
'''simple docstring'''






UpperCAmelCase_							: List[Any] =							[
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

UpperCAmelCase_							: str =							[
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

UpperCAmelCase_							: str =							[
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

UpperCAmelCase_							: Optional[Any] =							[
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

UpperCAmelCase_							: int =							[
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

UpperCAmelCase_							: Optional[int] =							[
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

UpperCAmelCase_							: Optional[Any] =							[
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

UpperCAmelCase_							: List[str] =							[
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]


'''simple docstring'''

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE							:				List[str]     	=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE							:				List[str]     	=			"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir,     'models/bert/'       )       )
											SCREAMING_SNAKE_CASE_				=				self.transformer_dir
											shutil.copy(
											    os.path.join(_lowercase,     'src/transformers/models/bert/modeling_bert.py'       ),     os.path.join(self.transformer_dir,     'models/bert/modeling_bert.py'       ),     )



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				'src/transformers'
											shutil.rmtree(self.transformer_dir       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase=None       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
											if overwrite_result is not None:
																		SCREAMING_SNAKE_CASE_				=				comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
											SCREAMING_SNAKE_CASE_				=				black.Mode(target_versions={black.TargetVersion.PYaa},     line_length=119       )
											SCREAMING_SNAKE_CASE_				=				black.format_str(_lowercase,     mode=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				os.path.join(self.transformer_dir,     'new_code.py'       )
											with open(_lowercase,     'w',     newline='\n'       ) as f:
																		f.write(_lowercase       )
											if overwrite_result is None:
																		self.assertTrue(len(check_copies.is_copy_consistent(_lowercase       )       ) == 0       )
											else:
																		check_copies.is_copy_consistent(f.name,     overwrite=_lowercase       )
																		with open(_lowercase,     'r'       ) as f:
																									self.assertTrue(f.read(),     _lowercase       )



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'       )
											self.assertEqual(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Union[str, Any]:
											# Base copy consistency
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead',     'BertLMPredictionHead',     REFERENCE_CODE + '\n',     )

											# With no empty line at the end
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead',     'BertLMPredictionHead',     _lowercase,     )

											# Copy consistency with rename
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel',     'TestModelLMPredictionHead',     re.sub('Bert',     'TestModel',     _lowercase       ),     )

											# Copy consistency with a really long name
											SCREAMING_SNAKE_CASE_				=				'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
											self.check_copy_consistency(
											    f"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}""",     f"""{long_class_name}LMPredictionHead""",     re.sub('Bert',     _lowercase,     _lowercase       ),     )

											# Copy consistency with overwrite
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel',     'TestModelLMPredictionHead',     _lowercase,     overwrite_result=re.sub('Bert',     'TestModel',     _lowercase       ),     )





				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				check_copies.LOCALIZED_READMES['README_zh-hans.md']

											SCREAMING_SNAKE_CASE_				=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
											    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
											    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
											    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
											    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
											    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
											    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
											    ' method has been applied to compress GPT2 into'
											    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
											    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
											    ' Multilingual BERT into'
											    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
											    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
											    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
											    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
											    ' Luong, Quoc V. Le, Christopher D. Manning.'
											)
											SCREAMING_SNAKE_CASE_				=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)
											SCREAMING_SNAKE_CASE_				=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
											    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
											    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
											    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
											    ' method has been applied to compress GPT2 into'
											    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
											    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
											    ' Multilingual BERT into'
											    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
											    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
											    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
											    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
											    ' Christopher D. Manning 发布。\n'
											)

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

											self.assertFalse(_lowercase       )
											self.assertEqual(_lowercase,     _lowercase       )

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

											# Check whether the number of models is equal to README.md after conversion.
											self.assertTrue(_lowercase       )

											SCREAMING_SNAKE_CASE_				=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
											    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
											    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
											    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
											)
											SCREAMING_SNAKE_CASE_				=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
											    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)
											SCREAMING_SNAKE_CASE_				=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

											# Check if the model link is synchronized.
											self.assertEqual(_lowercase,     _lowercase       )





