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

import black


_UpperCAmelCase            =		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.
_UpperCAmelCase            =		"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir		,     'models/bert/' ) )
						A_		:    Tuple         =				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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				'src/transformers'
						shutil.rmtree(self.transformer_dir )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase=None ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
						if overwrite_result is not None:
								A_		:    Dict         =				comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
						A_		:    Optional[int]         =				black.Mode(target_versions={black.TargetVersion.PYaa}		,     line_length=1_1_9 )
						A_		:    int         =				black.format_str(lowercase		,     mode=lowercase )
						A_		:    List[Any]         =				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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
						self.assertEqual(lowercase		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""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'		,     lowercase		,     )

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

						# Copy consistency with a really long name
						A_		:    Optional[Any]         =				'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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				check_copies.LOCALIZED_READMES['README_zh-hans.md']

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

						A_       ,      A_		:    str         =				check_copies.convert_to_localized_md(
						    lowercase		,     lowercase		,     localized_readme['format_model_list'] )

						self.assertFalse(lowercase )
						self.assertEqual(lowercase		,     lowercase )

						A_       ,      A_		:    Tuple         =				check_copies.convert_to_localized_md(
						    lowercase		,     lowercase		,     localized_readme['format_model_list'] )

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

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

						A_       ,      A_		:    int         =				check_copies.convert_to_localized_md(
						    lowercase		,     lowercase		,     localized_readme['format_model_list'] )

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







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_UpperCAmelCase            =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			str     ):
		'''simple docstring'''




		return (abs(source - target     ) / target) < 0.01




@pytest.mark.integration
def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				_TestCommandArgs(dataset=__lowercase						,all_configs=__lowercase						,save_infos=__lowercase     )
		A_		:    List[Any]         =				TestCommand(*__lowercase     )
		test_command.run()
		A_		:    Any         =				os.path.join(__lowercase						,'README.md'     )
		assert os.path.exists(__lowercase     )
		A_		:    Tuple         =				DatasetInfosDict.from_directory(__lowercase     )
		A_		:    Any         =				DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'     )     ),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']     )     ),
		                    'langs': Sequence(Value('string'     )     ),
		                    'spans': Sequence(Value('string'     )     ),
		                }     )						,splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_35_15_63,
		                    'num_examples': 1_00_00,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 23_84_18,
		                    'num_examples': 10_00,
		                },
		            ]						,download_size=3_94_06_80						,dataset_size=2_58_99_81						,)
		    }     )
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				A_       ,      A_		:    Union[str, Any]         =				getattr(dataset_infos['default']						,__lowercase     ), getattr(expected_dataset_infos['default']						,__lowercase     )
				if key == "num_bytes":
						assert is_apercent_close(__lowercase						,__lowercase     )
				elif key == "splits":
						assert list(__lowercase     ) == list(__lowercase     )
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,expected[split].num_bytes     )
				else:
						result == expected






