UpperCAmelCase , generator=UpperCAmelCase , device=self.device , dtype=UpperCAmelCase )
A_ = image.to(device=self.device , dtype=UpperCAmelCase )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(UpperCAmelCase , device=self.device )
A_ = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
A_ = 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_ = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
A_ = {}
if accepts_eta:
A_ = eta
for t in self.progress_bar(UpperCAmelCase ):
# concat latents and low resolution image in the channel dimension.
A_ = torch.cat([latents, image] , dim=1 )
A_ = self.scheduler.scale_model_input(UpperCAmelCase , UpperCAmelCase )
# predict the noise residual
A_ = self.unet(UpperCAmelCase , UpperCAmelCase ).sample
# compute the previous noisy sample x_t -> x_t-1
A_ = self.scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
# decode the image latents with the VQVAE
A_ = self.vqvae.decode(UpperCAmelCase ).sample
A_ = torch.clamp(UpperCAmelCase , -1.0 , 1.0 )
A_ = image / 2 + 0.5
A_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A_ = self.numpy_to_pil(UpperCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCAmelCase ) | 86 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any ):
lowerCAmelCase = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCAmelCase = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
lowerCAmelCase = 4
lowerCAmelCase = 48
lowerCAmelCase = 'pixelshuffle_aux'
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCAmelCase = [6, 6, 6, 6]
lowerCAmelCase = 60
lowerCAmelCase = [6, 6, 6, 6]
lowerCAmelCase = 'pixelshuffledirect'
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCAmelCase = 4
lowerCAmelCase = 'nearest+conv'
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
lowerCAmelCase = 1
lowerCAmelCase = 1
lowerCAmelCase = 126
lowerCAmelCase = 7
lowerCAmelCase = 255.0
lowerCAmelCase = ''
return config
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] ):
if "patch_embed.proj" in name and "layers" not in name:
lowerCAmelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
lowerCAmelCase = name.replace('patch_embed.norm' , 'embeddings.patch_embeddings.layernorm' )
if "layers" in name:
lowerCAmelCase = name.replace('layers' , 'encoder.stages' )
if "residual_group.blocks" in name:
lowerCAmelCase = name.replace('residual_group.blocks' , 'layers' )
if "attn.proj" in name:
lowerCAmelCase = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
lowerCAmelCase = name.replace('attn' , 'attention.self' )
if "norm1" in name:
lowerCAmelCase = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowerCAmelCase = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowerCAmelCase = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCAmelCase = name.replace('mlp.fc2' , 'output.dense' )
if "q_bias" in name:
lowerCAmelCase = name.replace('q_bias' , 'query.bias' )
if "k_bias" in name:
lowerCAmelCase = name.replace('k_bias' , 'key.bias' )
if "v_bias" in name:
lowerCAmelCase = name.replace('v_bias' , 'value.bias' )
if "cpb_mlp" in name:
lowerCAmelCase = name.replace('cpb_mlp' , 'continuous_position_bias_mlp' )
if "patch_embed.proj" in name:
lowerCAmelCase = name.replace('patch_embed.proj' , 'patch_embed.projection' )
if name == "norm.weight":
lowerCAmelCase = 'layernorm.weight'
if name == "norm.bias":
lowerCAmelCase = 'layernorm.bias'
if "conv_first" in name:
lowerCAmelCase = name.replace('conv_first' , 'first_convolution' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
lowerCAmelCase = name.replace('conv_last' , 'final_convolution' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
lowerCAmelCase = name.replace('conv_before_upsample.0' , 'conv_before_upsample' )
if "upsample.0" in name:
lowerCAmelCase = name.replace('upsample.0' , 'upsample.convolution_0' )
if "upsample.2" in name:
lowerCAmelCase = name.replace('upsample.2' , 'upsample.convolution_1' )
lowerCAmelCase = 'upsample.' + name
elif config.upsampler == "pixelshuffledirect":
lowerCAmelCase = name.replace('upsample.0.weight' , 'upsample.conv.weight' )
lowerCAmelCase = name.replace('upsample.0.bias' , 'upsample.conv.bias' )
else:
pass
else:
lowerCAmelCase = 'swin2sr.' + name
return name
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict ):
for key in orig_state_dict.copy().keys():
lowerCAmelCase = orig_state_dict.pop(_UpperCAmelCase )
if "qkv" in key:
lowerCAmelCase = key.split('.' )
lowerCAmelCase = int(key_split[1] )
lowerCAmelCase = int(key_split[4] )
lowerCAmelCase = config.embed_dim
if "weight" in key:
lowerCAmelCase = val[:dim, :]
lowerCAmelCase = val[dim : dim * 2, :]
lowerCAmelCase = val[-dim:, :]
else:
lowerCAmelCase = val[:dim]
lowerCAmelCase = val[dim : dim * 2]
lowerCAmelCase = val[-dim:]
pass
else:
lowerCAmelCase = val
return orig_state_dict
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple ):
lowerCAmelCase = get_config(_UpperCAmelCase )
lowerCAmelCase = SwinaSRForImageSuperResolution(_UpperCAmelCase )
model.eval()
lowerCAmelCase = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location='cpu' )
lowerCAmelCase = convert_state_dict(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase ,lowerCAmelCase = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
raise ValueError('Missing keys when converting: {}'.format(_UpperCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F'Unexpected key {key} in state_dict' )
# verify values
lowerCAmelCase = 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
lowerCAmelCase = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('RGB' )
lowerCAmelCase = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
lowerCAmelCase = 126 if 'Jpeg' in checkpoint_url else 256
lowerCAmelCase = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
lowerCAmelCase = transforms(_UpperCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
lowerCAmelCase = pixel_values[:, 0, :, :].unsqueeze(1 )
lowerCAmelCase = model(_UpperCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 512, 512] )
lowerCAmelCase = torch.tensor(
[[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 512, 512] )
lowerCAmelCase = torch.tensor(
[[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] )
assert (
outputs.reconstruction.shape == expected_shape
), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , _UpperCAmelCase , atol=1e-3 )
print('Looks ok!' )
lowerCAmelCase = {
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
'swin2SR-classical-sr-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
'swin2SR-classical-sr-x4-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
'swin2SR-compressed-sr-x4-48'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
'swin2SR-lightweight-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
),
}
lowerCAmelCase = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_UpperCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
model.push_to_hub(F'caidas/{model_name}' )
processor.push_to_hub(F'caidas/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
type=str,
help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 4 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''naver-clova-ix/donut-base-finetuned-docvqa'''
UpperCAmelCase__ = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
UpperCAmelCase__ = '''document_qa'''
UpperCAmelCase__ = AutoProcessor
UpperCAmelCase__ = VisionEncoderDecoderModel
UpperCAmelCase__ = ['''image''', '''text''']
UpperCAmelCase__ = ['''text''']
def __init__( self : int , *UpperCAmelCase__ : Dict , **UpperCAmelCase__ : Dict) ->List[str]:
'''simple docstring'''
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''')
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , UpperCAmelCase__ : "Image" , UpperCAmelCase__ : str) ->Optional[int]:
'''simple docstring'''
A__ = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
A__ = task_prompt.replace('''{user_input}''' , UpperCAmelCase__)
A__ = self.pre_processor.tokenizer(
UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ , return_tensors='''pt''').input_ids
A__ = self.pre_processor(UpperCAmelCase__ , return_tensors='''pt''').pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def SCREAMING_SNAKE_CASE ( self : int , UpperCAmelCase__ : List[str]) ->Any:
'''simple docstring'''
return self.model.generate(
inputs['''pixel_values'''].to(self.device) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=UpperCAmelCase__ , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=UpperCAmelCase__ , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=UpperCAmelCase__ , ).sequences
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : Optional[int]) ->Dict:
'''simple docstring'''
A__ = self.pre_processor.batch_decode(UpperCAmelCase__)[0]
A__ = sequence.replace(self.pre_processor.tokenizer.eos_token , '''''')
A__ = sequence.replace(self.pre_processor.tokenizer.pad_token , '''''')
A__ = re.sub(R'''<.*?>''' , '''''' , UpperCAmelCase__ , count=1).strip() # remove first task start token
A__ = self.pre_processor.tokenajson(UpperCAmelCase__)
return sequence["answer"]
| 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''megatron-bert'''
def __init__( self , _snake_case=2_90_56 , _snake_case=10_24 , _snake_case=24 , _snake_case=16 , _snake_case=40_96 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
| 4 | 0 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
"""
UpperCAmelCase = """\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""
UpperCAmelCase = """
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
predictions: list of predictions to score (as int64),
except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
references: list of ground truth labels corresponding to the predictions (as int64),
except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
\"accuracy\": Accuracy
\"f1\": F1 score
\"precision\": Precision@10
Examples:
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli') # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
>>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'precision@10': 1.0}
"""
def _snake_case ( __snake_case : List[Any] , __snake_case : str ):
"""simple docstring"""
return float((preds == labels).mean() )
def _snake_case ( __snake_case : Optional[int] , __snake_case : str ):
"""simple docstring"""
_lowerCamelCase : str = simple_accuracy(__snake_case , __snake_case )
_lowerCamelCase : Union[str, Any] = float(fa_score(y_true=__snake_case , y_pred=__snake_case ) )
return {
"accuracy": acc,
"f1": fa,
}
def _snake_case ( __snake_case : Optional[Any] , __snake_case : Tuple ):
"""simple docstring"""
_lowerCamelCase : Dict = np.array(__snake_case )
_lowerCamelCase : Optional[Any] = np.array(__snake_case )
_lowerCamelCase : List[Any] = en_sentvecs.shape[0]
# mean centering
_lowerCamelCase : str = en_sentvecs - np.mean(__snake_case , axis=0 )
_lowerCamelCase : Optional[int] = in_sentvecs - np.mean(__snake_case , axis=0 )
_lowerCamelCase : Union[str, Any] = cdist(__snake_case , __snake_case , """cosine""" )
_lowerCamelCase : str = np.array(range(__snake_case ) )
_lowerCamelCase : Optional[Any] = sim.argsort(axis=1 )[:, :10]
_lowerCamelCase : Dict = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class lowercase__ ( datasets.Metric ):
def UpperCamelCase_ ( self) -> Tuple:
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", """
"""\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", """
"""\"wiki-ner\"]""")
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int64""")
if self.config_name != """cvit-mkb-clsr"""
else datasets.Sequence(datasets.Value("""float32""")),
"""references""": datasets.Value("""int64""")
if self.config_name != """cvit-mkb-clsr"""
else datasets.Sequence(datasets.Value("""float32""")),
}) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if self.config_name != """cvit-mkb-clsr""" else None , )
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> Any:
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", """
"""\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", """
"""\"wiki-ner\"]""")
| 88 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> float:
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise Exception('Years to repay must be an integer > 0' )
# Yearly rate is divided by 12 to get monthly rate
_lowercase : Any = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
_lowercase : Any = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class a ( a__ ):
snake_case__ = 42
class a ( a__ , a__ ):
@register_to_config
def __init__( self , _snake_case = 3 , _snake_case = 3 , _snake_case = ("DownEncoderBlock2D",) , _snake_case = ("UpDecoderBlock2D",) , _snake_case = (64,) , _snake_case = 1 , _snake_case = "silu" , _snake_case = 3 , _snake_case = 32 , _snake_case = 2_56 , _snake_case = 32 , _snake_case = None , _snake_case = 0.18_215 , _snake_case = "group" , ):
"""simple docstring"""
super().__init__()
# pass init params to Encoder
lowerCAmelCase = Encoder(
in_channels=_snake_case , out_channels=_snake_case , down_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , double_z=_snake_case , )
lowerCAmelCase = vq_embed_dim if vq_embed_dim is not None else latent_channels
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
lowerCAmelCase = VectorQuantizer(_snake_case , _snake_case , beta=0.25 , remap=_snake_case , sane_index_shape=_snake_case )
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
# pass init params to Decoder
lowerCAmelCase = Decoder(
in_channels=_snake_case , out_channels=_snake_case , up_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , norm_type=_snake_case , )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = self.encoder(_snake_case )
lowerCAmelCase = self.quant_conv(_snake_case )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_snake_case )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = False , _snake_case = True ):
"""simple docstring"""
if not force_not_quantize:
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = self.quantize(_snake_case )
else:
lowerCAmelCase = h
lowerCAmelCase = self.post_quant_conv(_snake_case )
lowerCAmelCase = self.decoder(_snake_case , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = sample
lowerCAmelCase = self.encode(_snake_case ).latents
lowerCAmelCase = self.decode(_snake_case ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = list[list[int]]
# assigning initial values to the grid
__UpperCAmelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
__UpperCAmelCase = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def _snake_case ( A , A , A , A ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def _snake_case ( A ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def _snake_case ( A ) -> Matrix | None:
if location := find_empty_location(A ):
lowerCAmelCase__ , lowerCAmelCase__ = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(A , A , A , A ):
lowerCAmelCase__ = digit
if sudoku(A ) is not None:
return grid
lowerCAmelCase__ = 0
return None
def _snake_case ( A ) -> None:
for row in grid:
for cell in row:
print(A , end=''' ''' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 20)
print_solution(example_grid)
print('''\nExample grid solution:''')
__UpperCAmelCase = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''') | 90 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = vertices
lowerCAmelCase = {
(min(_snake_case ), max(_snake_case )): weight for edge, weight in edges.items()
}
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
lowerCAmelCase = weight
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Graph({min(self.vertices )} , {} )
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
lowerCAmelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
lowerCAmelCase = edge
lowerCAmelCase = weight
subgraph.add_edge(_snake_case , _snake_case )
return subgraph
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "p107_network.txt" ):
lowerCAmelCase = os.path.abspath(os.path.dirname(_UpperCAmelCase ) )
lowerCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = {}
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
with open(_UpperCAmelCase ) as f:
lowerCAmelCase = f.read().strip().split('\n' )
lowerCAmelCase = [line.split(',' ) for line in data]
for edgea in range(1 , len(_UpperCAmelCase ) ):
for edgea in range(_UpperCAmelCase ):
if adjaceny_matrix[edgea][edgea] != "-":
lowerCAmelCase = int(adjaceny_matrix[edgea][edgea] )
lowerCAmelCase = Graph(set(range(len(_UpperCAmelCase ) ) ) , _UpperCAmelCase )
lowerCAmelCase = graph.prims_algorithm()
lowerCAmelCase = sum(graph.edges.values() )
lowerCAmelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 4 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( snake_case__ : list[int] ):
if not nums:
return 0
A = nums[0]
A = 0
for num in nums[1:]:
A , A = (
max_excluding + num,
max(snake_case__ , snake_case__ ),
)
return max(snake_case__ , snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 91 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase )] )
lowerCAmelCase = np.array(_UpperCAmelCase )
lowerCAmelCase = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , _UpperCAmelCase ) ) , x.transpose() ) , _UpperCAmelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = (1, 2, 1)
lowerCAmelCase = (1, 1, 0, 7)
lowerCAmelCase = SARIMAX(
_UpperCAmelCase , exog=_UpperCAmelCase , order=_UpperCAmelCase , seasonal_order=_UpperCAmelCase )
lowerCAmelCase = model.fit(disp=_UpperCAmelCase , maxiter=600 , method='nm' )
lowerCAmelCase = model_fit.predict(1 , len(_UpperCAmelCase ) , exog=[test_match] )
return result[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = SVR(kernel='rbf' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = regressor.predict(_UpperCAmelCase )
return y_pred[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list ):
train_user.sort()
lowerCAmelCase = np.percentile(_UpperCAmelCase , 25 )
lowerCAmelCase = np.percentile(_UpperCAmelCase , 75 )
lowerCAmelCase = qa - qa
lowerCAmelCase = qa - (iqr * 0.1)
return low_lim
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : float ):
lowerCAmelCase = 0
lowerCAmelCase = 0
for i in list_vote:
if i > actual_result:
lowerCAmelCase = not_safe + 1
else:
if abs(abs(_UpperCAmelCase ) - abs(_UpperCAmelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
__UpperCamelCase : Optional[Any] = [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
__UpperCamelCase : Any = pd.DataFrame(
data_input, columns=['''total_user''', '''total_even''', '''days''']
)
__UpperCamelCase : Dict = Normalizer().fit_transform(data_input_df.values)
# split data
__UpperCamelCase : Dict = normalize_df[:, 2].tolist()
__UpperCamelCase : Union[str, Any] = normalize_df[:, 0].tolist()
__UpperCamelCase : List[str] = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
__UpperCamelCase : Optional[int] = normalize_df[:, [1, 2]].tolist()
__UpperCamelCase : Tuple = x[: len(x) - 1]
__UpperCamelCase : Any = x[len(x) - 1 :]
# for linear regression & sarimax
__UpperCamelCase : str = total_date[: len(total_date) - 1]
__UpperCamelCase : Union[str, Any] = total_user[: len(total_user) - 1]
__UpperCamelCase : List[Any] = total_match[: len(total_match) - 1]
__UpperCamelCase : Optional[Any] = total_date[len(total_date) - 1 :]
__UpperCamelCase : str = total_user[len(total_user) - 1 :]
__UpperCamelCase : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
__UpperCamelCase : Any = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
__UpperCamelCase : List[str] = '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
print('''Today\'s data is {not_str}safe.''')
