

import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__snake_case			:     Any										=      pytest.mark.integration
@pytest.mark.parametrize("""path""",		["""paws""", """csv"""]	)
def 						__lowerCamelCase	(       __snake_case					:		List[Any],		__snake_case					:		int	)	->							Tuple:






						"""simple docstring"""





						inspect_dataset(__snake_case,		__snake_case	)
						A__					:       Optional[int]     =path + """.py"""
						assert script_name in os.listdir(__snake_case	)
						assert "__pycache__" not in os.listdir(__snake_case	)
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""	)
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""	)
@pytest.mark.parametrize("""path""",		["""accuracy"""]	)
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		List[Any]	)	->							Dict:






						"""simple docstring"""





						inspect_metric(__snake_case,		__snake_case	)
						A__					:       List[str]     =path + """.py"""
						assert script_name in os.listdir(__snake_case	)
						assert "__pycache__" not in os.listdir(__snake_case	)
@pytest.mark.parametrize(
    """path, config_name, expected_splits""",		[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ],		)
def 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		Optional[int],		__snake_case					:		int	)	->							Dict:






						"""simple docstring"""





						A__					:       int     =get_dataset_config_info(__snake_case,		config_name=__snake_case	)
						assert info.config_name == config_name
						assert list(info.splits.keys()	) == expected_splits
@pytest.mark.parametrize(
    """path, config_name, expected_exception""",		[
        ("""paws""", None, ValueError),
    ],		)
def 						__lowerCamelCase	(       __snake_case					:		Tuple,		__snake_case					:		List[Any],		__snake_case					:		Union[str, Any]	)	->							Dict:






						"""simple docstring"""





						with pytest.raises(__snake_case	):
												get_dataset_config_info(__snake_case,		config_name=__snake_case	)
@pytest.mark.parametrize(
    """path, expected""",		[
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ],		)
def 						__lowerCamelCase	(       __snake_case					:		Dict,		__snake_case					:		Any	)	->							Optional[int]:






						"""simple docstring"""





						A__					:       str     =get_dataset_config_names(__snake_case	)
						assert expected in config_names
@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config""",		[
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ],		)
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		Dict,		__snake_case					:		Tuple	)	->							Union[str, Any]:






						"""simple docstring"""





						A__					:       Optional[int]     =get_dataset_infos(__snake_case	)
						assert list(infos.keys()	) == expected_configs
						A__					:       Optional[int]     =expected_configs[0]
						assert expected_config in infos
						A__					:       int     =infos[expected_config]
						assert info.config_name == expected_config
						assert list(info.splits.keys()	) == expected_splits_in_first_config
@pytest.mark.parametrize(
    """path, expected_config, expected_splits""",		[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ],		)
def 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		Tuple,		__snake_case					:		str	)	->							Any:






						"""simple docstring"""





						A__					:       Any     =get_dataset_infos(__snake_case	)
						assert expected_config in infos
						A__					:       int     =infos[expected_config]
						assert info.config_name == expected_config
						assert list(info.splits.keys()	) == expected_splits
@pytest.mark.parametrize(
    """path, config_name, expected_exception""",		[
        ("""paws""", None, ValueError),
    ],		)
def 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		Union[str, Any],		__snake_case					:		List[str]	)	->							Dict:






						"""simple docstring"""





						with pytest.raises(__snake_case	):
												get_dataset_split_names(__snake_case,		config_name=__snake_case	)




'''simple docstring'''



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

import black


__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.
__snake_case			:     Optional[int]										=      '    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'



class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Tuple			)      ->		Dict:





									'''simple docstring'''
									A__					:       Tuple     =tempfile.mkdtemp()
									os.makedirs(os.path.join(self.transformer_dir		,      """models/bert/"""			)			)
									A__					:       List[str]     =self.transformer_dir
									shutil.copy(
									    os.path.join(lowerCAmelCase_		,      """src/transformers/models/bert/modeling_bert.py"""			)		,      os.path.join(self.transformer_dir		,      """models/bert/modeling_bert.py"""			)		,      )


			def lowercase__						(				self	:						str			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     ="""src/transformers"""
									shutil.rmtree(self.transformer_dir			)


			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Any		,      lowerCAmelCase_	:						List[Any]		,      lowerCAmelCase_	:						Optional[Any]		,      lowerCAmelCase_	:						int=None			)      ->		Dict:





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


			def lowercase__						(				self	:						Any			)      ->		Any:





									'''simple docstring'''
									A__					:       Dict     =check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""			)
									self.assertEqual(lowerCAmelCase_		,      lowerCAmelCase_			)


			def lowercase__						(				self	:						Dict			)      ->		List[Any]:





									'''simple docstring'''
									# 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"""		,      lowerCAmelCase_		,      )

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

									# Copy consistency with a really long name
									A__					:       Tuple     ="""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"""		,      lowerCAmelCase_		,      lowerCAmelCase_			)		,      )

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



			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       List[Any]     =check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

									A__					:       List[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__					:       Union[str, 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__					:       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__					:       Optional[Any]     =check_copies.convert_to_localized_md(
									    lowerCAmelCase_		,      lowerCAmelCase_		,      localized_readme["""format_model_list"""]			)

									self.assertFalse(lowerCAmelCase_			)
									self.assertEqual(lowerCAmelCase_		,      lowerCAmelCase_			)

									A__   ,						A__					:       List[str]     =check_copies.convert_to_localized_md(
									    lowerCAmelCase_		,      lowerCAmelCase_		,      localized_readme["""format_model_list"""]			)

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

									A__					:       Dict     =(
									    """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__					:       str     =(
									    """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__					:       int     =(
									    """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(
									    lowerCAmelCase_		,      lowerCAmelCase_		,      localized_readme["""format_model_list"""]			)

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

