


'''simple docstring'''






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

import black


lowercase							=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.
lowercase							='    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       lowerCAmelCase		(      self)						->  Dict:




										'''simple docstring'''
										_UpperCAmelCase       :       Union[str, Any]							   =tempfile.mkdtemp()
										os.makedirs(os.path.join(self.transformer_dir  ,							'models/bert/'))
										_UpperCAmelCase       :       Optional[Any]							   =self.transformer_dir
										shutil.copy(
										    os.path.join(snake_case  ,							'src/transformers/models/bert/modeling_bert.py')  ,							os.path.join(self.transformer_dir  ,							'models/bert/modeling_bert.py')  ,							)






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




										'''simple docstring'''
										_UpperCAmelCase       :       int							   ='src/transformers'
										shutil.rmtree(self.transformer_dir)






							def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case=None)						->  Optional[int]:




										'''simple docstring'''
										_UpperCAmelCase       :       Optional[Any]							   =comment + f"\nclass {class_name}(nn.Module):\n" + class_code
										if overwrite_result is not None:
													_UpperCAmelCase       :       Union[str, Any]							   =comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
										_UpperCAmelCase       :       Tuple							   =black.Mode(target_versions={black.TargetVersion.PYaa}  ,							line_length=1_1_9)
										_UpperCAmelCase       :       str							   =black.format_str(snake_case  ,							mode=snake_case)
										_UpperCAmelCase       :       Dict							   =os.path.join(self.transformer_dir  ,							'new_code.py')
										with open(snake_case  ,							'w'  ,							newline='\n') as f:
													f.write(snake_case)
										if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(snake_case)) == 0)
										else:
													check_copies.is_copy_consistent(f.name  ,							overwrite=snake_case)
													with open(snake_case  ,							'r') as f:
																self.assertTrue(f.read()  ,							snake_case)






							def       lowerCAmelCase		(      self)						->  Dict:




										'''simple docstring'''
										_UpperCAmelCase       :       str							   =check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead')
										self.assertEqual(snake_case  ,							snake_case)






							def       lowerCAmelCase		(      self)						->  Optional[Any]:




										'''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'  ,							snake_case  ,							)

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

										# Copy consistency with a really long name
										_UpperCAmelCase       :       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'  ,							snake_case  ,							snake_case)  ,							)

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






							def       lowerCAmelCase		(      self)						->  List[Any]:




										'''simple docstring'''
										_UpperCAmelCase       :       int							   =check_copies.LOCALIZED_READMES['README_zh-hans.md']

										_UpperCAmelCase       :       Optional[Any]							   =(
										    '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       :       Optional[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'
										)
										_UpperCAmelCase       :       Optional[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。\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       :       Optional[Any]							   =check_copies.convert_to_localized_md(
										    snake_case  ,							snake_case  ,							localized_readme['format_model_list'])

										self.assertFalse(snake_case)
										self.assertEqual(snake_case  ,							snake_case)

										_UpperCAmelCase       :       List[str]							   =check_copies.convert_to_localized_md(
										    snake_case  ,							snake_case  ,							localized_readme['format_model_list'])

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

										_UpperCAmelCase       :       Union[str, Any]							   =(
										    '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       :       Optional[int]							   =(
										    '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       :       List[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'
										)

										_UpperCAmelCase       :       Tuple							   =check_copies.convert_to_localized_md(
										    snake_case  ,							snake_case  ,							localized_readme['format_model_list'])

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





'''simple docstring'''






from __future__ import annotations

from random import choice





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   return choice(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =random_pivot(__lowerCamelCase							)

   # partition based on pivot
   # linear time
   _UpperCAmelCase       :       str							   =[e for e in lst if e < pivot]
   _UpperCAmelCase       :       Dict							   =[e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__lowerCamelCase							) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__lowerCamelCase							) < k - 1:
      return kth_number(__lowerCamelCase	,   k - len(__lowerCamelCase							) - 1							)
   # pivot is in elements smaller than k
   else:
      return kth_number(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

