

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  :      List[Any]			 ="""    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    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir				,      "models/bert/"						)						)
						UpperCAmelCase__:	List[str]									= 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    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= "src/transformers"
						shutil.rmtree(self.transformer_dir						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=None						):
						UpperCAmelCase__:	int									= comment + F"\nclass {class_name}(nn.Module):\n" + class_code
						if overwrite_result is not None:
							UpperCAmelCase__:	List[str]									= 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    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"						)
						self.assertEqual(snake_case_				,      snake_case_						)





					def    _UpperCAmelCase    (				self						):
						# 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"				,      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__:	Dict									= "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    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= check_copies.LOCALIZED_READMES["README_zh-hans.md"]

						UpperCAmelCase__:	List[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__:	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__:	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"
						)

						UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= 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__							,						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__:	List[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"
						)
						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__							,						UpperCAmelCase__:	Optional[int]									= 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

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  2
						__lowerCAmelCase     =  []
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase						)
						if n > 1:
												factors.append(_lowerCAmelCase						)
						return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()