| 4 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCase__ : str=3 , UpperCAmelCase__ : Optional[int]=64 , UpperCAmelCase__ : Union[str, Any]=None ):
'''simple docstring'''
lowercase : Optional[Any] =np.random.default_rng(UpperCAmelCase__ )
lowercase : Union[str, Any] =length
lowercase : List[Any] =rng.normal(size=(length,) ).astype(np.floataa )
lowercase : Dict =a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Tuple ):
'''simple docstring'''
return self.length
def __getitem__( self : Union[str, Any] , UpperCAmelCase__ : Any ):
'''simple docstring'''
return {"x": self.x[i], "y": self.y[i]}
class __SCREAMING_SNAKE_CASE ( torch.nn.Module ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : List[Any]=0 , UpperCAmelCase__ : List[Any]=0 , UpperCAmelCase__ : str=False ):
'''simple docstring'''
super().__init__()
lowercase : Any =torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowercase : List[str] =torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowercase : List[Any] =True
def lowerCamelCase_ ( self : str , UpperCAmelCase__ : Any=None ):
'''simple docstring'''
if self.first_batch:
print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
lowercase : Any =False
return x * self.a[0] + self.b[0]
class __SCREAMING_SNAKE_CASE ( torch.nn.Module ):
def __init__( self : List[Any] , UpperCAmelCase__ : Optional[Any]=0 , UpperCAmelCase__ : int=0 , UpperCAmelCase__ : Optional[Any]=False ):
'''simple docstring'''
super().__init__()
lowercase : Optional[int] =torch.nn.Parameter(torch.tensor(UpperCAmelCase__ ).float() )
lowercase : int =torch.nn.Parameter(torch.tensor(UpperCAmelCase__ ).float() )
lowercase : Tuple =True
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Optional[Any]=None ):
'''simple docstring'''
if self.first_batch:
print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
lowercase : List[Any] =False
return x * self.a + self.b
def _lowerCAmelCase ( __magic_name__ : Tuple , __magic_name__ : int = 16 ) -> Union[str, Any]:
from datasets import load_dataset
from transformers import AutoTokenizer
lowercase : Dict =AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase : Optional[int] ={'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
lowercase : Dict =load_dataset('''csv''' , data_files=__magic_name__ )
lowercase : int =datasets['''train'''].unique('''label''' )
lowercase : List[str] ={v: i for i, v in enumerate(__magic_name__ )}
def tokenize_function(__magic_name__ : Dict ):
# max_length=None => use the model max length (it's actually the default)
lowercase : Dict =tokenizer(
examples['''sentence1'''] , examples['''sentence2'''] , truncation=__magic_name__ , max_length=__magic_name__ , padding='''max_length''' )
if "label" in examples:
lowercase : List[Any] =[label_to_id[l] for l in examples['''label''']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase : Optional[int] =datasets.map(
__magic_name__ , batched=__magic_name__ , remove_columns=['''sentence1''', '''sentence2''', '''label'''] , )
def collate_fn(__magic_name__ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__magic_name__ , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(__magic_name__ , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
lowercase : Union[str, Any] =DataLoader(tokenized_datasets['''train'''] , shuffle=__magic_name__ , collate_fn=__magic_name__ , batch_size=2 )
lowercase : Tuple =DataLoader(tokenized_datasets['''validation'''] , shuffle=__magic_name__ , collate_fn=__magic_name__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 92 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'-m' , '--pretrained_model_name_or_path' , type=_UpperCAmelCase , default=_UpperCAmelCase , required=_UpperCAmelCase , help='Path to pretrained model or model identifier from huggingface.co/models.' , )
parser.add_argument(
'-c' , '--caption' , type=_UpperCAmelCase , default='robotic cat with wings' , help='Text used to generate images.' , )
parser.add_argument(
'-n' , '--images_num' , type=_UpperCAmelCase , default=4 , help='How much images to generate.' , )
parser.add_argument(
'-s' , '--seed' , type=_UpperCAmelCase , default=42 , help='Seed for random process.' , )
parser.add_argument(
'-ci' , '--cuda_id' , type=_UpperCAmelCase , default=0 , help='cuda_id.' , )
lowerCAmelCase = parser.parse_args()
return args
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] ):
if not len(_UpperCAmelCase ) == rows * cols:
raise ValueError('The specified number of rows and columns are not correct.' )
lowerCAmelCase ,lowerCAmelCase = imgs[0].size
lowerCAmelCase = Image.new('RGB' , size=(cols * w, rows * h) )
lowerCAmelCase ,lowerCAmelCase = grid.size
for i, img in enumerate(_UpperCAmelCase ):
grid.paste(_UpperCAmelCase , box=(i % cols * w, i // cols * h) )
return grid
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any]="robotic cat with wings" , _UpperCAmelCase : Optional[int]=7.5 , _UpperCAmelCase : Dict=50 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : int=42 , ):
lowerCAmelCase = torch.Generator(pipeline.device ).manual_seed(_UpperCAmelCase )
lowerCAmelCase = pipeline(
_UpperCAmelCase , guidance_scale=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , generator=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , ).images
lowerCAmelCase = int(math.sqrt(_UpperCAmelCase ) )
lowerCAmelCase = image_grid(_UpperCAmelCase , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__UpperCamelCase : Optional[Any] = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase : List[Any] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__UpperCamelCase : str = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__UpperCamelCase : Optional[int] = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__UpperCamelCase : List[str] = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')
__UpperCamelCase : Tuple = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase : Union[str, Any] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
__UpperCamelCase : Dict = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, '''unet''', unet)
else:
__UpperCamelCase : Dict = unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase : Optional[Any] = pipeline.to(unet.device)
__UpperCamelCase ,__UpperCamelCase : List[Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
__UpperCamelCase : int = os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))
| 4 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :Optional[int] = """switch_transformers"""
__magic_name__ :Optional[Any] = ["""past_key_values"""]
__magic_name__ :str = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , __UpperCAmelCase=3_2_1_2_8 , __UpperCAmelCase=7_6_8 , __UpperCAmelCase=6_4 , __UpperCAmelCase=2_0_4_8 , __UpperCAmelCase=6_4 , __UpperCAmelCase=1_2 , __UpperCAmelCase=3 , __UpperCAmelCase=1_2 , __UpperCAmelCase=3 , __UpperCAmelCase=1_2 , __UpperCAmelCase=8 , __UpperCAmelCase=False , __UpperCAmelCase=0.01 , __UpperCAmelCase="float32" , __UpperCAmelCase=False , __UpperCAmelCase=3_2 , __UpperCAmelCase=1_2_8 , __UpperCAmelCase=0.1 , __UpperCAmelCase=1E-6 , __UpperCAmelCase=0.0_01 , __UpperCAmelCase=0.0_01 , __UpperCAmelCase=1.0 , __UpperCAmelCase="relu" , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=0 , __UpperCAmelCase=1 , **__UpperCAmelCase , ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = vocab_size
lowerCAmelCase__ :int = d_model
lowerCAmelCase__ :Union[str, Any] = d_kv
lowerCAmelCase__ :Dict = d_ff
lowerCAmelCase__ :List[str] = num_sparse_encoder_layers
lowerCAmelCase__ :Union[str, Any] = num_layers
lowerCAmelCase__ :int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowerCAmelCase__ :List[Any] = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
lowerCAmelCase__ :Optional[Any] = self.num_layers // self.num_sparse_encoder_layers
else:
lowerCAmelCase__ :Optional[int] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
lowerCAmelCase__ :List[str] = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
lowerCAmelCase__ :Optional[int] = self.num_decoder_layers # HACK: this will create 0 sparse layers
lowerCAmelCase__ :List[str] = num_heads
lowerCAmelCase__ :Dict = num_experts
lowerCAmelCase__ :Optional[Any] = expert_capacity
lowerCAmelCase__ :Union[str, Any] = router_bias
lowerCAmelCase__ :Optional[Any] = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}" )
lowerCAmelCase__ :str = router_dtype
lowerCAmelCase__ :int = router_ignore_padding_tokens
lowerCAmelCase__ :Dict = relative_attention_num_buckets
lowerCAmelCase__ :Dict = relative_attention_max_distance
lowerCAmelCase__ :str = dropout_rate
lowerCAmelCase__ :List[Any] = layer_norm_epsilon
lowerCAmelCase__ :List[str] = initializer_factor
lowerCAmelCase__ :List[Any] = feed_forward_proj
lowerCAmelCase__ :str = use_cache
lowerCAmelCase__ :Any = add_router_probs
lowerCAmelCase__ :List[Any] = router_z_loss_coef
lowerCAmelCase__ :List[str] = router_aux_loss_coef
lowerCAmelCase__ :Union[str, Any] = self.feed_forward_proj.split('-' )
lowerCAmelCase__ :Dict = act_info[-1]
lowerCAmelCase__ :str = act_info[0] == 'gated'
if len(__UpperCAmelCase ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase ) > 2:
raise ValueError(
F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
lowerCAmelCase__ :Tuple = 'gelu_new'
super().__init__(
pad_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , is_encoder_decoder=__UpperCAmelCase , **__UpperCAmelCase , )
| 93 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : List[int] ):
lowerCAmelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(_UpperCAmelCase ) == len(_UpperCAmelCase ), F'{len(_UpperCAmelCase )} != {len(_UpperCAmelCase )}'
dest_layers.load_state_dict(layers_to_copy.state_dict() )
__UpperCamelCase : Optional[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCamelCase : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] ):
try:
lowerCAmelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
F' {n_student}' )
return list(range(_UpperCAmelCase ) )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ):
if n_student > n_teacher:
raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}' )
elif n_teacher == n_student:
return list(range(_UpperCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, PreTrainedModel] , _UpperCAmelCase : Union[str, Path] = "student" , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Optional[Any]=None , **_UpperCAmelCase : str , ):
lowerCAmelCase = 'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
assert (e is not None) or (d is not None), _msg
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
AutoTokenizer.from_pretrained(_UpperCAmelCase ).save_pretrained(_UpperCAmelCase ) # purely for convenience
lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).eval()
else:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ), F'teacher must be a model or string got type {type(_UpperCAmelCase )}'
lowerCAmelCase = teacher.config.to_diff_dict()
try:
lowerCAmelCase ,lowerCAmelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
init_kwargs.update({'encoder_layers': e, 'decoder_layers': d} )
except AttributeError: # T5
if hasattr(teacher.config , 'num_encoder_layers' ):
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
if hasattr(teacher.config , 'num_encoder_layers' ):
init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d} )
else:
init_kwargs.update({'num_layers': e, 'num_decoder_layers': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(_UpperCAmelCase )
# Copy weights
lowerCAmelCase = teacher.config_class(**_UpperCAmelCase )
lowerCAmelCase = AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
lowerCAmelCase = student.load_state_dict(teacher.state_dict() , strict=_UpperCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
lowerCAmelCase ,lowerCAmelCase = list(range(_UpperCAmelCase ) ), list(range(_UpperCAmelCase ) )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
F' {save_path}' )
student.save_pretrained(_UpperCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
if d_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
try:
if hasattr(
_UpperCAmelCase , 'prophetnet' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _UpperCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _UpperCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _UpperCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , _UpperCAmelCase )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' )
lowerCAmelCase = {
'teacher_type': teacher.config.model_type,
'copied_encoder_layers': e_layers_to_copy,
'copied_decoder_layers': d_layers_to_copy,
}
student.save_pretrained(_UpperCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 4 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__A )
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
UpperCamelCase_ = field(default='''summarization''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
UpperCamelCase_ = Features({'''text''': Value('''string''' )} )
UpperCamelCase_ = Features({'''summary''': Value('''string''' )} )
UpperCamelCase_ = "text"
UpperCamelCase_ = "summary"
@property
def A__ ( self : int ) -> Dict[str, str]:
'''simple docstring'''
return {self.text_column: "text", self.summary_column: "summary"}
| 94 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Dict = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = ['''LayoutXLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = ['''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
__UpperCamelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 4 | 0 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
),
}
class UpperCamelCase_ (__A ):
__magic_name__ = '''efficientformer'''
def __init__( self : int , lowerCAmelCase_ : List[int] = [3, 2, 6, 4] , lowerCAmelCase_ : List[int] = [48, 96, 224, 448] , lowerCAmelCase_ : List[bool] = [True, True, True, True] , lowerCAmelCase_ : int = 448 , lowerCAmelCase_ : int = 32 , lowerCAmelCase_ : int = 4 , lowerCAmelCase_ : int = 7 , lowerCAmelCase_ : int = 5 , lowerCAmelCase_ : int = 8 , lowerCAmelCase_ : int = 4 , lowerCAmelCase_ : float = 0.0 , lowerCAmelCase_ : int = 16 , lowerCAmelCase_ : int = 3 , lowerCAmelCase_ : int = 3 , lowerCAmelCase_ : int = 3 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : float = 0.0 , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : float = 1e-5 , lowerCAmelCase_ : str = "gelu" , lowerCAmelCase_ : float = 0.0_2 , lowerCAmelCase_ : float = 1e-12 , lowerCAmelCase_ : int = 224 , lowerCAmelCase_ : float = 1e-05 , **lowerCAmelCase_ : List[str] , ) -> None:
super().__init__(**lowerCAmelCase_ )
UpperCAmelCase_ : int = hidden_act
UpperCAmelCase_ : Optional[int] = hidden_dropout_prob
UpperCAmelCase_ : Any = hidden_sizes
UpperCAmelCase_ : Dict = num_hidden_layers
UpperCAmelCase_ : List[Any] = num_attention_heads
UpperCAmelCase_ : int = initializer_range
UpperCAmelCase_ : Any = layer_norm_eps
UpperCAmelCase_ : Any = patch_size
UpperCAmelCase_ : Optional[int] = num_channels
UpperCAmelCase_ : Any = depths
UpperCAmelCase_ : Optional[Any] = mlp_expansion_ratio
UpperCAmelCase_ : Tuple = downsamples
UpperCAmelCase_ : List[Any] = dim
UpperCAmelCase_ : Tuple = key_dim
UpperCAmelCase_ : Tuple = attention_ratio
UpperCAmelCase_ : Any = resolution
UpperCAmelCase_ : Any = pool_size
UpperCAmelCase_ : str = downsample_patch_size
UpperCAmelCase_ : Optional[Any] = downsample_stride
UpperCAmelCase_ : Tuple = downsample_pad
UpperCAmelCase_ : Optional[Any] = drop_path_rate
UpperCAmelCase_ : List[str] = num_metaad_blocks
UpperCAmelCase_ : Optional[Any] = distillation
UpperCAmelCase_ : Optional[Any] = use_layer_scale
UpperCAmelCase_ : Dict = layer_scale_init_value
UpperCAmelCase_ : Any = image_size
UpperCAmelCase_ : Optional[Any] = batch_norm_eps
| 95 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase = F'Resistor at index {index} has a negative or zero value!'
raise ValueError(_UpperCAmelCase )
first_sum += 1 / float(_UpperCAmelCase )
index += 1
return 1 / first_sum
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowerCAmelCase = F'Resistor at index {index} has a negative value!'
