import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


SCREAMING_SNAKE_CASE		:     Dict         =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE		:     Any         =	{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE		:     Dict         =	{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE		:     Any         =	{
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}

class        UpperCamelCase						(				__a				):
						a__		:List[Any]      =			VOCAB_FILES_NAMES
						a__		:Optional[int]      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:str      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:List[str]      =			['''input_ids''', '''attention_mask''']
						a__		:Union[str, Any]      =			BartTokenizer





						def __init__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase="replace"			,     __UpperCamelCase="<s>"			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase="<pad>"			,     __UpperCamelCase="<mask>"			,     __UpperCamelCase=False			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				List[str]:
												super().__init__(
												    __UpperCamelCase			,     __UpperCamelCase			,     tokenizer_file=__UpperCamelCase			,     errors=__UpperCamelCase			,     bos_token=__UpperCamelCase			,     eos_token=__UpperCamelCase			,     sep_token=__UpperCamelCase			,     cls_token=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     pad_token=__UpperCamelCase			,     mask_token=__UpperCamelCase			,     add_prefix_space=__UpperCamelCase			,     trim_offsets=__UpperCamelCase			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							Union[str, Any]  =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
												if pre_tok_state.get("""add_prefix_space"""			,     __UpperCamelCase						) != add_prefix_space:
																		UpperCamelCase_		:							Optional[int]  =		getattr(__UpperCamelCase			,     pre_tok_state.pop("""type"""						)						)
																		UpperCamelCase_		:							List[str]  =		add_prefix_space
																		UpperCamelCase_		:							List[Any]  =		pre_tok_class(**__UpperCamelCase						)

												UpperCamelCase_		:							List[str]  =		add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												UpperCamelCase_		:							Union[str, Any]  =		"""post_processor"""
												UpperCamelCase_		:							Union[str, Any]  =		getattr(self.backend_tokenizer			,     __UpperCamelCase			,     __UpperCamelCase						)
												if tokenizer_component_instance:
																		UpperCamelCase_		:							Union[str, Any]  =		json.loads(tokenizer_component_instance.__getstate__()						)

																		# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																		if "sep" in state:
																								UpperCamelCase_		:							Optional[Any]  =		tuple(state["""sep"""]						)
																		if "cls" in state:
																								UpperCamelCase_		:							Tuple  =		tuple(state["""cls"""]						)

																		UpperCamelCase_		:							Optional[Any]  =		False

																		if state.get("""add_prefix_space"""			,     __UpperCamelCase						) != add_prefix_space:
																								UpperCamelCase_		:							int  =		add_prefix_space
																								UpperCamelCase_		:							Any  =		True

																		if state.get("""trim_offsets"""			,     __UpperCamelCase						) != trim_offsets:
																								UpperCamelCase_		:							List[str]  =		trim_offsets
																								UpperCamelCase_		:							List[str]  =		True

																		if changes_to_apply:
																								UpperCamelCase_		:							int  =		getattr(__UpperCamelCase			,     state.pop("""type"""						)						)
																								UpperCamelCase_		:							str  =		component_class(**__UpperCamelCase						)
																								setattr(self.backend_tokenizer			,     __UpperCamelCase			,     __UpperCamelCase						)





						@property
						def        A_							(self						)				->				str:
												if self._mask_token is None:
																		if self.verbose:
																								logger.error("""Using mask_token, but it is not set yet."""						)
																		return None
												return str(self._mask_token						)





						@mask_token.setter
						def        A_							(self			,     __UpperCamelCase						)				->				Any:
												UpperCamelCase_		:							int  =		AddedToken(__UpperCamelCase			,     lstrip=__UpperCamelCase			,     rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase			,     __UpperCamelCase						) else value
												UpperCamelCase_		:							str  =		value





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				BatchEncoding:
												UpperCamelCase_		:							List[Any]  =		kwargs.get("""is_split_into_words"""			,     __UpperCamelCase						)

												if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    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        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				BatchEncoding:
												UpperCamelCase_		:							Any  =		kwargs.get("""is_split_into_words"""			,     __UpperCamelCase						)

												if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												UpperCamelCase_		:							List[str]  =		self._tokenizer.model.save(__UpperCamelCase			,     name=__UpperCamelCase						)
												return tuple(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=None						)				->				str:
												UpperCamelCase_		:							Union[str, Any]  =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																		return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				List[int]:
												UpperCamelCase_		:							List[Any]  =		[self.sep_token_id]
												UpperCamelCase_		:							Optional[int]  =		[self.cls_token_id]

												if token_ids_a is None:
																		return len(cls + token_ids_a + sep						) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812






def     lowerCAmelCase_			(   ):
						print("""Making key files..."""			)
						make_key_files("""rsa"""		, 1024			)
						print("""Key files generation successful."""			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int			):
						print("""Generating prime p..."""			)
						UpperCamelCase_		:							Tuple  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						print("""Generating prime q..."""			)
						UpperCamelCase_		:							List[Any]  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							List[str]  =		p * q

						print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""			)
						while True:
												UpperCamelCase_		:							Union[str, Any]  =		random.randrange(2 ** (key_size - 1)		, 2 ** (key_size)			)
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			) == 1:
																		break

						print("""Calculating d that is mod inverse of e..."""			)
						UpperCamelCase_		:							Tuple  =		cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			)

						UpperCamelCase_		:							Tuple  =		(n, e)
						UpperCamelCase_		:							int  =		(n, d)
						return (public_key, private_key)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				int			):
						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_		:							str  =		generate_key(_SCREAMING_SNAKE_CASE			)
						print(f'''\nWriting public key to file {name}_pubkey.txt...'''			)
						with open(f'''{name}_pubkey.txt'''		, """w"""			) as out_file:
												out_file.write(f'''{key_size},{public_key[0]},{public_key[1]}'''			)

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


if __name__ == "__main__":
	main()






