


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

import black


__snake_case            =   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            =   "    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    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir       ,       """models/bert/"""						)						)
						UpperCamelCase         =					self.transformer_dir
						shutil.copy(
						    os.path.join(__magic_name__       ,       """src/transformers/models/bert/modeling_bert.py"""						)       ,       os.path.join(self.transformer_dir       ,       """models/bert/modeling_bert.py"""						)       ,       )




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					"""src/transformers"""
						shutil.rmtree(self.transformer_dir						)




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Tuple=None						):







						"""simple docstring"""







						UpperCamelCase         =					comment + F'\nclass {class_name}(nn.Module):\n' + class_code
						if overwrite_result is not None:
									UpperCamelCase         =					comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
						UpperCamelCase         =					black.Mode(target_versions={black.TargetVersion.PYaa}       ,       line_length=1_1_9						)
						UpperCamelCase         =					black.format_str(__magic_name__       ,       mode=__magic_name__						)
						UpperCamelCase         =					os.path.join(self.transformer_dir       ,       """new_code.py"""						)
						with open(__magic_name__       ,       """w"""       ,       newline="""\n"""						) as f:
									f.write(__magic_name__						)
						if overwrite_result is None:
									self.assertTrue(len(check_copies.is_copy_consistent(__magic_name__						)						) == 0						)
						else:
									check_copies.is_copy_consistent(f.name       ,       overwrite=__magic_name__						)
									with open(__magic_name__       ,       """r"""						) as f:
												self.assertTrue(f.read()       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""						)
						self.assertEqual(__magic_name__       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		int						):







						"""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"""       ,       __magic_name__       ,       )

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

						# Copy consistency with a really long name
						UpperCamelCase         =					"""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"""       ,       __magic_name__       ,       __magic_name__						)       ,       )

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





			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

						UpperCamelCase         =					(
						    """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         =					(
						    """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         =					(
						    """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         =					check_copies.convert_to_localized_md(
						    __magic_name__       ,       __magic_name__       ,       localized_readme["""format_model_list"""]						)

						self.assertFalse(__magic_name__						)
						self.assertEqual(__magic_name__       ,       __magic_name__						)

						UpperCamelCase  ,  UpperCamelCase         =					check_copies.convert_to_localized_md(
						    __magic_name__       ,       __magic_name__       ,       localized_readme["""format_model_list"""]						)

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

						UpperCamelCase         =					(
						    """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         =					(
						    """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         =					(
						    """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         =					check_copies.convert_to_localized_md(
						    __magic_name__       ,       __magic_name__       ,       localized_readme["""format_model_list"""]						)

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








import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

__snake_case            =   3






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			print("""Generating primitive root of p"""	)
			while True:
						UpperCamelCase         =					random.randrange(3       ,  SCREAMING_SNAKE_CASE_	)
						if pow(SCREAMING_SNAKE_CASE_       ,  2       ,  SCREAMING_SNAKE_CASE_	) == 1:
									continue
						if pow(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) == 1:
									continue
						return g






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			print("""Generating prime p..."""	)
			UpperCamelCase         =					rabin_miller.generate_large_prime(SCREAMING_SNAKE_CASE_	)  # select large prime number.
			UpperCamelCase         =					primitive_root(SCREAMING_SNAKE_CASE_	)  # one primitive root on modulo p.
			UpperCamelCase         =					random.randrange(3       ,  SCREAMING_SNAKE_CASE_	)  # private_key -> have to be greater than 2 for safety.
			UpperCamelCase         =					cryptomath.find_mod_inverse(pow(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)       ,  SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					(key_size, e_a, e_a, p)
			UpperCamelCase         =					(key_size, d)

			return public_key, private_key






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			if os.path.exists(f'{name}_pubkey.txt'	) or os.path.exists(f'{name}_privkey.txt'	):
						print("""\nWARNING:"""	)
						print(
						    f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
						    """Use a different name or delete these files and re-run this program."""	)
						sys.exit()

			UpperCamelCase  ,  UpperCamelCase         =					generate_key(SCREAMING_SNAKE_CASE_	)
			print(f'\nWriting public key to file {name}_pubkey.txt...'	)
			with open(f'{name}_pubkey.txt'       ,  """w"""	) as fo:
						fo.write(f'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'	)

			print(f'Writing private key to file {name}_privkey.txt...'	)
			with open(f'{name}_privkey.txt'       ,  """w"""	) as fo:
						fo.write(f'{private_key[0]},{private_key[1]}'	)






def 							_lowercase    (		):





			"""simple docstring"""




			print("""Making key files..."""	)
			make_key_files("""elgamal"""       ,  2_048	)
			print("""Key files generation successful"""	)


if __name__ == "__main__":
			main()




