

'''simple docstring'''



from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def __init__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						pyspark.sql.DataFrame		,      lowerCAmelCase_	:						Optional[NamedSplit] = None		,      lowerCAmelCase_	:						Optional[Features] = None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						str = None		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						str = None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						str = "arrow"		,      **lowerCAmelCase_	:						Dict		,      )      ->		str:





									'''simple docstring'''
									super().__init__(
									    split=lowerCAmelCase_		,      features=lowerCAmelCase_		,      cache_dir=lowerCAmelCase_		,      keep_in_memory=lowerCAmelCase_		,      streaming=lowerCAmelCase_		,      **lowerCAmelCase_		,      )
									A__					:       List[Any]     =load_from_cache_file
									A__					:       Union[str, Any]     =file_format
									A__					:       List[Any]     =Spark(
									    df=lowerCAmelCase_		,      features=lowerCAmelCase_		,      cache_dir=lowerCAmelCase_		,      working_dir=lowerCAmelCase_		,      **lowerCAmelCase_		,      )



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





									'''simple docstring'''
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split			)
									A__					:       List[str]     =None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=lowerCAmelCase_		,      file_format=self._file_format		,      )
									return self.builder.as_dataset(split=self.split			)




'''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_			)

