




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

import black


snake_case_ : Tuple    		=       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_ : str    		=       "    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 __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir					,						'''models/bert/'''))
        UpperCAmelCase_          =							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		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							'''src/transformers'''
        shutil.rmtree(self.transformer_dir)







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Any					,						_snake_case		:						Any					,						_snake_case		:						List[Any]					,						_snake_case		:						List[str]=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=119)
        UpperCAmelCase_          =							black.format_str(_snake_case					,						mode=_snake_case)
        UpperCAmelCase_          =							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		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''')
        self.assertEqual(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						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_          =							'''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		:						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(
            _snake_case					,						_snake_case					,						localized_readme['''format_model_list'''])

        self.assertFalse(_snake_case)
        self.assertEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_     ,       UpperCAmelCase_          =							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_          =							(
            '''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(
            _snake_case					,						_snake_case					,						localized_readme['''format_model_list'''])

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












def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			""
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			[chr(i + 65   ) for i in range(26   )]
			# Remove duplicate characters from key
			a    =			remove_duplicates(key.upper()   )
			a    =			len(A   )
			# First fill cipher with key characters
			a    =			{alphabet[i]: char for i, char in enumerate(A   )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(A   ),  26   ):
						a    =			alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									a    =			alphabet[i - offset]
						a    =			char
			return cipher_alphabet




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			return "".join(cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			a    =			{v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			input("Enter message to encode or decode: "   ).strip()
			a    =			input("Enter keyword: "   ).strip()
			a    =			input("Encipher or decipher? E/D:"   ).strip()[0].lower()
			try:
						a    =			{"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"   )
			a    =			create_cipher_map(A   )
			print(func(A,  A   )   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