raise ValueError(_UpperCAmelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCamelCase = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class __A ( SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
UpperCAmelCase__ = GPTSwaTokenizer
UpperCAmelCase__ = False
UpperCAmelCase__ = True
UpperCAmelCase__ = False
def lowerCamelCase__ ( self : int ) -> List[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
__magic_name__: Any = GPTSwaTokenizer(__snake_case , eos_token="""<unk>""" , bos_token="""<unk>""" , pad_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self : List[Any] , __snake_case : List[Any] ) -> Optional[int]:
__magic_name__: Tuple = """This is a test"""
__magic_name__: Dict = """This is a test"""
return input_text, output_text
def lowerCamelCase__ ( self : Dict ) -> str:
__magic_name__: Union[str, Any] = """<s>"""
__magic_name__: Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case ) , __snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case ) , __snake_case )
def lowerCamelCase__ ( self : Tuple ) -> Optional[int]:
__magic_name__: Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(__snake_case ) , 2_0_0_0 )
def lowerCamelCase__ ( self : Any ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 2_0_0_0 )
def lowerCamelCase__ ( self : List[str] ) -> List[str]:
__magic_name__: List[Any] = GPTSwaTokenizer(__snake_case )
__magic_name__: str = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__snake_case , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2] )
__magic_name__: Optional[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
# fmt: off
self.assertListEqual(
__snake_case , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] , )
# fmt: on
__magic_name__: List[Any] = tokenizer.convert_tokens_to_ids(__snake_case )
self.assertListEqual(
__snake_case , [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0] , )
__magic_name__: Tuple = tokenizer.convert_ids_to_tokens(__snake_case )
# fmt: off
self.assertListEqual(
__snake_case , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] )
# fmt: on
def lowerCamelCase__ ( self : Tuple ) -> Union[str, Any]:
__magic_name__: Tuple = GPTSwaTokenizer(__snake_case )
__magic_name__: Any = ["""This is a test""", """I was born in 92000, and this is falsé."""]
__magic_name__: Dict = [
[4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
[2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(__snake_case , __snake_case ):
self.assertListEqual(tokenizer.encode_fast(__snake_case ) , __snake_case )
# Test that decode_fast returns the input text
for text, token_ids in zip(__snake_case , __snake_case ):
self.assertEqual(tokenizer.decode_fast(__snake_case ) , __snake_case )
@slow
def lowerCamelCase__ ( self : List[str] ) -> Optional[Any]:
__magic_name__: Tuple = [
"""<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')""",
"""Hey there, how are you doing this fine day?""",
"""This is a text with a trailing spaces followed by a dot .""",
"""Häj sväjs lillebrör! =)""",
"""Det är inget fel på Mr. Cool""",
]
# fmt: off
__magic_name__: str = {"""input_ids""": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__snake_case , model_name="""AI-Sweden/gpt-sw3-126m""" , sequences=__snake_case , )
| 96 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class a ( a__ ):
snake_case__ = '''glpn'''
def __init__( self , _snake_case=3 , _snake_case=4 , _snake_case=[2, 2, 2, 2] , _snake_case=[8, 4, 2, 1] , _snake_case=[32, 64, 1_60, 2_56] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[1, 2, 5, 8] , _snake_case=[4, 4, 4, 4] , _snake_case="gelu" , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=0.1 , _snake_case=1E-6 , _snake_case=64 , _snake_case=10 , _snake_case=-1 , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
lowerCAmelCase = num_channels
lowerCAmelCase = num_encoder_blocks
lowerCAmelCase = depths
lowerCAmelCase = sr_ratios
lowerCAmelCase = hidden_sizes
lowerCAmelCase = patch_sizes
lowerCAmelCase = strides
lowerCAmelCase = mlp_ratios
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = initializer_range
lowerCAmelCase = drop_path_rate
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = decoder_hidden_size
lowerCAmelCase = max_depth
lowerCAmelCase = head_in_index
| 4 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowercase__:
"""simple docstring"""
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : Any ) -> str:
lowercase_ = data
lowercase_ = None
class lowercase__:
"""simple docstring"""
def __init__( self : Any ) -> str:
lowercase_ = None
lowercase_ = None
def __iter__( self : str ) -> Iterator[Any]:
lowercase_ = self.head
while self.head:
yield node.data
lowercase_ = node.next
if node == self.head:
break
def __len__( self : Tuple ) -> int:
return sum(1 for _ in self )
def __repr__( self : Tuple ) -> List[str]:
return "->".join(str(SCREAMING_SNAKE_CASE_ ) for item in iter(self ) )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Any ) -> None:
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Any ) -> None:
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any ) -> None:
if index < 0 or index > len(self ):
raise IndexError('''list index out of range.''' )
lowercase_ = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
lowercase_ = new_node # first node points itself
lowercase_ = lowercase_ = new_node
elif index == 0: # insert at head
lowercase_ = self.head
lowercase_ = lowercase_ = new_node
else:
lowercase_ = self.head
for _ in range(index - 1 ):
lowercase_ = temp.next
lowercase_ = temp.next
lowercase_ = new_node
if index == len(self ) - 1: # insert at tail
lowercase_ = new_node
def _lowercase ( self : List[Any] ) -> Dict:
return self.delete_nth(0 )
def _lowercase ( self : Any ) -> Any:
return self.delete_nth(len(self ) - 1 )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : int = 0 ) -> Any:
if not 0 <= index < len(self ):
raise IndexError('''list index out of range.''' )
lowercase_ = self.head
if self.head == self.tail: # just one node
lowercase_ = lowercase_ = None
elif index == 0: # delete head node
lowercase_ = self.tail.next.next
lowercase_ = self.head.next
else:
lowercase_ = self.head
for _ in range(index - 1 ):
lowercase_ = temp.next
lowercase_ = temp.next
lowercase_ = temp.next.next
if index == len(self ) - 1: # delete at tail
lowercase_ = temp
return delete_node.data
def _lowercase ( self : List[Any] ) -> bool:
return len(self ) == 0
def a ( ):
'''simple docstring'''
lowercase_ = CircularLinkedList()
assert len(snake_case__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(snake_case__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(snake_case__ ) == i
circular_linked_list.insert_nth(snake_case__ , i + 1 )
assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 97 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , _snake_case=10_00 , ):
"""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
lowerCAmelCase = range_bbox
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase = bbox[i, j, 3]
lowerCAmelCase = bbox[i, j, 1]
lowerCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase = bbox[i, j, 2]
lowerCAmelCase = bbox[i, j, 0]
lowerCAmelCase = t
lowerCAmelCase = tf.convert_to_tensor(_snake_case )
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 = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForMaskedLM(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForSequenceClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForTokenClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
snake_case__ = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ = False
snake_case__ = True
snake_case__ = 1_0
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = TFLayoutLMModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@unittest.skip('Onnx compliancy broke with TF 2.10' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def _SCREAMING_SNAKE_CASE ():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowerCAmelCase = tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
lowerCAmelCase = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the sequence output on [0, :3, :3]
lowerCAmelCase = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , _snake_case , atol=1E-3 ) )
# test the pooled output on [1, :3]
lowerCAmelCase = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , _snake_case , atol=1E-3 ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=2 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
lowerCAmelCase = outputs.loss
lowerCAmelCase = (2,)
self.assertEqual(loss.shape , _snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = (2, 2)
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=13 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the shape of the logits
lowerCAmelCase = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , _snake_case )
self.assertEqual(outputs.end_logits.shape , _snake_case )
| 4 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def a__ ( lowercase : Dict, lowercase : str, lowercase : str, lowercase : Path, lowercase : str = None, lowercase : str = None, lowercase : str = None, ) -> str:
"""simple docstring"""
if config_name_or_path is None:
_UpperCamelCase = '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''
if generator_tokenizer_name_or_path is None:
_UpperCamelCase = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
_UpperCamelCase = question_encoder_name_or_path
_UpperCamelCase = RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration
# Save model.
_UpperCamelCase = RagConfig.from_pretrained(lowercase )
_UpperCamelCase = AutoConfig.from_pretrained(lowercase )
_UpperCamelCase = AutoConfig.from_pretrained(lowercase )
_UpperCamelCase = gen_config
_UpperCamelCase = question_encoder_config
_UpperCamelCase = model_class.from_pretrained_question_encoder_generator(
lowercase, lowercase, config=lowercase )
rag_model.save_pretrained(lowercase )
# Sanity check.
model_class.from_pretrained(lowercase )
# Save tokenizers.
_UpperCamelCase = AutoTokenizer.from_pretrained(lowercase )
gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''' )
_UpperCamelCase = AutoTokenizer.from_pretrained(lowercase )
question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''' )
if __name__ == "__main__":
lowercase__ : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'--model_type',
choices=['rag_sequence', 'rag_token'],
required=True,
type=str,
help='RAG model type: rag_sequence, rag_token',
)
parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
parser.add_argument(
'--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
)
parser.add_argument(
'--generator_tokenizer_name_or_path',
type=str,
help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
)
parser.add_argument(
'--question_encoder_tokenizer_name_or_path',
type=str,
help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
)
parser.add_argument(
'--config_name_or_path',
type=str,
help=(
'Identifier of the model config to use, if not provided, resolves to a base config for a given'
' ``model_type``'
),
)
lowercase__ : Optional[int] = parser.parse_args()
lowercase__ : Union[str, Any] = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 98 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__UpperCamelCase : Union[str, Any] = '''examples/'''
__UpperCamelCase : str = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
'''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
__UpperCamelCase : List[str] = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__UpperCamelCase : Optional[int] = '''README.md'''
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : Any ):
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase ,lowerCAmelCase = REPLACE_PATTERNS[pattern]
lowerCAmelCase = replace.replace('VERSION' , _UpperCAmelCase )
lowerCAmelCase = re_pattern.sub(_UpperCAmelCase , _UpperCAmelCase )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, Any] ):
for folder, directories, fnames in os.walk(_UpperCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase , pattern='examples' )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if not patch:
update_version_in_examples(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = '🤗 Transformers currently provides the following architectures'
lowerCAmelCase = '1. Want to contribute a new model?'
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.readlines()
# Find the start of the list.
lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
lowerCAmelCase = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
with open(REPLACE_FILES['init'] , 'r' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase = REPLACE_PATTERNS['init'][0].search(_UpperCAmelCase ).groups()[0]
return packaging.version.parse(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple=False ):
lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
lowerCAmelCase = default_version.base_version
elif patch:
lowerCAmelCase = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
lowerCAmelCase = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
lowerCAmelCase = input(F'Which version are you releasing? [{default_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = default_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase , patch=_UpperCAmelCase )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = get_version()
lowerCAmelCase = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
lowerCAmelCase = input(F'Which version are we developing now? [{dev_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = dev_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 4 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self , __A , __A=2 , __A=True , __A=False , __A=10 , __A=3 , __A=32 * 8 , __A=32 * 8 , __A=4 , __A=64 , ):
__a = parent
__a = batch_size
__a = is_training
__a = use_auxiliary_loss
__a = num_queries
__a = num_channels
__a = min_size
__a = max_size
__a = num_labels
__a = hidden_dim
__a = hidden_dim
def snake_case_ ( self ):
__a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
__a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
__a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
__a = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
__a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def snake_case_ ( self ):
__a = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__a = self.num_queries
__a = self.num_labels
__a = [1, 1, 1, 1]
__a = self.num_channels
__a = 64
__a = 128
__a = self.hidden_dim
__a = self.hidden_dim
__a = self.hidden_dim
return config
def snake_case_ ( self ):
__a , __a , __a , __a , __a = self.prepare_config_and_inputs()
__a = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def snake_case_ ( self , __A , __A ):
__a = output.encoder_hidden_states
__a = output.pixel_decoder_hidden_states
__a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_layers )
def snake_case_ ( self , __A , __A , __A , __A=False ):
with torch.no_grad():
__a = MaskaFormerModel(config=__A )
model.to(__A )
model.eval()
__a = model(pixel_values=__A , pixel_mask=__A )
__a = model(__A , output_hidden_states=__A )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def snake_case_ ( self , __A , __A , __A , __A , __A ):
__a = MaskaFormerForUniversalSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__a = model(pixel_values=__A , pixel_mask=__A )
__a = model(__A )
comm_check_on_output(__A )
__a = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __UpperCAmelCase ( __A , __A , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
_lowerCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def snake_case_ ( self ):
__a = MaskaFormerModelTester(self )
__a = ConfigTester(self , config_class=__A , has_text_modality=__A )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__A , **__A , output_hidden_states=__A )
def snake_case_ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def snake_case_ ( self ):
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def snake_case_ ( self ):
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def snake_case_ ( self ):
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def snake_case_ ( self ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def snake_case_ ( self ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(__A )
__a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a = [*signature.parameters.keys()]
__a = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __A )
@slow
def snake_case_ ( self ):
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__a = MaskaFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def snake_case_ ( self ):
__a = (self.model_tester.min_size,) * 2
__a = {
"""pixel_values""": torch.randn((2, 3, *size) , device=__A ),
"""mask_labels""": torch.randn((2, 10, *size) , device=__A ),
"""class_labels""": torch.zeros(2 , 10 , device=__A ).long(),
}
__a = self.model_tester.get_config()
__a = MaskaFormerForUniversalSegmentation(__A ).to(__A )
__a = model(**__A )
self.assertTrue(outputs.loss is not None )
def snake_case_ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__A , **__A , output_hidden_states=__A )
def snake_case_ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(__A ).to(__A )
__a = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def snake_case_ ( self ):
if not self.model_tester.is_training:
return
__a = self.all_model_classes[1]
__a , __a , __a , __a , __a = self.model_tester.prepare_config_and_inputs()
__a = model_class(__A )
model.to(__A )
model.train()
__a = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def snake_case_ ( self ):
__a = self.all_model_classes[1]
__a , __a , __a , __a , __a = self.model_tester.prepare_config_and_inputs()
__a = True
__a = True
__a = model_class(__A ).to(__A )
model.train()
__a = model(__A , mask_labels=__A , class_labels=__A )
__a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
SCREAMING_SNAKE_CASE = 1E-4
def a ():
__a = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case_ ( self ):
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def snake_case_ ( self ):
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def snake_case_ ( self ):
__a = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__A )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(__A , return_tensors="""pt""" ).to(__A )
__a = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 384, 384) )
with torch.no_grad():
__a = model(**__A )
__a = torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
__a = torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
__a = torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def snake_case_ ( self ):
__a = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__A ).eval()
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(__A , return_tensors="""pt""" ).to(__A )
__a = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 384, 384) )
with torch.no_grad():
__a = model(**__A )
# masks_queries_logits
__a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__a = [
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
__a = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
__a = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__a = torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def snake_case_ ( self ):
__a = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__A ).eval()
__a = self.default_image_processor
__a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="""pt""" , )
__a = inputs["""pixel_values"""].to(__A )
__a = [el.to(__A ) for el in inputs["""mask_labels"""]]
__a = [el.to(__A ) for el in inputs["""class_labels"""]]
with torch.no_grad():
__a = model(**__A )
self.assertTrue(outputs.loss is not None )
| 99 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
__UpperCamelCase : Optional[int] = pytest.mark.integration
@require_faiss
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(_snake_case ) for x in np.arange(30 ).tolist()]} )
return dset
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
lowerCAmelCase = dset.map(
lambda _snake_case , _snake_case : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=_snake_case , keep_in_memory=_snake_case )
lowerCAmelCase = dset.add_faiss_index('vecs' , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT , )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_snake_case ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(_snake_case , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
lowerCAmelCase = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCAmelCase = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
lowerCAmelCase = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=_snake_case )
lowerCAmelCase ,lowerCAmelCase = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertRaises(_snake_case , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
lowerCAmelCase = np.eye(5 , dtype=np.floataa )[::-1]
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case )
self.assertRaises(_snake_case , index.search_batch , queries[0] )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , _snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
lowerCAmelCase = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(_snake_case ):
lowerCAmelCase = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = faiss.IndexFlat(5 )
lowerCAmelCase = FaissIndex(custom_index=_snake_case )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def UpperCamelCase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_snake_case ) as tmp_file:
index.save(tmp_file.name )
lowerCAmelCase = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Dict ):
import faiss
lowerCAmelCase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
lowerCAmelCase = 'index.faiss'
lowerCAmelCase = F'mock://{index_name}'
index.save(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCAmelCase = FaissIndex.load(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCAmelCase = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase = 1
lowerCAmelCase ,lowerCAmelCase = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCAmelCase = Elasticsearch()
lowerCAmelCase = {'acknowledged': True}
lowerCAmelCase = ElasticSearchIndex(es_client=_snake_case )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
lowerCAmelCase = 'foo'
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
lowerCAmelCase = 'foo'
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCAmelCase ,lowerCAmelCase = index.search(_snake_case , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
lowerCAmelCase = ['foo', 'bar', 'foobar']
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , _snake_case )
# batched queries with timeout
lowerCAmelCase = ['foo', 'bar', 'foobar']
lowerCAmelCase = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCAmelCase ,lowerCAmelCase = index.search_batch(_snake_case , request_timeout=30 )
lowerCAmelCase = [scores[0] for scores in total_scores]
lowerCAmelCase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_snake_case ) , 0 )
self.assertListEqual([1, 1, 1] , _snake_case )
| 4 | 0 |
def __snake_case ( lowerCAmelCase_ = 1_0_0 ) -> int:
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F'{solution() = }')
| 100 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = IFInpaintingPipeline
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._get_dummy_components()
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_save_load_local()
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 4 | 0 |
from collections import Counter
from timeit import timeit
def a__ ( A__ = "", ):
return sum(c % 2 for c in Counter(input_str.replace(' ', '' ).lower() ).values() ) < 2
def a__ ( A__ = "" ):
if len(A__ ) == 0:
return True
SCREAMING_SNAKE_CASE_ : Optional[int] = input_str.replace(' ', '' ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
SCREAMING_SNAKE_CASE_ : dict[str, int] = {}
for character in lower_case_input_str:
SCREAMING_SNAKE_CASE_ : Optional[Any] = character_freq_dict.get(A__, 0 ) + 1
SCREAMING_SNAKE_CASE_ : str = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def a__ ( A__ = "" ):
print('\nFor string = ', A__, ':' )
print(
'> can_string_be_rearranged_as_palindrome_counter()', '\tans =', can_string_be_rearranged_as_palindrome_counter(A__ ), '\ttime =', timeit(
'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)', setup='import __main__ as z', ), 'seconds', )
print(
'> can_string_be_rearranged_as_palindrome()', '\tans =', can_string_be_rearranged_as_palindrome(A__ ), '\ttime =', timeit(
'z.can_string_be_rearranged_as_palindrome(z.check_str)', setup='import __main__ as z', ), 'seconds', )
if __name__ == "__main__":
lowerCAmelCase__ : int =input(
'Enter string to determine if it can be rearranged as a palindrome or not: '
).strip()
benchmark(check_str)
lowerCAmelCase__ : List[Any] =can_string_be_rearranged_as_palindrome_counter(check_str)
print(F"""{check_str} can {"" if status else "not "}be rearranged as a palindrome""")
| 101 |
"""simple docstring"""
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 a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
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
lowerCAmelCase = self.vocab_size - 1
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
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 = 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 , )
lowerCAmelCase = 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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , head_mask=_snake_case )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTDoubleHeadsModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = OpenAIGPTForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class a ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
snake_case__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
snake_case__ = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case=False ):
"""simple docstring"""
lowerCAmelCase = super()._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_snake_case )
return inputs_dict
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , n_embd=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = OpenAIGPTModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@require_torch
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(_snake_case )
lowerCAmelCase = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=_snake_case ) # the president is
lowerCAmelCase = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowerCAmelCase = model.generate(_snake_case , do_sample=_snake_case )
self.assertListEqual(output_ids[0].tolist() , _snake_case )
| 4 | 0 |
"""simple docstring"""
# Copyright 2022 The HuggingFace 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.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__magic_name__ : Any = """Run commands across TPU VMs for initial setup before running `accelerate launch`."""
