


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


A	:		List[Any]    =       logging.get_logger(__name__)

A	:		List[Any]    =       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A	:		Dict    =       {
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
    },
}

A	:		Tuple    =       {
    'gpt2': 1_0_2_4,
    'gpt2-medium': 1_0_2_4,
    'gpt2-large': 1_0_2_4,
    'gpt2-xl': 1_0_2_4,
    'distilgpt2': 1_0_2_4,
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			VOCAB_FILES_NAMES
					A__      =			PRETRAINED_VOCAB_FILES_MAP
					A__      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__      =			['''input_ids''', '''attention_mask''']
					A__      =			GPTaTokenizer

					def __init__(self   :						Tuple				, _UpperCAmelCase   :						str=None				, _UpperCAmelCase   :						str=None				, _UpperCAmelCase   :						str=None				, _UpperCAmelCase   :						List[str]="<|endoftext|>"				, _UpperCAmelCase   :						Union[str, Any]="<|endoftext|>"				, _UpperCAmelCase   :						int="<|endoftext|>"				, _UpperCAmelCase   :						Optional[int]=False				, **_UpperCAmelCase   :						str				, )						->       Optional[int]:




							"""simple docstring"""


							super().__init__(
							    _UpperCAmelCase				, _UpperCAmelCase				, tokenizer_file=_UpperCAmelCase				, unk_token=_UpperCAmelCase				, bos_token=_UpperCAmelCase				, eos_token=_UpperCAmelCase				, add_prefix_space=_UpperCAmelCase				, **_UpperCAmelCase				, )

							lowercase__					      =		kwargs.pop("""add_bos_token"""				, _UpperCAmelCase		)

							lowercase__					      =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
							if pre_tok_state.get("""add_prefix_space"""				, _UpperCAmelCase		) != add_prefix_space:
									lowercase__					      =		getattr(_UpperCAmelCase				, pre_tok_state.pop("""type"""		)		)
									lowercase__					      =		add_prefix_space
									lowercase__					      =		pre_tok_class(**_UpperCAmelCase		)

							lowercase__					      =		add_prefix_space

					def   lowerCamelCase__					(self   :						Optional[Any]				, *_UpperCAmelCase   :						List[str]				, **_UpperCAmelCase   :						Tuple		)						->       BatchEncoding:




							"""simple docstring"""


							lowercase__					      =		kwargs.get("""is_split_into_words"""				, _UpperCAmelCase		)
							assert self.add_prefix_space or not is_split_into_words, (
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*_UpperCAmelCase				, **_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						List[str]				, *_UpperCAmelCase   :						Optional[int]				, **_UpperCAmelCase   :						Optional[Any]		)						->       BatchEncoding:




							"""simple docstring"""


							lowercase__					      =		kwargs.get("""is_split_into_words"""				, _UpperCAmelCase		)

							assert self.add_prefix_space or not is_split_into_words, (
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*_UpperCAmelCase				, **_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						str				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Optional[str] = None		)						->       Tuple[str]:




							"""simple docstring"""


							lowercase__					      =		self._tokenizer.model.save(_UpperCAmelCase				, name=_UpperCAmelCase		)
							return tuple(_UpperCAmelCase		)


					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						"Conversation"		)						->       List[int]:




							"""simple docstring"""


							lowercase__					      =		[]
							for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(_UpperCAmelCase				, add_special_tokens=_UpperCAmelCase		) + [self.eos_token_id]		)

							if len(_UpperCAmelCase		) > self.model_max_length:
									lowercase__					      =		input_ids[-self.model_max_length :]
							return input_ids





A	:		Tuple    =       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def 			UpperCamelCase (     )       ->  None:
		"""simple docstring"""




		lowercase__					      =		input("""Enter message: """					)
		lowercase__					      =		input("""Enter key [alphanumeric]: """					)
		lowercase__					      =		input("""Encrypt/Decrypt [e/d]: """					)

		if mode.lower().startswith("""e"""					):
				lowercase__					      =		"""encrypt"""
				lowercase__					      =		encrypt_message(__magic_name__   ,   __magic_name__					)
		elif mode.lower().startswith("""d"""					):
				lowercase__					      =		"""decrypt"""
				lowercase__					      =		decrypt_message(__magic_name__   ,   __magic_name__					)

		print(f'''\n{mode.title()}ed message:'''					)
		print(__magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		return translate_message(__magic_name__   ,   __magic_name__   ,   """encrypt"""					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		return translate_message(__magic_name__   ,   __magic_name__   ,   """decrypt"""					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		[]
		lowercase__					      =		0
		lowercase__					      =		key.upper()

		for symbol in message:
				lowercase__					      =		LETTERS.find(symbol.upper()					)
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]					)
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]					)

						num %= len(__magic_name__					)

						if symbol.isupper():
								translated.append(LETTERS[num]					)
						elif symbol.islower():
								translated.append(LETTERS[num].lower()					)

						key_index += 1
						if key_index == len(__magic_name__					):
								lowercase__					      =		0
				else:
						translated.append(__magic_name__					)
		return "".join(__magic_name__					)


if __name__ == "__main__":
	main()

