
import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

_A     =  3






def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


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

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

			return public_key, private_key






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						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()

			lowerCAmelCase_			,						lowerCAmelCase_      			=			generate_key(__lowerCAmelCase       )
			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      lowerCamelCase__       (       ):




			"""simple docstring"""


			print("Making key files..."       )
			make_key_files("elgamal"					,							2048       )
			print("Key files generation successful"       )


if __name__ == "__main__":
			main()





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

import black


_A     =  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.
_A     =  "    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 ):




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir       ,       "models/bert/"  )  )
							lowerCAmelCase_      			=			self.transformer_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase       ,       "src/transformers/models/bert/modeling_bert.py"  )       ,       os.path.join(self.transformer_dir       ,       "models/bert/modeling_bert.py"  )       ,       )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			"src/transformers"
							shutil.rmtree(self.transformer_dir  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   Optional[int]:
							lowerCAmelCase_      			=			comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
							if overwrite_result is not None:
										lowerCAmelCase_      			=			comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
							lowerCAmelCase_      			=			black.Mode(target_versions={black.TargetVersion.PYaa}       ,       line_length=119  )
							lowerCAmelCase_      			=			black.format_str(_UpperCamelCase       ,       mode=_UpperCamelCase  )
							lowerCAmelCase_      			=			os.path.join(self.transformer_dir       ,       "new_code.py"  )
							with open(_UpperCamelCase       ,       "w"       ,       newline="\n"  ) as f:
										f.write(_UpperCamelCase  )
							if overwrite_result is None:
										self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase  )  ) == 0  )
							else:
										check_copies.is_copy_consistent(f.name       ,       overwrite=_UpperCamelCase  )
										with open(_UpperCamelCase       ,       "r"  ) as f:
													self.assertTrue(f.read()       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"  )
							self.assertEqual(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Any:
							# 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"       ,       _UpperCamelCase       ,       )

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

							# Copy consistency with a really long name
							lowerCAmelCase_      			=			"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"       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ,       )

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




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			check_copies.LOCALIZED_READMES["README_zh-hans.md"]

							lowerCAmelCase_      			=			(
							    "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."
							)
							lowerCAmelCase_      			=			(
							    "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"
							)
							lowerCAmelCase_      			=			(
							    "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"
							)

							lowerCAmelCase_			,						lowerCAmelCase_      			=			check_copies.convert_to_localized_md(
							    _UpperCamelCase       ,       _UpperCamelCase       ,       localized_readme["format_model_list"]  )

							self.assertFalse(_UpperCamelCase  )
							self.assertEqual(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_			,						lowerCAmelCase_      			=			check_copies.convert_to_localized_md(
							    _UpperCamelCase       ,       _UpperCamelCase       ,       localized_readme["format_model_list"]  )

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

							lowerCAmelCase_      			=			(
							    "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."
							)
							lowerCAmelCase_      			=			(
							    "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"
							)
							lowerCAmelCase_      			=			(
							    "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"
							)

							lowerCAmelCase_			,						lowerCAmelCase_      			=			check_copies.convert_to_localized_md(
							    _UpperCamelCase       ,       _UpperCamelCase       ,       localized_readme["format_model_list"]  )

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