def UpperCamelCase (SCREAMING_SNAKE_CASE=None ):
if subparsers is not None:
UpperCamelCase : Optional[Any] = subparsers.add_parser("""tpu-config""" , description=_description )
else:
UpperCamelCase : Optional[Any] = argparse.ArgumentParser("""Accelerate tpu-config command""" , description=_description )
# Core arguments
UpperCamelCase : Any = parser.add_argument_group(
"""Config Arguments""" , """Arguments that can be configured through `accelerate config`.""" )
config_args.add_argument(
"""--config_file""" , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help="""Path to the config file to use for accelerate.""" , )
config_args.add_argument(
"""--tpu_name""" , default=SCREAMING_SNAKE_CASE , help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file.""" , )
config_args.add_argument(
"""--tpu_zone""" , default=SCREAMING_SNAKE_CASE , help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file.""" , )
UpperCamelCase : int = parser.add_argument_group("""TPU Arguments""" , """Arguments for options ran inside the TPU.""" )
pod_args.add_argument(
"""--use_alpha""" , action="""store_true""" , help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.""" , )
pod_args.add_argument(
"""--command_file""" , default=SCREAMING_SNAKE_CASE , help="""The path to the file containing the commands to run on the pod on startup.""" , )
pod_args.add_argument(
"""--command""" , action="""append""" , nargs="""+""" , help="""A command to run on the pod. Can be passed multiple times.""" , )
pod_args.add_argument(
"""--install_accelerate""" , action="""store_true""" , help="""Whether to install accelerate on the pod. Defaults to False.""" , )
pod_args.add_argument(
"""--accelerate_version""" , default="""latest""" , help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.""" , )
pod_args.add_argument(
"""--debug""" , action="""store_true""" , help="""If set, will print the command that would be run instead of running it.""" )
if subparsers is not None:
parser.set_defaults(func=SCREAMING_SNAKE_CASE )
return parser
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
UpperCamelCase : Any = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE ):
UpperCamelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCamelCase : Tuple = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCamelCase : Optional[int] = defaults.commands
if not args.tpu_name:
UpperCamelCase : Dict = defaults.tpu_name
if not args.tpu_zone:
UpperCamelCase : Optional[Any] = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCamelCase : int = """git+https://github.com/huggingface/accelerate.git"""
elif args.accelerate_version == "latest":
UpperCamelCase : Dict = """accelerate -U"""
elif isinstance(parse(args.accelerate_version ) , SCREAMING_SNAKE_CASE ):
UpperCamelCase : int = f"""accelerate=={args.accelerate_version}"""
if not args.command_file and not args.command:
raise ValueError("""You must specify either a command file or a command to run on the pod.""" )
if args.command_file:
with open(args.command_file , """r""" ) as f:
UpperCamelCase : Optional[Any] = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Union[str, Any] = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCamelCase : Optional[int] = ["""cd /usr/share"""]
if args.install_accelerate:
new_cmd += [f"""pip install {args.accelerate_version}"""]
new_cmd += args.command
UpperCamelCase : Tuple = """; """.join(SCREAMING_SNAKE_CASE )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCamelCase : int = ["""gcloud"""]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f"""Running {" ".join(SCREAMING_SNAKE_CASE )}""" )
return
subprocess.run(SCREAMING_SNAKE_CASE )
print("""Successfully setup pod.""" )
def UpperCamelCase ():
UpperCamelCase : Any = tpu_command_parser()
UpperCamelCase : int = parser.parse_args()
tpu_command_launcher(SCREAMING_SNAKE_CASE )
| 102 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase : str = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=_UpperCAmelCase , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=_UpperCAmelCase , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=_UpperCAmelCase , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=_UpperCAmelCase , default='data/dump' , help='The dump file prefix.' )
lowerCAmelCase = parser.parse_args()
logger.info(F'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
lowerCAmelCase = BertTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
lowerCAmelCase = RobertaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `<s>`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
lowerCAmelCase = GPTaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
lowerCAmelCase = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(F'Loading text from {args.file_path}' )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
lowerCAmelCase = fp.readlines()
logger.info('Start encoding' )
logger.info(F'{len(_UpperCAmelCase )} examples to process.' )
lowerCAmelCase = []
lowerCAmelCase = 0
lowerCAmelCase = 1_0000
lowerCAmelCase = time.time()
for text in data:
lowerCAmelCase = F'{bos} {text.strip()} {sep}'
lowerCAmelCase = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
rslt.append(_UpperCAmelCase )
iter += 1
if iter % interval == 0:
lowerCAmelCase = time.time()
logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
lowerCAmelCase = time.time()
logger.info('Finished binarization' )
logger.info(F'{len(_UpperCAmelCase )} examples processed.' )
lowerCAmelCase = F'{args.dump_file}.{args.tokenizer_name}.pickle'
lowerCAmelCase = tokenizer.vocab_size
if vocab_size < (1 << 16):
lowerCAmelCase = [np.uintaa(_UpperCAmelCase ) for d in rslt]
else:
lowerCAmelCase = [np.intaa(_UpperCAmelCase ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F'Dump to {dp_file}' )
with open(_UpperCAmelCase , 'wb' ) as handle:
pickle.dump(rslt_ , _UpperCAmelCase , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 4 | 0 |
"""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 = logging.get_logger(__name__)
snake_case = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
snake_case = {
'''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 = {
'''allenai/longformer-base-4096''': 4_0_9_6,
'''allenai/longformer-large-4096''': 4_0_9_6,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 4_0_9_6,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 4_0_9_6,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 4_0_9_6,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def snake_case ( ) -> Tuple:
_snake_case = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_snake_case = bs[:]
_snake_case = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCAmelCase_ )
cs.append(2**8 + n )
n += 1
_snake_case = [chr(lowerCAmelCase_ ) for n in cs]
return dict(zip(lowerCAmelCase_ , lowerCAmelCase_ ) )
def snake_case ( lowerCAmelCase_ ) -> Optional[Any]:
_snake_case = set()
_snake_case = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_snake_case = char
return pairs
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : Dict = VOCAB_FILES_NAMES
A__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[int] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any]="replace" , __lowerCamelCase : Union[str, Any]="<s>" , __lowerCamelCase : Optional[int]="</s>" , __lowerCamelCase : List[Any]="</s>" , __lowerCamelCase : str="<s>" , __lowerCamelCase : Dict="<unk>" , __lowerCamelCase : Any="<pad>" , __lowerCamelCase : Optional[Any]="<mask>" , __lowerCamelCase : List[str]=False , **__lowerCamelCase : Optional[Any] , ):
"""simple docstring"""
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
super().__init__(
errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding='''utf-8''' ) as vocab_handle:
_snake_case = json.load(__lowerCamelCase )
_snake_case = {v: k for k, v in self.encoder.items()}
_snake_case = errors # how to handle errors in decoding
_snake_case = bytes_to_unicode()
_snake_case = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCamelCase , encoding='''utf-8''' ) as merges_handle:
_snake_case = merges_handle.read().split('''\n''' )[1:-1]
_snake_case = [tuple(merge.split() ) for merge in bpe_merges]
_snake_case = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
_snake_case = {}
_snake_case = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_snake_case = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def __UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
return len(self.encoder )
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def __UpperCAmelCase ( self : int , __lowerCamelCase : List[Any] ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
_snake_case = tuple(__lowerCamelCase )
_snake_case = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
_snake_case = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_snake_case , _snake_case = bigram
_snake_case = []
_snake_case = 0
while i < len(__lowerCamelCase ):
try:
_snake_case = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_snake_case = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_snake_case = tuple(__lowerCamelCase )
_snake_case = new_word
if len(__lowerCamelCase ) == 1:
break
else:
_snake_case = get_pairs(__lowerCamelCase )
_snake_case = ''' '''.join(__lowerCamelCase )
_snake_case = word
return word
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : int ):
"""simple docstring"""
_snake_case = []
for token in re.findall(self.pat , __lowerCamelCase ):
_snake_case = ''''''.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(__lowerCamelCase ).split(''' ''' ) )
return bpe_tokens
def __UpperCAmelCase ( self : str , __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def __UpperCAmelCase ( self : int , __lowerCamelCase : Dict ):
"""simple docstring"""
return self.decoder.get(__lowerCamelCase )
def __UpperCAmelCase ( self : Any , __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
_snake_case = ''''''.join(__lowerCamelCase )
_snake_case = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_snake_case = os.path.join(
__lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_snake_case = os.path.join(
__lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + '''\n''' )
_snake_case = 0
with open(__lowerCamelCase , '''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 __lowerCamelCase : 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!''' )
_snake_case = token_index
writer.write(''' '''.join(__lowerCamelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
def __UpperCAmelCase ( self : Any , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_snake_case = [self.cls_token_id]
_snake_case = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : 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 )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def __UpperCAmelCase ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
_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 + sep + token_ids_a + sep ) * [0]
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : Tuple , __lowerCamelCase : List[Any]=False , **__lowerCamelCase : Dict ):
"""simple docstring"""
_snake_case = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
_snake_case = ''' ''' + text
return (text, kwargs)
| 103 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
'''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
'''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
),
'''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''bert'''
def __init__( self , _snake_case=3_05_22 , _snake_case=7_68 , _snake_case=12 , _snake_case=12 , _snake_case=30_72 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , _snake_case=None , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
lowerCAmelCase = classifier_dropout
class a ( a__ ):
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 4 | 0 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : Optional[int] ) -> str:
"""simple docstring"""
A__ = [0] * len(UpperCAmelCase_ )
A__ = []
A__ = [1] * len(UpperCAmelCase_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(UpperCAmelCase_ ) ):
if indegree[i] == 0:
queue.append(UpperCAmelCase_ )
while queue:
A__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
A__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(UpperCAmelCase_ )
print(max(UpperCAmelCase_ ) )
# Adjacency list of Graph
UpperCamelCase = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 104 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class a ( a__ , unittest.TestCase ):
snake_case__ = DanceDiffusionPipeline
snake_case__ = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
snake_case__ = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
snake_case__ = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
snake_case__ = False
snake_case__ = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=5_12 , sample_rate=1_60_00 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_snake_case , use_timestep_embedding=_snake_case , time_embedding_type='fourier' , mid_block_type='UNetMidBlock1D' , down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') , up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') , )
lowerCAmelCase = IPNDMScheduler()
lowerCAmelCase = {
'unet': unet,
'scheduler': scheduler,
}
return components
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 4,
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = DanceDiffusionPipeline(**_snake_case )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = self.get_dummy_inputs(_snake_case )
lowerCAmelCase = pipe(**_snake_case )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
lowerCAmelCase = np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase__ ( self ):
"""simple docstring"""
return super().test_attention_slicing_forward_pass()
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = torch_device
lowerCAmelCase = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(generator=_snake_case , num_inference_steps=1_00 , audio_length_in_s=4.096 )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase = np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = torch_device
lowerCAmelCase = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' , torch_dtype=torch.floataa )
lowerCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(generator=_snake_case , num_inference_steps=1_00 , audio_length_in_s=4.096 )
lowerCAmelCase = output.audios
lowerCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCAmelCase = np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 4 | 0 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : int = TypeVar('''DatasetType''', Dataset, IterableDataset)
def __UpperCAmelCase ( lowerCamelCase_ : List[DatasetType] , lowerCamelCase_ : Optional[List[float]] = None , lowerCamelCase_ : Optional[int] = None , lowerCamelCase_ : Optional[DatasetInfo] = None , lowerCamelCase_ : Optional[NamedSplit] = None , lowerCamelCase_ : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(lowerCamelCase_ ):
if not isinstance(lowerCamelCase_ , (Dataset, IterableDataset) ):
if isinstance(lowerCamelCase_ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
F'Dataset at position {i} has at least one split: {list(lowerCamelCase_ )}\n'
F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase_ ) )}\']' )
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase_ ).__name__}.' )
if i == 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = (
(Dataset, IterableDataset) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(
F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , info=lowerCamelCase_ , split=lowerCamelCase_ , stopping_strategy=lowerCamelCase_ )
else:
return _interleave_iterable_datasets(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , info=lowerCamelCase_ , split=lowerCamelCase_ , stopping_strategy=lowerCamelCase_ )
def __UpperCAmelCase ( lowerCamelCase_ : List[DatasetType] , lowerCamelCase_ : Optional[DatasetInfo] = None , lowerCamelCase_ : Optional[NamedSplit] = None , lowerCamelCase_ : int = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(lowerCamelCase_ ):
if not isinstance(lowerCamelCase_ , (Dataset, IterableDataset) ):
if isinstance(lowerCamelCase_ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
F'Dataset at position {i} has at least one split: {list(lowerCamelCase_ )}\n'
F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase_ ) )}\']' )
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase_ ).__name__}.' )
if i == 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = (
(Dataset, IterableDataset) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(
F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(lowerCamelCase_ , info=lowerCamelCase_ , split=lowerCamelCase_ , axis=lowerCamelCase_ )
else:
return _concatenate_iterable_datasets(lowerCamelCase_ , info=lowerCamelCase_ , split=lowerCamelCase_ , axis=lowerCamelCase_ )
| 105 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=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 UpperCamelCase__ ( 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 UpperCamelCase__ ( 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=_snake_case , initializer_range=self.initializer_range , use_stable_embedding=_snake_case , )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = OpenLlamaModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , )
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , )
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
# first forward pass
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , use_cache=_snake_case , )
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(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , output_hidden_states=_snake_case , )['hidden_states'][0]
lowerCAmelCase = model(
_snake_case , attention_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , past_key_values=_snake_case , output_hidden_states=_snake_case , )['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(_snake_case , _snake_case , atol=1E-3 ) )
def UpperCamelCase__ ( 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 ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
snake_case__ = (OpenLlamaForCausalLM,) if is_torch_available() else ()
snake_case__ = (
{
'''feature-extraction''': OpenLlamaModel,
'''text-classification''': OpenLlamaForSequenceClassification,
'''text-generation''': OpenLlamaForCausalLM,
'''zero-shot''': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case__ = False
snake_case__ = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenLlamaModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( 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(*_snake_case )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCamelCase__ ( 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(_snake_case )
lowerCAmelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCAmelCase = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
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 UpperCamelCase__ ( self ):
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase ,lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = ids_tensor([1, 10] , config.vocab_size )
lowerCAmelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase = OpenLlamaModel(_snake_case )
original_model.to(_snake_case )
original_model.eval()
lowerCAmelCase = original_model(_snake_case ).last_hidden_state
lowerCAmelCase = original_model(_snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase = {'type': scaling_type, 'factor': 10.0}
lowerCAmelCase = OpenLlamaModel(_snake_case )
scaled_model.to(_snake_case )
scaled_model.eval()
lowerCAmelCase = scaled_model(_snake_case ).last_hidden_state
lowerCAmelCase = scaled_model(_snake_case ).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(_snake_case , _snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_snake_case , _snake_case , atol=1E-5 ) )
| 4 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class lowerCAmelCase__ :
def __init__( self : List[str] , __UpperCamelCase : Dict , __UpperCamelCase : List[str]=13 , __UpperCamelCase : Tuple=7 , __UpperCamelCase : int=False , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : Dict=False , __UpperCamelCase : List[str]=False , __UpperCamelCase : Optional[int]=19 , __UpperCamelCase : Any=32 , __UpperCamelCase : Union[str, Any]=5 , __UpperCamelCase : str=4 , __UpperCamelCase : Tuple=37 , __UpperCamelCase : Optional[int]="gelu" , __UpperCamelCase : str=0.1 , __UpperCamelCase : Any=0.1 , __UpperCamelCase : Tuple=512 , __UpperCamelCase : str=16 , __UpperCamelCase : Union[str, Any]=2 , __UpperCamelCase : List[Any]=0.0_2 , __UpperCamelCase : int=3 , __UpperCamelCase : Optional[Any]=4 , __UpperCamelCase : Optional[int]=None , ) -> Optional[Any]:
