



'''simple docstring'''





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__lowerCamelCase     :							Any  							=				get_logger(__name__)





class  __magic_name__   (							enum.Enum						):
		lowercase      :       int          ='''all_checks'''
		lowercase      :       Optional[Any]          ='''basic_checks'''
		lowercase      :       List[Any]          ='''no_checks'''





class  __magic_name__   (							UpperCAmelCase__						):
		pass





class  __magic_name__   (							UpperCAmelCase__						):
		pass





class  __magic_name__   (							UpperCAmelCase__						):
		pass





class  __magic_name__   (							UpperCAmelCase__						):
		pass

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=None    ) ->       str:

						if expected_checksums is None:
												logger.info("Unable to verify checksums."    )
												return
						if len(set(UpperCAmelCase__    ) - set(UpperCAmelCase__    )    ) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(UpperCAmelCase__    ) - set(UpperCAmelCase__    )    )    )
						if len(set(UpperCAmelCase__    ) - set(UpperCAmelCase__    )    ) > 0:
												raise UnexpectedDownloadedFile(str(set(UpperCAmelCase__    ) - set(UpperCAmelCase__    )    )    )
						UpperCAmelCase         =     [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						UpperCAmelCase         =     " for " + verification_name if verification_name is not None else ""
						if len(UpperCAmelCase__    ) > 0:
												raise NonMatchingChecksumError(
												    F'Checksums didn\'t match{for_verification_name}:\n'
												    F'{bad_urls}\n'
												    "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"    )
						logger.info("All the checksums matched successfully" + for_verification_name    )





class  __magic_name__   (							UpperCAmelCase__						):
		pass





class  __magic_name__   (							UpperCAmelCase__						):
		pass





class  __magic_name__   (							UpperCAmelCase__						):
		pass





class  __magic_name__   (							UpperCAmelCase__						):
		pass

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Union[str, Any]:

						if expected_splits is None:
												logger.info("Unable to verify splits sizes."    )
												return
						if len(set(UpperCAmelCase__    ) - set(UpperCAmelCase__    )    ) > 0:
												raise ExpectedMoreSplits(str(set(UpperCAmelCase__    ) - set(UpperCAmelCase__    )    )    )
						if len(set(UpperCAmelCase__    ) - set(UpperCAmelCase__    )    ) > 0:
												raise UnexpectedSplits(str(set(UpperCAmelCase__    ) - set(UpperCAmelCase__    )    )    )
						UpperCAmelCase         =     [
						    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(UpperCAmelCase__    ) > 0:
												raise NonMatchingSplitsSizesError(str(UpperCAmelCase__    )    )
						logger.info("All the splits matched successfully."    )

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_ = True    ) ->       Dict:

						if record_checksum:
												UpperCAmelCase         =     shaaaa()
												with open(UpperCAmelCase__    ,   "rb"    ) as f:
																		for chunk in iter(lambda: f.read(1 << 20    )    ,   B""    ):
																								m.update(UpperCAmelCase__    )
																		UpperCAmelCase         =     m.hexdigest()
						else:
												UpperCAmelCase         =     None
						return {"num_bytes": os.path.getsize(UpperCAmelCase__    ), "checksum": checksum}





def    lowerCamelCase_(lowerCamelCase_    ) ->       Dict:

						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False








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

import black


__lowerCamelCase     :							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.
__lowerCamelCase     :							Optional[Any]  							=				"    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  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir				,		"models/bert/"      )      )
								UpperCAmelCase         =     self.transformer_dir
								shutil.copy(
								    os.path.join(UpperCamelCase__				,		"src/transformers/models/bert/modeling_bert.py"      )				,		os.path.join(self.transformer_dir				,		"models/bert/modeling_bert.py"      )				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     "src/transformers"
								shutil.rmtree(self.transformer_dir      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]=None      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
														UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
								UpperCAmelCase         =     black.Mode(target_versions={black.TargetVersion.PYaa}				,		line_length=1_19      )
								UpperCAmelCase         =     black.format_str(UpperCamelCase__				,		mode=UpperCamelCase__      )
								UpperCAmelCase         =     os.path.join(self.transformer_dir				,		"new_code.py"      )
								with open(UpperCamelCase__				,		"w"				,		newline="\n"      ) as f:
														f.write(UpperCamelCase__      )
								if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase__      )      ) == 0      )
								else:
														check_copies.is_copy_consistent(f.name				,		overwrite=UpperCamelCase__      )
														with open(UpperCamelCase__				,		"r"      ) as f:
																				self.assertTrue(f.read()				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								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"				,		UpperCamelCase__				,		)

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

								# Copy consistency with a really long name
								UpperCAmelCase         =     "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"				,		UpperCamelCase__				,		UpperCamelCase__      )				,		)

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







		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.LOCALIZED_READMES["README_zh-hans.md"]

								UpperCAmelCase         =     (
								    "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."
								)
								UpperCAmelCase         =     (
								    "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"
								)
								UpperCAmelCase         =     (
								    "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"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								self.assertFalse(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

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

								UpperCAmelCase         =     (
								    "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."
								)
								UpperCAmelCase         =     (
								    "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"
								)
								UpperCAmelCase         =     (
								    "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"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

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