A = parent
A = batch_size
A = seq_length
A = is_training
A = use_input_mask
A = use_token_type_ids
A = use_labels
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = type_sequence_label_size
A = initializer_range
A = num_labels
A = num_choices
A = scope
def __UpperCamelCase ( self : Any ) -> str:
A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A = None
if self.use_input_mask:
A = random_attention_mask([self.batch_size, self.seq_length] )
A = None
A = None
A = None
if self.use_labels:
A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A = ids_tensor([self.batch_size] , self.num_choices )
A = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
A = EsmConfig(
vocab_size=33 , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , is_folding_model=__UpperCamelCase , esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False} , )
return config
def __UpperCamelCase ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Tuple ) -> Optional[int]:
A = EsmForProteinFolding(config=__UpperCamelCase ).float()
model.to(__UpperCamelCase )
model.eval()
A = model(__UpperCamelCase , attention_mask=__UpperCamelCase )
A = model(__UpperCamelCase )
A = model(__UpperCamelCase )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def __UpperCamelCase ( self : Any ) -> Dict:
A = self.prepare_config_and_inputs()
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) = config_and_inputs
A = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
A_ : Dict = False
A_ : List[Any] = (EsmForProteinFolding,) if is_torch_available() else ()
A_ : Tuple = ()
A_ : List[Any] = {} if is_torch_available() else {}
A_ : str = False
def __UpperCamelCase ( self : List[Any] ) -> str:
A = EsmFoldModelTester(self )
A = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : List[str] ) -> Tuple:
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
@unittest.skip('Does not support attention outputs' )
def __UpperCamelCase ( self : Optional[int] ) -> Optional[int]:
pass
@unittest.skip
def __UpperCamelCase ( self : Dict ) -> List[str]:
pass
@unittest.skip('Esm does not support embedding resizing' )
def __UpperCamelCase ( self : List[str] ) -> Any:
pass
@unittest.skip('Esm does not support embedding resizing' )
def __UpperCamelCase ( self : List[str] ) -> Optional[int]:
pass
@unittest.skip('ESMFold does not support passing input embeds!' )
def __UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def __UpperCamelCase ( self : Tuple ) -> Tuple:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def __UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
pass
@unittest.skip('ESMFold does not output hidden states in the normal way.' )
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
pass
@unittest.skip('ESMfold does not output hidden states in the normal way.' )
def __UpperCamelCase ( self : Any ) -> Optional[int]:
pass
@unittest.skip('ESMFold only has one output format.' )
def __UpperCamelCase ( self : Optional[int] ) -> Any:
pass
@unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality' )
def __UpperCamelCase ( self : Tuple ) -> Tuple:
pass
@unittest.skip('ESMFold does not support input chunking.' )
def __UpperCamelCase ( self : Any ) -> Dict:
pass
@unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.' )
def __UpperCamelCase ( self : Optional[Any] ) -> Any:
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def __UpperCamelCase ( self : Optional[Any] ) -> Dict:
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def __UpperCamelCase ( self : str ) -> List[Any]:
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def __UpperCamelCase ( self : str ) -> Optional[Any]:
pass
@unittest.skip('ESMFold doesn\'t support data parallel.' )
def __UpperCamelCase ( self : List[Any] ) -> Any:
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __UpperCamelCase ( self : List[Any] ) -> int:
pass
@require_torch
class lowerCAmelCase__ ( _lowerCamelCase ):
@slow
def __UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
A = EsmForProteinFolding.from_pretrained('facebook/esmfold_v1' ).float()
model.eval()
A = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
A = model(__UpperCamelCase )['positions']
A = torch.tensor([2.5_8_2_8, 0.7_9_9_3, -1_0.9_3_3_4] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , __UpperCamelCase , atol=1e-4 ) ) | 106 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = data
lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return F'Node({self.data})'
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = None
def __iter__( self ):
"""simple docstring"""
lowerCAmelCase = self.head
while node:
yield node.data
lowerCAmelCase = node.next
def __len__( self ):
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self ):
"""simple docstring"""
return "->".join([str(_snake_case ) for item in self] )
def __getitem__( self , _snake_case ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , _snake_case , _snake_case ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('list index out of range.' )
lowerCAmelCase = self.head
for _ in range(_snake_case ):
lowerCAmelCase = current.next
lowerCAmelCase = data
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
self.insert_nth(len(self ) , _snake_case )
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
self.insert_nth(0 , _snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
if not 0 <= index <= len(self ):
raise IndexError('list index out of range' )
lowerCAmelCase = Node(_snake_case )
if self.head is None:
lowerCAmelCase = new_node
elif index == 0:
lowerCAmelCase = self.head # link new_node to head
lowerCAmelCase = new_node
else:
lowerCAmelCase = self.head
for _ in range(index - 1 ):
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next
lowerCAmelCase = new_node
def UpperCamelCase__ ( self ): # print every node data
"""simple docstring"""
print(self )
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.delete_nth(0 )
def UpperCamelCase__ ( self ): # delete from tail
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def UpperCamelCase__ ( self , _snake_case = 0 ):
"""simple docstring"""
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError('List index out of range.' )
lowerCAmelCase = self.head # default first node
if index == 0:
lowerCAmelCase = self.head.next
else:
lowerCAmelCase = self.head
for _ in range(index - 1 ):
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next
lowerCAmelCase = temp.next.next
return delete_node.data
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.head is None
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = None
lowerCAmelCase = self.head
while current:
# Store the current node's next node.
lowerCAmelCase = current.next
# Make the current node's next point backwards
lowerCAmelCase = prev
# Make the previous node be the current node
lowerCAmelCase = current
# Make the current node the next node (to progress iteration)
lowerCAmelCase = next_node
# Return prev in order to put the head at the end
lowerCAmelCase = prev
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = LinkedList()
assert linked_list.is_empty() is True
assert str(_UpperCAmelCase ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(_UpperCAmelCase ) == i
linked_list.insert_nth(_UpperCAmelCase , i + 1 )
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(_UpperCAmelCase ) == 9
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
lowerCAmelCase = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(_UpperCAmelCase ) == "->".join(str(_UpperCAmelCase ) for i in range(-8 , 1 ) )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = [
-9,
100,
Node(7734_5112 ),
'dlrow olleH',
7,
5555,
0,
-192.5_5555,
'Hello, world!',
77.9,
Node(10 ),
None,
None,
12.20,
]
lowerCAmelCase = LinkedList()
for i in test_input:
linked_list.insert_tail(_UpperCAmelCase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(_UpperCAmelCase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
lowerCAmelCase = linked_list.delete_head()
assert result == -9
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
lowerCAmelCase = linked_list.delete_tail()
assert result == 12.2
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
lowerCAmelCase = linked_list.delete_nth(10 )
assert result is None
assert (
str(_UpperCAmelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node('Hello again, world!' ) )
assert (
str(_UpperCAmelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(_UpperCAmelCase )
assert (
str(_UpperCAmelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(_UpperCAmelCase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def _SCREAMING_SNAKE_CASE ():
from doctest import testmod
testmod()
lowerCAmelCase = LinkedList()
linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
print('\nPrint list:' )
linked_list.print_list()
print('\nDelete head' )
linked_list.delete_head()
print('Delete tail' )
linked_list.delete_tail()
print('\nPrint list:' )
linked_list.print_list()
print('\nReverse linked list' )
linked_list.reverse()
print('\nPrint list:' )
linked_list.print_list()
print('\nString representation of linked list:' )
print(_UpperCAmelCase )
print('\nReading/changing Node data using indexing:' )
print(F'Element at Position 1: {linked_list[1]}' )
lowerCAmelCase = input('Enter New Value: ' ).strip()
print('New list:' )
print(_UpperCAmelCase )
print(F'length of linked_list is : {len(_UpperCAmelCase )}' )
if __name__ == "__main__":
main()
| 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : str = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : List[str] = [
'''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NezhaForNextSentencePrediction''',
'''NezhaForMaskedLM''',
'''NezhaForPreTraining''',
'''NezhaForMultipleChoice''',
'''NezhaForQuestionAnswering''',
'''NezhaForSequenceClassification''',
'''NezhaForTokenClassification''',
'''NezhaModel''',
'''NezhaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
_UpperCAmelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 107 |
"""simple docstring"""
from __future__ import annotations
import requests
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(_UpperCAmelCase ).json()
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 10 ):
lowerCAmelCase = 'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
lowerCAmelCase = requests.get(_UpperCAmelCase ).json()[:max_stories]
return [get_hackernews_story(_UpperCAmelCase ) for story_id in story_ids]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 10 ):
lowerCAmelCase = hackernews_top_stories(_UpperCAmelCase )
return "\n".join('* [{title}]({url})'.format(**_UpperCAmelCase ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 4 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> Tuple:
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_UpperCAmelCase = flax_key_tuple[:-1] + ("""weight""",)
_UpperCAmelCase = torch.permute(__snake_case , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__snake_case ):
# linear layer
_UpperCAmelCase = flax_key_tuple[:-1] + ("""weight""",)
_UpperCAmelCase = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_UpperCAmelCase = flax_key_tuple[:-1] + ("""weight""",)
return flax_key_tuple, flax_tensor
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> List[str]:
if "metadata" in layer:
_UpperCAmelCase = layer.split("""metadata""" )
_UpperCAmelCase = """""".join(split_layer[0] )[:-1]
_UpperCAmelCase = [tuple(("""metadata""" + split_layer[1]).split("""/""" ) )]
elif "kvstore" in layer:
_UpperCAmelCase = layer.split("""kvstore""" )
_UpperCAmelCase = """""".join(split_layer[0] )[:-1]
_UpperCAmelCase = [tuple(("""kvstore""" + split_layer[1]).split("""/""" ) )]
else:
_UpperCAmelCase = layer.split("""/""" )
_UpperCAmelCase = """/""".join(split_layer[:-1] )
_UpperCAmelCase = (split_layer[-1],)
if "kvstore/path" in layer:
_UpperCAmelCase = f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
elif "kvstore/driver" in layer:
_UpperCAmelCase = """file"""
else:
_UpperCAmelCase = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> Optional[Any]:
_UpperCAmelCase = rename_keys(__snake_case )
_UpperCAmelCase = {}
for k, v in current_block.items():
_UpperCAmelCase = v
_UpperCAmelCase = new_current_block
torch.save(__snake_case , __snake_case )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case = WEIGHTS_NAME ) -> Any:
_UpperCAmelCase = convert_file_size_to_int(__snake_case )
_UpperCAmelCase = []
_UpperCAmelCase = {}
_UpperCAmelCase = 0
_UpperCAmelCase = 0
os.makedirs(__snake_case , exist_ok=__snake_case )
with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""" ) as fp:
_UpperCAmelCase = serialization.msgpack_restore(fp.read() )["""optimizer"""]["""target"""]
_UpperCAmelCase = flatten_dict(__snake_case , sep="""/""" )
_UpperCAmelCase = {}
for layer in checkpoint_info.keys():
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = get_key_and_tensorstore_dict(
__snake_case , __snake_case , __snake_case )
if curr_real_layer_name in all_layers:
_UpperCAmelCase = content
else:
_UpperCAmelCase = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_UpperCAmelCase = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_UpperCAmelCase = torch.tensor(__snake_case )
_UpperCAmelCase = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_UpperCAmelCase , _UpperCAmelCase = rename_base_flax_keys(tuple(key.split("""/""" ) ) , __snake_case )
_UpperCAmelCase = """/""".join(__snake_case )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_UpperCAmelCase = os.path.join(
__snake_case , weights_name.replace(""".bin""" , f"""-{len(__snake_case )+1:05d}-of-???.bin""" ) )
rename_and_save_block(__snake_case , __snake_case )
sharded_state_dicts.append(current_block.keys() )
del current_block
_UpperCAmelCase = {}
_UpperCAmelCase = 0
_UpperCAmelCase = raw_weights.to(getattr(__snake_case , __snake_case ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_UpperCAmelCase = os.path.join(__snake_case , weights_name.replace(""".bin""" , f"""-{len(__snake_case )+1:05d}-of-???.bin""" ) )
rename_and_save_block(__snake_case , __snake_case )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(__snake_case ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_UpperCAmelCase = {}
_UpperCAmelCase = {}
for idx, shard in enumerate(__snake_case ):
_UpperCAmelCase = weights_name.replace(
""".bin""" , f"""-{idx+1:05d}-of-{len(__snake_case ):05d}.bin""" ) # len(sharded_state_dicts):05d}
_UpperCAmelCase = os.path.join(__snake_case , weights_name.replace(""".bin""" , f"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(__snake_case , os.path.join(__snake_case , __snake_case ) )
_UpperCAmelCase = shard
for key in shard:
_UpperCAmelCase = shard_file
# Add the metadata
_UpperCAmelCase = {"""total_size""": total_size}
_UpperCAmelCase = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(__snake_case , __snake_case ) , """w""" , encoding="""utf-8""" ) as f:
_UpperCAmelCase = json.dumps(__snake_case , indent=2 , sort_keys=__snake_case ) + """\n"""
f.write(__snake_case )
return metadata, index
if __name__ == "__main__":
__a: Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--switch_t5x_checkpoint_path''',
default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
type=str,
required=False,
help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
)
parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
type=str,
required=False,
help='''Path to the output pytorch model.''',
)
__a: int = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def _SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_UpperCAmelCase = SwitchTransformersConfig.from_pretrained("""google/switch-base-8""" )
config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""" )
_UpperCAmelCase = SwitchTransformersForConditionalGeneration.from_pretrained(
"""/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""" )
_UpperCAmelCase = TaTokenizer.from_pretrained("""t5-small""" )
_UpperCAmelCase = """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""
_UpperCAmelCase = tokenizer(__snake_case , return_tensors="""pt""" ).input_ids
_UpperCAmelCase = model.generate(__snake_case , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) ) | 108 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any ):
lowerCAmelCase = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCAmelCase = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
lowerCAmelCase = 4
lowerCAmelCase = 48
lowerCAmelCase = 'pixelshuffle_aux'
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCAmelCase = [6, 6, 6, 6]
lowerCAmelCase = 60
lowerCAmelCase = [6, 6, 6, 6]
lowerCAmelCase = 'pixelshuffledirect'
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCAmelCase = 4
lowerCAmelCase = 'nearest+conv'
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
lowerCAmelCase = 1
lowerCAmelCase = 1
lowerCAmelCase = 126
lowerCAmelCase = 7
lowerCAmelCase = 255.0
lowerCAmelCase = ''
return config
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] ):
if "patch_embed.proj" in name and "layers" not in name:
lowerCAmelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
lowerCAmelCase = name.replace('patch_embed.norm' , 'embeddings.patch_embeddings.layernorm' )
if "layers" in name:
lowerCAmelCase = name.replace('layers' , 'encoder.stages' )
if "residual_group.blocks" in name:
lowerCAmelCase = name.replace('residual_group.blocks' , 'layers' )
if "attn.proj" in name:
lowerCAmelCase = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
lowerCAmelCase = name.replace('attn' , 'attention.self' )
if "norm1" in name:
lowerCAmelCase = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowerCAmelCase = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowerCAmelCase = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCAmelCase = name.replace('mlp.fc2' , 'output.dense' )
if "q_bias" in name:
lowerCAmelCase = name.replace('q_bias' , 'query.bias' )
if "k_bias" in name:
lowerCAmelCase = name.replace('k_bias' , 'key.bias' )
if "v_bias" in name:
lowerCAmelCase = name.replace('v_bias' , 'value.bias' )
if "cpb_mlp" in name:
lowerCAmelCase = name.replace('cpb_mlp' , 'continuous_position_bias_mlp' )
if "patch_embed.proj" in name:
lowerCAmelCase = name.replace('patch_embed.proj' , 'patch_embed.projection' )
if name == "norm.weight":
lowerCAmelCase = 'layernorm.weight'
if name == "norm.bias":
lowerCAmelCase = 'layernorm.bias'
if "conv_first" in name:
lowerCAmelCase = name.replace('conv_first' , 'first_convolution' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
lowerCAmelCase = name.replace('conv_last' , 'final_convolution' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
lowerCAmelCase = name.replace('conv_before_upsample.0' , 'conv_before_upsample' )
if "upsample.0" in name:
lowerCAmelCase = name.replace('upsample.0' , 'upsample.convolution_0' )
if "upsample.2" in name:
lowerCAmelCase = name.replace('upsample.2' , 'upsample.convolution_1' )
lowerCAmelCase = 'upsample.' + name
elif config.upsampler == "pixelshuffledirect":
lowerCAmelCase = name.replace('upsample.0.weight' , 'upsample.conv.weight' )
lowerCAmelCase = name.replace('upsample.0.bias' , 'upsample.conv.bias' )
else:
pass
else:
lowerCAmelCase = 'swin2sr.' + name
return name
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict ):
for key in orig_state_dict.copy().keys():
lowerCAmelCase = orig_state_dict.pop(_UpperCAmelCase )
if "qkv" in key:
lowerCAmelCase = key.split('.' )
lowerCAmelCase = int(key_split[1] )
lowerCAmelCase = int(key_split[4] )
lowerCAmelCase = config.embed_dim
if "weight" in key:
lowerCAmelCase = val[:dim, :]
lowerCAmelCase = val[dim : dim * 2, :]
lowerCAmelCase = val[-dim:, :]
else:
lowerCAmelCase = val[:dim]
lowerCAmelCase = val[dim : dim * 2]
lowerCAmelCase = val[-dim:]
pass
else:
lowerCAmelCase = val
return orig_state_dict
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple ):
lowerCAmelCase = get_config(_UpperCAmelCase )
lowerCAmelCase = SwinaSRForImageSuperResolution(_UpperCAmelCase )
model.eval()
lowerCAmelCase = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location='cpu' )
lowerCAmelCase = convert_state_dict(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase ,lowerCAmelCase = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
raise ValueError('Missing keys when converting: {}'.format(_UpperCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F'Unexpected key {key} in state_dict' )
# verify values
lowerCAmelCase = 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
lowerCAmelCase = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('RGB' )
lowerCAmelCase = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
lowerCAmelCase = 126 if 'Jpeg' in checkpoint_url else 256
lowerCAmelCase = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
lowerCAmelCase = transforms(_UpperCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
lowerCAmelCase = pixel_values[:, 0, :, :].unsqueeze(1 )
lowerCAmelCase = model(_UpperCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 512, 512] )
lowerCAmelCase = torch.tensor(
[[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 512, 512] )
lowerCAmelCase = torch.tensor(
[[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowerCAmelCase = torch.Size([1, 3, 1024, 1024] )
lowerCAmelCase = torch.tensor(
[[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] )
assert (
outputs.reconstruction.shape == expected_shape
), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , _UpperCAmelCase , atol=1e-3 )
print('Looks ok!' )
lowerCAmelCase = {
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
'swin2SR-classical-sr-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
'swin2SR-classical-sr-x4-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
'swin2SR-compressed-sr-x4-48'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
'swin2SR-lightweight-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
),
}
lowerCAmelCase = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_UpperCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
model.push_to_hub(F'caidas/{model_name}' )
processor.push_to_hub(F'caidas/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
type=str,
help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 4 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class __a ( _snake_case ):
__UpperCamelCase : Optional[int] = 'fnet'
def __init__( self : Dict ,lowerCamelCase : List[Any]=3_2000 ,lowerCamelCase : Dict=768 ,lowerCamelCase : Dict=12 ,lowerCamelCase : Tuple=3072 ,lowerCamelCase : Union[str, Any]="gelu_new" ,lowerCamelCase : List[str]=0.1 ,lowerCamelCase : Union[str, Any]=512 ,lowerCamelCase : List[Any]=4 ,lowerCamelCase : Dict=0.02 ,lowerCamelCase : Optional[Any]=1E-1_2 ,lowerCamelCase : Optional[Any]=False ,lowerCamelCase : Dict=512 ,lowerCamelCase : List[Any]=3 ,lowerCamelCase : Tuple=1 ,lowerCamelCase : Dict=2 ,**lowerCamelCase : List[str] ,):
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase ,bos_token_id=lowerCamelCase ,eos_token_id=lowerCamelCase ,**lowerCamelCase )
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = type_vocab_size
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = use_tpu_fourier_optimizations
__SCREAMING_SNAKE_CASE = tpu_short_seq_length
| 109 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a ( a__ ):
snake_case__ = '''megatron-bert'''
def __init__( self , _snake_case=2_90_56 , _snake_case=10_24 , _snake_case=24 , _snake_case=16 , _snake_case=40_96 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=True , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , **_snake_case )
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 = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = use_cache
| 4 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a ( lowercase , lowercase , lowercase ):
@register_to_config
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = False , ):
super().__init__()
UpperCAmelCase__ : Optional[int] = nn.Embedding(UpperCamelCase_ , UpperCamelCase_ )
UpperCAmelCase__ : Optional[Any] = nn.Embedding(UpperCamelCase_ , UpperCamelCase_ )
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = nn.Dropout(p=UpperCamelCase_ )
UpperCAmelCase__ : List[str] = TaConfig(
vocab_size=UpperCamelCase_ , d_model=UpperCamelCase_ , num_heads=UpperCamelCase_ , d_kv=UpperCamelCase_ , d_ff=UpperCamelCase_ , dropout_rate=UpperCamelCase_ , feed_forward_proj=UpperCamelCase_ , is_decoder=UpperCamelCase_ , is_encoder_decoder=UpperCamelCase_ , )
UpperCAmelCase__ : Any = nn.ModuleList()
for lyr_num in range(UpperCamelCase_ ):
UpperCAmelCase__ : Tuple = TaBlock(UpperCamelCase_ )
self.encoders.append(UpperCamelCase_ )
UpperCAmelCase__ : Any = TaLayerNorm(UpperCamelCase_ )
UpperCAmelCase__ : int = nn.Dropout(p=UpperCamelCase_ )
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ ):
UpperCAmelCase__ : Dict = self.token_embedder(UpperCamelCase_ )
UpperCAmelCase__ : Dict = encoder_input_tokens.shape[1]
UpperCAmelCase__ : List[Any] = torch.arange(UpperCamelCase_ , device=encoder_input_tokens.device )
x += self.position_encoding(UpperCamelCase_ )
UpperCAmelCase__ : Optional[int] = self.dropout_pre(UpperCamelCase_ )
# inverted the attention mask
UpperCAmelCase__ : int = encoder_input_tokens.size()
UpperCAmelCase__ : Tuple = self.get_extended_attention_mask(UpperCamelCase_ , UpperCamelCase_ )
for lyr in self.encoders:
UpperCAmelCase__ : Any = lyr(UpperCamelCase_ , UpperCamelCase_ )[0]
UpperCAmelCase__ : List[str] = self.layer_norm(UpperCamelCase_ )
return self.dropout_post(UpperCamelCase_ ), encoder_inputs_mask
| 110 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase ( a__ , a__ , a__ ):
lowercase : Optional[int] = [R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 5_0257 , SCREAMING_SNAKE_CASE_ = 1024 , SCREAMING_SNAKE_CASE_ = 768 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "gelu_new" , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 1e-5 , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = False , ):
super().__init__()
UpperCamelCase : List[Any] = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
f' `n_embd`: {n_embd} are not equal.' )
UpperCamelCase : int = prefix_inner_dim
UpperCamelCase : Dict = prefix_hidden_dim
UpperCamelCase : Union[str, Any] = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
UpperCamelCase : Tuple = (
nn.Linear(self.prefix_hidden_dim , _snake_case ) if self.prefix_hidden_dim is not None else nn.Identity()
)
UpperCamelCase : Any = GPTaConfig(
vocab_size=_snake_case , n_positions=_snake_case , n_embd=_snake_case , n_layer=_snake_case , n_head=_snake_case , n_inner=_snake_case , activation_function=_snake_case , resid_pdrop=_snake_case , embd_pdrop=_snake_case , attn_pdrop=_snake_case , layer_norm_epsilon=_snake_case , initializer_range=_snake_case , scale_attn_weights=_snake_case , use_cache=_snake_case , scale_attn_by_inverse_layer_idx=_snake_case , reorder_and_upcast_attn=_snake_case , )
UpperCamelCase : str = GPTaLMHeadModel(_snake_case )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , ):
UpperCamelCase : Optional[int] = self.transformer.transformer.wte(_snake_case )
UpperCamelCase : List[str] = self.encode_prefix(_snake_case )
UpperCamelCase : Optional[Any] = self.decode_prefix(_snake_case )
UpperCamelCase : str = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
UpperCamelCase : str = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
UpperCamelCase : List[str] = torch.cat((dummy_token, input_ids) , dim=1 )
UpperCamelCase : str = self.transformer(inputs_embeds=_snake_case , labels=_snake_case , attention_mask=_snake_case )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return torch.zeros(_snake_case , self.prefix_length , dtype=torch.intaa , device=_snake_case )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.encode_prefix(_snake_case )
@torch.no_grad()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = torch.split(_snake_case , 1 , dim=0 )
UpperCamelCase : List[Any] = []
UpperCamelCase : Tuple = []
for feature in features:
UpperCamelCase : Optional[int] = self.decode_prefix(feature.to(_snake_case ) ) # back to the clip feature
# Only support beam search for now
UpperCamelCase , UpperCamelCase : str = self.generate_beam(
input_embeds=_snake_case , device=_snake_case , eos_token_id=_snake_case )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
UpperCamelCase : Dict = torch.stack(_snake_case )
UpperCamelCase : Dict = torch.stack(_snake_case )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = 5 , SCREAMING_SNAKE_CASE_ = 67 , SCREAMING_SNAKE_CASE_ = 1.0 , SCREAMING_SNAKE_CASE_ = None , ):
UpperCamelCase : Tuple = eos_token_id
UpperCamelCase : int = None
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Union[str, Any] = torch.ones(_snake_case , device=_snake_case , dtype=torch.int )
UpperCamelCase : Optional[Any] = torch.zeros(_snake_case , device=_snake_case , dtype=torch.bool )
if input_embeds is not None:
UpperCamelCase : Tuple = input_embeds
else:
UpperCamelCase : Union[str, Any] = self.transformer.transformer.wte(_snake_case )
for i in range(_snake_case ):
UpperCamelCase : Dict = self.transformer(inputs_embeds=_snake_case )
UpperCamelCase : Optional[int] = outputs.logits
UpperCamelCase : Optional[int] = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
UpperCamelCase : List[Any] = logits.softmax(-1 ).log()
if scores is None:
UpperCamelCase , UpperCamelCase : Optional[Any] = logits.topk(_snake_case , -1 )
UpperCamelCase : Dict = generated.expand(_snake_case , *generated.shape[1:] )
UpperCamelCase , UpperCamelCase : int = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
UpperCamelCase : Optional[int] = next_tokens
else:
UpperCamelCase : Any = tokens.expand(_snake_case , *tokens.shape[1:] )
UpperCamelCase : List[str] = torch.cat((tokens, next_tokens) , dim=1 )
else:
UpperCamelCase : Union[str, Any] = -float(np.inf )
UpperCamelCase : List[Any] = 0
UpperCamelCase : Union[str, Any] = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
UpperCamelCase : Tuple = scores_sum / seq_lengths[:, None]
UpperCamelCase , UpperCamelCase : str = scores_sum_average.view(-1 ).topk(_snake_case , -1 )
UpperCamelCase : int = next_tokens // scores_sum.shape[1]
UpperCamelCase : List[Any] = seq_lengths[next_tokens_source]
UpperCamelCase : int = next_tokens % scores_sum.shape[1]
UpperCamelCase : Optional[int] = next_tokens.unsqueeze(1 )
UpperCamelCase : Any = tokens[next_tokens_source]
UpperCamelCase : int = torch.cat((tokens, next_tokens) , dim=1 )
UpperCamelCase : List[Any] = generated[next_tokens_source]
UpperCamelCase : List[Any] = scores_sum_average * seq_lengths
UpperCamelCase : int = is_stopped[next_tokens_source]
UpperCamelCase : Tuple = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
UpperCamelCase : Optional[int] = torch.cat((generated, next_token_embed) , dim=1 )
UpperCamelCase : List[Any] = is_stopped + next_tokens.eq(_snake_case ).squeeze()
if is_stopped.all():
break
UpperCamelCase : Dict = scores / seq_lengths
UpperCamelCase : str = scores.argsort(descending=_snake_case )
# tokens tensors are already padded to max_seq_length
UpperCamelCase : Optional[int] = [tokens[i] for i in order]
UpperCamelCase : int = torch.stack(_snake_case , dim=0 )
UpperCamelCase : int = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 499 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class a ( a__ ):
snake_case__ = 42
class a ( a__ , a__ ):
@register_to_config
def __init__( self , _snake_case = 3 , _snake_case = 3 , _snake_case = ("DownEncoderBlock2D",) , _snake_case = ("UpDecoderBlock2D",) , _snake_case = (64,) , _snake_case = 1 , _snake_case = "silu" , _snake_case = 3 , _snake_case = 32 , _snake_case = 2_56 , _snake_case = 32 , _snake_case = None , _snake_case = 0.18_215 , _snake_case = "group" , ):
"""simple docstring"""
super().__init__()
# pass init params to Encoder
lowerCAmelCase = Encoder(
in_channels=_snake_case , out_channels=_snake_case , down_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , double_z=_snake_case , )
lowerCAmelCase = vq_embed_dim if vq_embed_dim is not None else latent_channels
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
lowerCAmelCase = VectorQuantizer(_snake_case , _snake_case , beta=0.25 , remap=_snake_case , sane_index_shape=_snake_case )
lowerCAmelCase = nn.Convad(_snake_case , _snake_case , 1 )
# pass init params to Decoder
lowerCAmelCase = Decoder(
in_channels=_snake_case , out_channels=_snake_case , up_block_types=_snake_case , block_out_channels=_snake_case , layers_per_block=_snake_case , act_fn=_snake_case , norm_num_groups=_snake_case , norm_type=_snake_case , )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = self.encoder(_snake_case )
lowerCAmelCase = self.quant_conv(_snake_case )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_snake_case )
@apply_forward_hook
def UpperCamelCase__ ( self , _snake_case , _snake_case = False , _snake_case = True ):
"""simple docstring"""
if not force_not_quantize:
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = self.quantize(_snake_case )
else:
lowerCAmelCase = h
lowerCAmelCase = self.post_quant_conv(_snake_case )
lowerCAmelCase = self.decoder(_snake_case , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case = True ):
"""simple docstring"""
lowerCAmelCase = sample
lowerCAmelCase = self.encode(_snake_case ).latents
lowerCAmelCase = self.decode(_snake_case ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_snake_case )
| 4 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : str , lowerCAmelCase : List[Any] , lowerCAmelCase : str=13 , lowerCAmelCase : List[str]=7 , lowerCAmelCase : List[Any]=True , lowerCAmelCase : int=True , lowerCAmelCase : str=True , lowerCAmelCase : List[Any]=True , lowerCAmelCase : Union[str, Any]=99 , lowerCAmelCase : str=32 , lowerCAmelCase : int=5 , lowerCAmelCase : List[str]=4 , lowerCAmelCase : Any=37 , lowerCAmelCase : Optional[Any]="gelu" , lowerCAmelCase : int=0.1 , lowerCAmelCase : Optional[int]=0.1 , lowerCAmelCase : List[str]=5_12 , lowerCAmelCase : str=16 , lowerCAmelCase : Union[str, Any]=2 , lowerCAmelCase : List[Any]=0.02 , lowerCAmelCase : Tuple=4 , ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = parent
__lowerCAmelCase : int = batch_size
__lowerCAmelCase : Optional[int] = seq_length
__lowerCAmelCase : List[str] = is_training
__lowerCAmelCase : Optional[int] = use_attention_mask
__lowerCAmelCase : Optional[Any] = use_token_type_ids
__lowerCAmelCase : Dict = use_labels
__lowerCAmelCase : List[Any] = vocab_size
__lowerCAmelCase : Any = hidden_size
__lowerCAmelCase : Union[str, Any] = num_hidden_layers
__lowerCAmelCase : int = num_attention_heads
__lowerCAmelCase : List[str] = intermediate_size
__lowerCAmelCase : List[str] = hidden_act
__lowerCAmelCase : Optional[Any] = hidden_dropout_prob
__lowerCAmelCase : List[str] = attention_probs_dropout_prob
__lowerCAmelCase : str = max_position_embeddings
__lowerCAmelCase : Dict = type_vocab_size
__lowerCAmelCase : Optional[int] = type_sequence_label_size
__lowerCAmelCase : Union[str, Any] = initializer_range
__lowerCAmelCase : Any = num_choices
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
"""simple docstring"""
__lowerCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase : Any = None
if self.use_attention_mask:
__lowerCAmelCase : int = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase : Union[str, Any] = None
if self.use_token_type_ids:
__lowerCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase : Union[str, Any] = BertConfig(
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=_snake_case , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def SCREAMING_SNAKE_CASE ( self : str ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Any = self.prepare_config_and_inputs()
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase : Any = config_and_inputs
__lowerCAmelCase : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = self.prepare_config_and_inputs()
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase : Optional[Any] = config_and_inputs
__lowerCAmelCase : List[Any] = True
__lowerCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class SCREAMING_SNAKE_CASE ( a__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[int] =True
lowerCamelCase : int =(
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = FlaxBertModelTester(self )
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
"""simple docstring"""
__lowerCAmelCase : int = FlaxBertModel.from_pretrained("""bert-base-cased""" )
__lowerCAmelCase : Optional[int] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_snake_case )
| 651 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = vertices
lowerCAmelCase = {
(min(_snake_case ), max(_snake_case )): weight for edge, weight in edges.items()
}
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
lowerCAmelCase = weight
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Graph({min(self.vertices )} , {} )
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
lowerCAmelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
lowerCAmelCase = edge
lowerCAmelCase = weight
subgraph.add_edge(_snake_case , _snake_case )
return subgraph
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "p107_network.txt" ):
lowerCAmelCase = os.path.abspath(os.path.dirname(_UpperCAmelCase ) )
lowerCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = {}
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
with open(_UpperCAmelCase ) as f:
lowerCAmelCase = f.read().strip().split('\n' )
lowerCAmelCase = [line.split(',' ) for line in data]
for edgea in range(1 , len(_UpperCAmelCase ) ):
for edgea in range(_UpperCAmelCase ):
if adjaceny_matrix[edgea][edgea] != "-":
lowerCAmelCase = int(adjaceny_matrix[edgea][edgea] )
lowerCAmelCase = Graph(set(range(len(_UpperCAmelCase ) ) ) , _UpperCAmelCase )
lowerCAmelCase = graph.prims_algorithm()
lowerCAmelCase = sum(graph.edges.values() )
lowerCAmelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 4 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 412 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase )] )
lowerCAmelCase = np.array(_UpperCAmelCase )
lowerCAmelCase = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , _UpperCAmelCase ) ) , x.transpose() ) , _UpperCAmelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = (1, 2, 1)
lowerCAmelCase = (1, 1, 0, 7)
lowerCAmelCase = SARIMAX(
_UpperCAmelCase , exog=_UpperCAmelCase , order=_UpperCAmelCase , seasonal_order=_UpperCAmelCase )
lowerCAmelCase = model.fit(disp=_UpperCAmelCase , maxiter=600 , method='nm' )
lowerCAmelCase = model_fit.predict(1 , len(_UpperCAmelCase ) , exog=[test_match] )
return result[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list ):
lowerCAmelCase = SVR(kernel='rbf' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = regressor.predict(_UpperCAmelCase )
return y_pred[0]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list ):
train_user.sort()
lowerCAmelCase = np.percentile(_UpperCAmelCase , 25 )
lowerCAmelCase = np.percentile(_UpperCAmelCase , 75 )
lowerCAmelCase = qa - qa
lowerCAmelCase = qa - (iqr * 0.1)
return low_lim
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : float ):
lowerCAmelCase = 0
lowerCAmelCase = 0
for i in list_vote:
if i > actual_result:
lowerCAmelCase = not_safe + 1
else:
if abs(abs(_UpperCAmelCase ) - abs(_UpperCAmelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
__UpperCamelCase : Optional[Any] = [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
__UpperCamelCase : Any = pd.DataFrame(
data_input, columns=['''total_user''', '''total_even''', '''days''']
)
__UpperCamelCase : Dict = Normalizer().fit_transform(data_input_df.values)
# split data
__UpperCamelCase : Dict = normalize_df[:, 2].tolist()
__UpperCamelCase : Union[str, Any] = normalize_df[:, 0].tolist()
__UpperCamelCase : List[str] = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
__UpperCamelCase : Optional[int] = normalize_df[:, [1, 2]].tolist()
__UpperCamelCase : Tuple = x[: len(x) - 1]
__UpperCamelCase : Any = x[len(x) - 1 :]
# for linear regression & sarimax
__UpperCamelCase : str = total_date[: len(total_date) - 1]
__UpperCamelCase : Union[str, Any] = total_user[: len(total_user) - 1]
__UpperCamelCase : List[Any] = total_match[: len(total_match) - 1]
__UpperCamelCase : Optional[Any] = total_date[len(total_date) - 1 :]
__UpperCamelCase : str = total_user[len(total_user) - 1 :]
__UpperCamelCase : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
__UpperCamelCase : Any = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
__UpperCamelCase : List[str] = '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
print('''Today\'s data is {not_str}safe.''')
| 4 | 0 |
from __future__ import annotations
class a :
"""simple docstring"""
def __init__( self : Dict , __lowercase : int , __lowercase : Tuple ) -> Union[str, Any]:
__UpperCAmelCase , __UpperCAmelCase : int = text, pattern
__UpperCAmelCase , __UpperCAmelCase : str = len(_snake_case ), len(_snake_case )
def UpperCAmelCase ( self : str , __lowercase : Any ) -> str:
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def UpperCAmelCase ( self : Union[str, Any] , __lowercase : Union[str, Any] ) -> Optional[int]:
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def UpperCAmelCase ( self : int ) -> Any:
__UpperCAmelCase : List[Any] = []
for i in range(self.textLen - self.patLen + 1 ):
__UpperCAmelCase : Union[str, Any] = self.mismatch_in_text(_snake_case )
if mismatch_index == -1:
positions.append(_snake_case )
else:
__UpperCAmelCase : List[str] = self.match_in_pattern(self.text[mismatch_index] )
__UpperCAmelCase : Any = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
a : int = '''ABAABA'''
a : List[str] = '''AB'''
a : Any = BoyerMooreSearch(text, pattern)
a : List[Any] = bms.bad_character_heuristic()
if len(positions) == 0:
print("No match found")
else:
print("Pattern found in following positions: ")
print(positions)
| 63 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'-m' , '--pretrained_model_name_or_path' , type=_UpperCAmelCase , default=_UpperCAmelCase , required=_UpperCAmelCase , help='Path to pretrained model or model identifier from huggingface.co/models.' , )
parser.add_argument(
'-c' , '--caption' , type=_UpperCAmelCase , default='robotic cat with wings' , help='Text used to generate images.' , )
parser.add_argument(
'-n' , '--images_num' , type=_UpperCAmelCase , default=4 , help='How much images to generate.' , )
parser.add_argument(
'-s' , '--seed' , type=_UpperCAmelCase , default=42 , help='Seed for random process.' , )
parser.add_argument(
'-ci' , '--cuda_id' , type=_UpperCAmelCase , default=0 , help='cuda_id.' , )
lowerCAmelCase = parser.parse_args()
return args
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] ):
if not len(_UpperCAmelCase ) == rows * cols:
raise ValueError('The specified number of rows and columns are not correct.' )
lowerCAmelCase ,lowerCAmelCase = imgs[0].size
lowerCAmelCase = Image.new('RGB' , size=(cols * w, rows * h) )
lowerCAmelCase ,lowerCAmelCase = grid.size
for i, img in enumerate(_UpperCAmelCase ):
grid.paste(_UpperCAmelCase , box=(i % cols * w, i // cols * h) )
return grid
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any]="robotic cat with wings" , _UpperCAmelCase : Optional[int]=7.5 , _UpperCAmelCase : Dict=50 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : int=42 , ):
lowerCAmelCase = torch.Generator(pipeline.device ).manual_seed(_UpperCAmelCase )
lowerCAmelCase = pipeline(
_UpperCAmelCase , guidance_scale=_UpperCAmelCase , num_inference_steps=_UpperCAmelCase , generator=_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase , ).images
lowerCAmelCase = int(math.sqrt(_UpperCAmelCase ) )
lowerCAmelCase = image_grid(_UpperCAmelCase , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__UpperCamelCase : Optional[Any] = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase : List[Any] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__UpperCamelCase : str = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__UpperCamelCase : Optional[int] = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__UpperCamelCase : List[str] = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')
__UpperCamelCase : Tuple = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase : Union[str, Any] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
__UpperCamelCase : Dict = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, '''unet''', unet)
else:
__UpperCamelCase : Dict = unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase : Optional[Any] = pipeline.to(unet.device)
__UpperCamelCase ,__UpperCamelCase : List[Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
__UpperCamelCase : int = os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))
| 4 | 0 |
import os
import sys
import unittest
lowercase_ = 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,
)
lowercase_ = os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
lowercase_ = os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_test_to_tester_mapping(_snake_case )
__SCREAMING_SNAKE_CASE : List[Any] = get_test_to_tester_mapping(_snake_case )
__SCREAMING_SNAKE_CASE : List[Any] = {'''BertModelTest''': '''BertModelTester'''}
__SCREAMING_SNAKE_CASE : List[Any] = {
'''BlipModelTest''': '''BlipModelTester''',
'''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
'''BlipTextModelTest''': '''BlipTextModelTester''',
'''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
'''BlipVQAModelTest''': '''BlipVQAModelTester''',
'''BlipVisionModelTest''': '''BlipVisionModelTester''',
}
self.assertEqual(get_test_info.to_json(_snake_case ) , _snake_case )
self.assertEqual(get_test_info.to_json(_snake_case ) , _snake_case )
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = get_model_to_test_mapping(_snake_case )
__SCREAMING_SNAKE_CASE : Tuple = get_model_to_test_mapping(_snake_case )
__SCREAMING_SNAKE_CASE : List[Any] = {
'''BertForMaskedLM''': ['''BertModelTest'''],
'''BertForMultipleChoice''': ['''BertModelTest'''],
'''BertForNextSentencePrediction''': ['''BertModelTest'''],
'''BertForPreTraining''': ['''BertModelTest'''],
'''BertForQuestionAnswering''': ['''BertModelTest'''],
'''BertForSequenceClassification''': ['''BertModelTest'''],
'''BertForTokenClassification''': ['''BertModelTest'''],
'''BertLMHeadModel''': ['''BertModelTest'''],
'''BertModel''': ['''BertModelTest'''],
}
__SCREAMING_SNAKE_CASE : Tuple = {
'''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
'''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
'''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
'''BlipModel''': ['''BlipModelTest'''],
'''BlipTextModel''': ['''BlipTextModelTest'''],
'''BlipVisionModel''': ['''BlipVisionModelTest'''],
}
self.assertEqual(get_test_info.to_json(_snake_case ) , _snake_case )
self.assertEqual(get_test_info.to_json(_snake_case ) , _snake_case )
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = get_model_to_tester_mapping(_snake_case )
__SCREAMING_SNAKE_CASE : int = get_model_to_tester_mapping(_snake_case )
__SCREAMING_SNAKE_CASE : Tuple = {
'''BertForMaskedLM''': ['''BertModelTester'''],
'''BertForMultipleChoice''': ['''BertModelTester'''],
'''BertForNextSentencePrediction''': ['''BertModelTester'''],
'''BertForPreTraining''': ['''BertModelTester'''],
'''BertForQuestionAnswering''': ['''BertModelTester'''],
'''BertForSequenceClassification''': ['''BertModelTester'''],
'''BertForTokenClassification''': ['''BertModelTester'''],
'''BertLMHeadModel''': ['''BertModelTester'''],
'''BertModel''': ['''BertModelTester'''],
}
__SCREAMING_SNAKE_CASE : List[Any] = {
'''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
'''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
'''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
'''BlipModel''': ['''BlipModelTester'''],
'''BlipTextModel''': ['''BlipTextModelTester'''],
'''BlipVisionModel''': ['''BlipVisionModelTester'''],
}
self.assertEqual(get_test_info.to_json(_snake_case ) , _snake_case )
self.assertEqual(get_test_info.to_json(_snake_case ) , _snake_case )
| 74 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : nn.ModuleList , _UpperCAmelCase : List[int] ):
lowerCAmelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(_UpperCAmelCase ) == len(_UpperCAmelCase ), F'{len(_UpperCAmelCase )} != {len(_UpperCAmelCase )}'
dest_layers.load_state_dict(layers_to_copy.state_dict() )
__UpperCamelCase : Optional[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCamelCase : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] ):
try:
lowerCAmelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
F' {n_student}' )
return list(range(_UpperCAmelCase ) )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ):
if n_student > n_teacher:
raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}' )
elif n_teacher == n_student:
return list(range(_UpperCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, PreTrainedModel] , _UpperCAmelCase : Union[str, Path] = "student" , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : Union[int, None] = None , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Optional[Any]=None , **_UpperCAmelCase : str , ):
lowerCAmelCase = 'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
assert (e is not None) or (d is not None), _msg
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
AutoTokenizer.from_pretrained(_UpperCAmelCase ).save_pretrained(_UpperCAmelCase ) # purely for convenience
lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).eval()
else:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ), F'teacher must be a model or string got type {type(_UpperCAmelCase )}'
lowerCAmelCase = teacher.config.to_diff_dict()
try:
lowerCAmelCase ,lowerCAmelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
init_kwargs.update({'encoder_layers': e, 'decoder_layers': d} )
except AttributeError: # T5
if hasattr(teacher.config , 'num_encoder_layers' ):
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
lowerCAmelCase ,lowerCAmelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
lowerCAmelCase = teacher_e
if d is None:
lowerCAmelCase = teacher_d
if hasattr(teacher.config , 'num_encoder_layers' ):
init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d} )
else:
init_kwargs.update({'num_layers': e, 'num_decoder_layers': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(_UpperCAmelCase )
# Copy weights
lowerCAmelCase = teacher.config_class(**_UpperCAmelCase )
lowerCAmelCase = AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
lowerCAmelCase = student.load_state_dict(teacher.state_dict() , strict=_UpperCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
lowerCAmelCase ,lowerCAmelCase = list(range(_UpperCAmelCase ) ), list(range(_UpperCAmelCase ) )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
F' {save_path}' )
student.save_pretrained(_UpperCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
if d_layers_to_copy is None:
lowerCAmelCase = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
try:
if hasattr(
_UpperCAmelCase , 'prophetnet' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _UpperCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _UpperCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _UpperCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , _UpperCAmelCase )
logger.info(
F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' )
lowerCAmelCase = {
'teacher_type': teacher.config.model_type,
'copied_encoder_layers': e_layers_to_copy,
'copied_decoder_layers': d_layers_to_copy,
}
student.save_pretrained(_UpperCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 4 | 0 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> Optional[Any]:
__A : Optional[int] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(_UpperCAmelCase ) == len(_UpperCAmelCase ), F"""{len(_UpperCAmelCase )} != {len(_UpperCAmelCase )}"""
dest_layers.load_state_dict(layers_to_copy.state_dict() )
UpperCAmelCase : Optional[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
UpperCAmelCase : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def _SCREAMING_SNAKE_CASE ( a , a ) -> Any:
try:
__A : str = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
F""" {n_student}""" )
return list(range(_UpperCAmelCase ) )
def _SCREAMING_SNAKE_CASE ( a , a ) -> List[str]:
if n_student > n_teacher:
raise ValueError(F"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}""" )
elif n_teacher == n_student:
return list(range(_UpperCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _SCREAMING_SNAKE_CASE ( a , a = "student" , a = None , a = None , a=False , a=None , a=None , **a , ) -> Union[str, Any]:
__A : int = 'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
assert (e is not None) or (d is not None), _msg
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
AutoTokenizer.from_pretrained(_UpperCAmelCase ).save_pretrained(_UpperCAmelCase ) # purely for convenience
__A : Dict = AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).eval()
else:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ), F"""teacher must be a model or string got type {type(_UpperCAmelCase )}"""
__A : List[str] = teacher.config.to_diff_dict()
try:
__A , __A : str = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
__A : str = teacher_e
if d is None:
__A : List[Any] = teacher_d
init_kwargs.update({'encoder_layers': e, 'decoder_layers': d} )
except AttributeError: # T5
if hasattr(teacher.config , 'num_encoder_layers' ):
__A , __A : List[str] = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
__A , __A : Union[str, Any] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
__A : Optional[int] = teacher_e
if d is None:
__A : Tuple = teacher_d
if hasattr(teacher.config , 'num_encoder_layers' ):
init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d} )
else:
init_kwargs.update({'num_layers': e, 'num_decoder_layers': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(_UpperCAmelCase )
# Copy weights
__A : Dict = teacher.config_class(**_UpperCAmelCase )
__A : List[str] = AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
__A : List[Any] = student.load_state_dict(teacher.state_dict() , strict=_UpperCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
__A , __A : Any = list(range(_UpperCAmelCase ) ), list(range(_UpperCAmelCase ) )
logger.info(
F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
F""" {save_path}""" )
student.save_pretrained(_UpperCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
__A : Union[str, Any] = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
if d_layers_to_copy is None:
__A : Dict = pick_layers_to_copy(_UpperCAmelCase , _UpperCAmelCase )
try:
if hasattr(
_UpperCAmelCase , 'prophetnet' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _UpperCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _UpperCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _UpperCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _UpperCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , _UpperCAmelCase )
logger.info(
F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}""" )
__A : Dict = {
'teacher_type': teacher.config.model_type,
'copied_encoder_layers': e_layers_to_copy,
'copied_decoder_layers': d_layers_to_copy,
}
student.save_pretrained(_UpperCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 239 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Dict = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = ['''LayoutXLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = ['''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
__UpperCamelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 4 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
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 PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
a = logging.get_logger(__name__)
@add_end_docstrings(a__ )
class __a ( a__ ):
def __init__( self : List[str] ,*lowerCamelCase : Optional[Any] ,**lowerCamelCase : Any ):
'''simple docstring'''
super().__init__(*_snake_case ,**_snake_case )
requires_backends(self ,"""vision""" )
self.check_model_type(_snake_case )
def __call__( self : str ,lowerCamelCase : int ,**lowerCamelCase : int ):
'''simple docstring'''
return super().__call__(_snake_case ,**_snake_case )
def UpperCAmelCase__ ( self : List[Any] ,**lowerCamelCase : List[str] ):
'''simple docstring'''
return {}, {}, {}
def UpperCAmelCase__ ( self : int ,lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = load_image(_snake_case )
__SCREAMING_SNAKE_CASE = image.size
__SCREAMING_SNAKE_CASE = self.image_processor(images=_snake_case ,return_tensors=self.framework )
return model_inputs
def UpperCAmelCase__ ( self : Dict ,lowerCamelCase : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.model(**_snake_case )
return model_outputs
def UpperCAmelCase__ ( self : Optional[int] ,lowerCamelCase : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = model_outputs.predicted_depth
__SCREAMING_SNAKE_CASE = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) ,size=self.image_size[::-1] ,mode="""bicubic""" ,align_corners=_snake_case )
__SCREAMING_SNAKE_CASE = prediction.squeeze().cpu().numpy()
__SCREAMING_SNAKE_CASE = (output * 255 / np.max(_snake_case )).astype("""uint8""" )
__SCREAMING_SNAKE_CASE = Image.fromarray(_snake_case )
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = predicted_depth
__SCREAMING_SNAKE_CASE = depth
return output_dict
| 109 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase = F'Resistor at index {index} has a negative or zero value!'
raise ValueError(_UpperCAmelCase )
first_sum += 1 / float(_UpperCAmelCase )
index += 1
return 1 / first_sum
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowerCAmelCase = F'Resistor at index {index} has a negative value!'
raise ValueError(_UpperCAmelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __lowercase( ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase = 9, 14 # noqa: F841
lowerCamelCase = [
[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],
]
lowerCamelCase = defaultdict(_UpperCAmelCase )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
lowerCamelCase = mst(_UpperCAmelCase )
lowerCamelCase = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
lowerCamelCase = tuple(answer[:2] )
lowerCamelCase = tuple(edge[::-1] )
assert edge in result or reverse in result | 623 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class a ( a__ ):
snake_case__ = '''glpn'''
def __init__( self , _snake_case=3 , _snake_case=4 , _snake_case=[2, 2, 2, 2] , _snake_case=[8, 4, 2, 1] , _snake_case=[32, 64, 1_60, 2_56] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[1, 2, 5, 8] , _snake_case=[4, 4, 4, 4] , _snake_case="gelu" , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=0.1 , _snake_case=1E-6 , _snake_case=64 , _snake_case=10 , _snake_case=-1 , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
lowerCAmelCase = num_channels
lowerCAmelCase = num_encoder_blocks
lowerCAmelCase = depths
lowerCAmelCase = sr_ratios
lowerCAmelCase = hidden_sizes
lowerCAmelCase = patch_sizes
lowerCAmelCase = strides
lowerCAmelCase = mlp_ratios
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = initializer_range
lowerCAmelCase = drop_path_rate
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = decoder_hidden_size
lowerCAmelCase = max_depth
lowerCAmelCase = head_in_index
| 4 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCamelCase :List[Any] = logging.get_logger(__name__)
class _lowerCAmelCase ( a__ ):
__SCREAMING_SNAKE_CASE : int = ['pixel_values']
def __init__(self , lowercase = True , lowercase = None , lowercase = None , lowercase = PILImageResampling.BILINEAR , lowercase = True , lowercase = 1 / 255 , lowercase = True , lowercase = None , lowercase = None , **lowercase , ):
super().__init__(**_snake_case )
A_ : List[Any] = size if size is not None else {"""shortest_edge""": 384}
A_ : Optional[Any] = get_size_dict(_snake_case , default_to_square=_snake_case )
A_ : Any = do_resize
A_ : Any = size
# Default value set here for backwards compatibility where the value in config is None
A_ : Union[str, Any] = crop_pct if crop_pct is not None else 224 / 256
A_ : Tuple = resample
A_ : Dict = do_rescale
A_ : Dict = rescale_factor
A_ : int = do_normalize
A_ : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A_ : Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _a (self , lowercase , lowercase , lowercase , lowercase = PILImageResampling.BICUBIC , lowercase = None , **lowercase , ):
A_ : int = get_size_dict(_snake_case , default_to_square=_snake_case )
if "shortest_edge" not in size:
raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
A_ : Tuple = size["""shortest_edge"""]
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
A_ : Any = int(shortest_edge / crop_pct )
A_ : Optional[Any] = get_resize_output_image_size(_snake_case , size=_snake_case , default_to_square=_snake_case )
A_ : str = resize(image=_snake_case , size=_snake_case , resample=_snake_case , data_format=_snake_case , **_snake_case )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_snake_case , size=(shortest_edge, shortest_edge) , data_format=_snake_case , **_snake_case )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_snake_case , size=(shortest_edge, shortest_edge) , resample=_snake_case , data_format=_snake_case , **_snake_case )
def _a (self , lowercase , lowercase , lowercase = None , **lowercase , ):
return rescale(_snake_case , scale=_snake_case , data_format=_snake_case , **_snake_case )
def _a (self , lowercase , lowercase , lowercase , lowercase = None , **lowercase , ):
return normalize(_snake_case , mean=_snake_case , std=_snake_case , data_format=_snake_case , **_snake_case )
def _a (self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ):
A_ : List[Any] = do_resize if do_resize is not None else self.do_resize
A_ : int = crop_pct if crop_pct is not None else self.crop_pct
A_ : Tuple = resample if resample is not None else self.resample
A_ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
A_ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
A_ : Any = image_mean if image_mean is not None else self.image_mean
A_ : Tuple = image_std if image_std is not None else self.image_std
A_ : Any = size if size is not None else self.size
A_ : int = get_size_dict(_snake_case , default_to_square=_snake_case )
A_ : List[str] = make_list_of_images(_snake_case )
if not valid_images(_snake_case ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError("""crop_pct must be specified if size < 384.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
A_ : str = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
A_ : Tuple = [self.resize(image=_snake_case , size=_snake_case , crop_pct=_snake_case , resample=_snake_case ) for image in images]
if do_rescale:
A_ : Optional[int] = [self.rescale(image=_snake_case , scale=_snake_case ) for image in images]
if do_normalize:
A_ : List[str] = [self.normalize(image=_snake_case , mean=_snake_case , std=_snake_case ) for image in images]
A_ : str = [to_channel_dimension_format(_snake_case , _snake_case ) for image in images]
A_ : Any = {"""pixel_values""": images}
return BatchFeature(data=_snake_case , tensor_type=_snake_case ) | 667 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , _snake_case=10_00 , ):
"""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
lowerCAmelCase = range_bbox
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase = bbox[i, j, 3]
lowerCAmelCase = bbox[i, j, 1]
lowerCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase = bbox[i, j, 2]
lowerCAmelCase = bbox[i, j, 0]
lowerCAmelCase = t
lowerCAmelCase = tf.convert_to_tensor(_snake_case )
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 = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForMaskedLM(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForSequenceClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForTokenClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
snake_case__ = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ = False
snake_case__ = True
snake_case__ = 1_0
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = TFLayoutLMModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@unittest.skip('Onnx compliancy broke with TF 2.10' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def _SCREAMING_SNAKE_CASE ():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowerCAmelCase = tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
lowerCAmelCase = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the sequence output on [0, :3, :3]
lowerCAmelCase = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , _snake_case , atol=1E-3 ) )
# test the pooled output on [1, :3]
lowerCAmelCase = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , _snake_case , atol=1E-3 ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=2 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
lowerCAmelCase = outputs.loss
lowerCAmelCase = (2,)
self.assertEqual(loss.shape , _snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = (2, 2)
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=13 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the shape of the logits
lowerCAmelCase = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , _snake_case )
self.assertEqual(outputs.end_logits.shape , _snake_case )
| 4 | 0 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
__snake_case : Optional[Any] = '''bert-base-cased'''
__snake_case : str = '''google/pegasus-xsum'''
__snake_case : int = [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__snake_case : Dict = ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__snake_case : Any = '''patrickvonplaten/t5-tiny-random'''
__snake_case : List[Any] = '''sshleifer/bart-tiny-random'''
__snake_case : Dict = '''sshleifer/tiny-mbart'''
__snake_case : List[Any] = '''sshleifer/tiny-marian-en-de'''
def _lowercase ( __snake_case ,__snake_case ) -> Tuple:
__lowerCAmelCase : Tuple = "\n".join(_UpperCAmelCase )
Path(_UpperCAmelCase ).open("w" ).writelines(_UpperCAmelCase )
def _lowercase ( __snake_case ) -> Dict:
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(_UpperCAmelCase ,F"""{split}.source""" ) ,_UpperCAmelCase )
_dump_articles(os.path.join(_UpperCAmelCase ,F"""{split}.target""" ) ,_UpperCAmelCase )
return tmp_dir
class A__ ( a__ ):
'''simple docstring'''
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def _SCREAMING_SNAKE_CASE ( self: List[Any] , _SCREAMING_SNAKE_CASE: Any) -> str:
"""simple docstring"""
__lowerCAmelCase : Any = AutoTokenizer.from_pretrained(_snake_case)
__lowerCAmelCase : int = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
__lowerCAmelCase : Tuple = max(len(tokenizer.encode(_snake_case)) for a in ARTICLES)
__lowerCAmelCase : Optional[Any] = max(len(tokenizer.encode(_snake_case)) for a in SUMMARIES)
__lowerCAmelCase : Any = 4
__lowerCAmelCase : Tuple = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__lowerCAmelCase , __lowerCAmelCase : Dict = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
__lowerCAmelCase : str = SeqaSeqDataset(
_snake_case , data_dir=_snake_case , type_path="train" , max_source_length=_snake_case , max_target_length=_snake_case , src_lang=_snake_case , tgt_lang=_snake_case , )
__lowerCAmelCase : Dict = DataLoader(_snake_case , batch_size=2 , collate_fn=train_dataset.collate_fn)
for batch in dataloader:
assert isinstance(_snake_case , _snake_case)
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__lowerCAmelCase : Tuple = shift_tokens_right(batch["labels"] , tokenizer.pad_token_id)
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED])
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: List[Any]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = AutoTokenizer.from_pretrained(_snake_case)
__lowerCAmelCase : List[str] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
__lowerCAmelCase : Optional[int] = max(len(tokenizer.encode(_snake_case)) for a in ARTICLES)
__lowerCAmelCase : Union[str, Any] = max(len(tokenizer.encode(_snake_case)) for a in SUMMARIES)
__lowerCAmelCase : Union[str, Any] = 4
__lowerCAmelCase : int = LegacySeqaSeqDataset(
_snake_case , data_dir=_snake_case , type_path="train" , max_source_length=20 , max_target_length=_snake_case , )
__lowerCAmelCase : Dict = DataLoader(_snake_case , batch_size=2 , collate_fn=train_dataset.collate_fn)
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def _SCREAMING_SNAKE_CASE ( self: Any) -> str:
"""simple docstring"""
__lowerCAmelCase : str = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
__lowerCAmelCase : int = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
__lowerCAmelCase : Tuple = tmp_dir.joinpath("train.source").open().readlines()
__lowerCAmelCase : Optional[int] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
pack_data_dir(_snake_case , _snake_case , 128 , _snake_case)
__lowerCAmelCase : Any = {x.name for x in tmp_dir.iterdir()}
__lowerCAmelCase : Tuple = {x.name for x in save_dir.iterdir()}
__lowerCAmelCase : Tuple = save_dir.joinpath("train.source").open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(_snake_case) < len(_snake_case)
assert len(_snake_case) == 1
assert len(packed_examples[0]) == sum(len(_snake_case) for x in orig_examples)
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason="This test requires fairseq")
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> List[str]:
"""simple docstring"""
if not FAIRSEQ_AVAILABLE:
return
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : List[str] = self._get_dataset(max_len=64)
__lowerCAmelCase : Union[str, Any] = 64
__lowerCAmelCase : str = ds.make_dynamic_sampler(_snake_case , required_batch_size_multiple=_snake_case)
__lowerCAmelCase : Tuple = [len(_snake_case) for x in batch_sampler]
assert len(set(_snake_case)) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(_snake_case) == len(_snake_case) # no dropped or added examples
__lowerCAmelCase : Any = DataLoader(_snake_case , batch_sampler=_snake_case , collate_fn=ds.collate_fn , num_workers=2)
__lowerCAmelCase : Optional[Any] = []
__lowerCAmelCase : Dict = []
for batch in data_loader:
__lowerCAmelCase : str = batch["input_ids"].shape
__lowerCAmelCase : List[str] = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__lowerCAmelCase : Tuple = np.product(batch["input_ids"].shape)
num_src_per_batch.append(_snake_case)
if num_src_tokens > (max_tokens * 1.1):
failures.append(_snake_case)
assert num_src_per_batch[0] == max(_snake_case)
if failures:
raise AssertionError(F"""too many tokens in {len(_snake_case)} batches""")
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> Any:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[int] = self._get_dataset(max_len=512)
__lowerCAmelCase : List[str] = 2
__lowerCAmelCase : str = ds.make_sortish_sampler(_snake_case , shuffle=_snake_case)
__lowerCAmelCase : Optional[Any] = DataLoader(_snake_case , batch_size=_snake_case , collate_fn=ds.collate_fn , num_workers=2)
__lowerCAmelCase : Tuple = DataLoader(_snake_case , batch_size=_snake_case , collate_fn=ds.collate_fn , num_workers=2 , sampler=_snake_case)
__lowerCAmelCase : Tuple = tokenizer.pad_token_id
def count_pad_tokens(_SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Optional[Any]="input_ids"):
return [batch[k].eq(_snake_case).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(_snake_case , k="labels")) < sum(count_pad_tokens(_snake_case , k="labels"))
assert sum(count_pad_tokens(_snake_case)) < sum(count_pad_tokens(_snake_case))
assert len(_snake_case) == len(_snake_case)
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: int=1000 , _SCREAMING_SNAKE_CASE: Any=128) -> str:
"""simple docstring"""
if os.getenv("USE_REAL_DATA" , _snake_case):
__lowerCAmelCase : List[Any] = "examples/seq2seq/wmt_en_ro"
__lowerCAmelCase : Optional[int] = max_len * 2 * 64
if not Path(_snake_case).joinpath("train.len").exists():
save_len_file(_snake_case , _snake_case)
else:
__lowerCAmelCase : Union[str, Any] = "examples/seq2seq/test_data/wmt_en_ro"
__lowerCAmelCase : List[str] = max_len * 4
save_len_file(_snake_case , _snake_case)
__lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained(_snake_case)
__lowerCAmelCase : str = SeqaSeqDataset(
_snake_case , data_dir=_snake_case , type_path="train" , max_source_length=_snake_case , max_target_length=_snake_case , n_obs=_snake_case , )
return ds, max_tokens, tokenizer
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : str = self._get_dataset()
__lowerCAmelCase : List[Any] = set(DistributedSortishSampler(_snake_case , 256 , num_replicas=2 , rank=0 , add_extra_examples=_snake_case))
__lowerCAmelCase : Optional[int] = set(DistributedSortishSampler(_snake_case , 256 , num_replicas=2 , rank=1 , add_extra_examples=_snake_case))
assert idsa.intersection(_snake_case) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: List[str]) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained(_snake_case , use_fast=_snake_case)
if tok_name == MBART_TINY:
__lowerCAmelCase : Dict = SeqaSeqDataset(
_snake_case , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) , type_path="train" , max_source_length=4 , max_target_length=8 , src_lang="EN" , tgt_lang="FR" , )
__lowerCAmelCase : List[str] = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__lowerCAmelCase : Dict = SeqaSeqDataset(
_snake_case , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) , type_path="train" , max_source_length=4 , max_target_length=8 , )
__lowerCAmelCase : Optional[Any] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(_snake_case) == 1 if tok_name == BART_TINY else len(_snake_case) == 0 | 293 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__UpperCamelCase : Union[str, Any] = '''examples/'''
__UpperCamelCase : str = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
'''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
__UpperCamelCase : List[str] = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
__UpperCamelCase : Optional[int] = '''README.md'''
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : Any ):
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase ,lowerCAmelCase = REPLACE_PATTERNS[pattern]
lowerCAmelCase = replace.replace('VERSION' , _UpperCAmelCase )
lowerCAmelCase = re_pattern.sub(_UpperCAmelCase , _UpperCAmelCase )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, Any] ):
for folder, directories, fnames in os.walk(_UpperCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase , pattern='examples' )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Dict=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if not patch:
update_version_in_examples(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = '🤗 Transformers currently provides the following architectures'
lowerCAmelCase = '1. Want to contribute a new model?'
with open(_UpperCAmelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase = f.readlines()
# Find the start of the list.
lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
lowerCAmelCase = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(_UpperCAmelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
with open(REPLACE_FILES['init'] , 'r' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase = REPLACE_PATTERNS['init'][0].search(_UpperCAmelCase ).groups()[0]
return packaging.version.parse(_UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple=False ):
lowerCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
lowerCAmelCase = default_version.base_version
elif patch:
lowerCAmelCase = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
lowerCAmelCase = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
lowerCAmelCase = input(F'Which version are you releasing? [{default_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = default_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase , patch=_UpperCAmelCase )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = get_version()
lowerCAmelCase = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
lowerCAmelCase = input(F'Which version are we developing now? [{dev_version}]' )
if len(_UpperCAmelCase ) == 0:
lowerCAmelCase = dev_version
print(F'Updating version to {version}.' )
global_version_update(_UpperCAmelCase )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
__UpperCamelCase : Optional[int] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 4 | 0 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.