





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				"""▁"""

UpperCAmelCase_							=				{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_							=				{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase_							=				{
    """facebook/xglm-564M""": 2_048,
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Dict       =	VOCAB_FILES_NAMES
		__a       :       str       =	PRETRAINED_VOCAB_FILES_MAP
		__a       :       List[Any]       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a       :       Tuple       =	["""input_ids""", """attention_mask"""]






		def __init__(							self,   snake_case__,   snake_case__="<s>",   snake_case__="</s>",   snake_case__="</s>",   snake_case__="<s>",   snake_case__="<unk>",   snake_case__="<pad>",   snake_case__ = None,   **snake_case__,   )       -> None:






									"""simple docstring"""
									lowercase_	:   int  				=     {} if sp_model_kwargs is None else sp_model_kwargs

									# Compatibility with the original tokenizer
									lowercase_	:   Any  				=     7
									lowercase_	:   Tuple  				=     [f"""<madeupword{i}>""" for i in range(self.num_madeup_words  )]

									lowercase_	:   str  				=     kwargs.get("""additional_special_tokens""",   []  )
									kwargs["additional_special_tokens"] += [
									    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    bos_token=snake_case__,   eos_token=snake_case__,   unk_token=snake_case__,   sep_token=snake_case__,   cls_token=snake_case__,   pad_token=snake_case__,   sp_model_kwargs=self.sp_model_kwargs,   **snake_case__,   )

									lowercase_	:   Tuple  				=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(str(snake_case__  )  )
									lowercase_	:   Tuple  				=     vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									lowercase_	:   str  				=     1

									# Mimic fairseq token-to-id alignment for the first 4 token
									lowercase_	:   List[Any]  				=     {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

									lowercase_	:   Any  				=     len(self.sp_model  )
									lowercase_	:   Union[str, Any]  				=     {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words  )}
									self.fairseq_tokens_to_ids.update(snake_case__  )

									lowercase_	:   List[Any]  				=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def __getstate__(							self  )       -> Any:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.__dict__.copy()
									lowercase_	:   Optional[int]  				=     None
									lowercase_	:   Union[str, Any]  				=     self.sp_model.serialized_model_proto()
									return state






		def __setstate__(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     d

									# for backward compatibility
									if not hasattr(self,   """sp_model_kwargs"""  ):
																lowercase_	:   Tuple  				=     {}

									lowercase_	:   Tuple  				=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> List[int]:






									"""simple docstring"""

									if token_ids_a is None:
																return [self.sep_token_id] + token_ids_a
									lowercase_	:   str  				=     [self.sep_token_id]
									return sep + token_ids_a + sep + sep + token_ids_a






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = False  )       -> List[int]:






									"""simple docstring"""

									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=snake_case__,   token_ids_a=snake_case__,   already_has_special_tokens=snake_case__  )

									if token_ids_a is None:
																return [1] + ([0] * len(snake_case__  ))
									return [1] + ([0] * len(snake_case__  )) + [1, 1] + ([0] * len(snake_case__  ))






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> List[int]:






									"""simple docstring"""
									lowercase_	:   str  				=     [self.sep_token_id]

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






		@property
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									return len(self.sp_model  ) + self.fairseq_offset + self.num_madeup_words






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_	:   Dict  				=     {self.convert_ids_to_tokens(snake_case__  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab






		def 						snake_case__				(							self,   snake_case__  )       -> List[str]:






									"""simple docstring"""
									return self.sp_model.encode(snake_case__,   out_type=snake_case__  )






		def 						snake_case__				(							self,   snake_case__  )       -> Union[str, Any]:






									"""simple docstring"""
									if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
									lowercase_	:   str  				=     self.sp_model.PieceToId(snake_case__  )

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






		def 						snake_case__				(							self,   snake_case__  )       -> Tuple:






									"""simple docstring"""
									if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset  )






		def 						snake_case__				(							self,   snake_case__  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   Dict  				=     """""".join(snake_case__  ).replace(snake_case__,   """ """  ).strip()
									return out_string






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> Tuple[str]:






									"""simple docstring"""
									if not os.path.isdir(snake_case__  ):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
																return
									lowercase_	:   Union[str, Any]  				=     os.path.join(
									    snake_case__,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case__  ) and os.path.isfile(self.vocab_file  ):
																copyfile(self.vocab_file,   snake_case__  )
									elif not os.path.isfile(self.vocab_file  ):
																with open(snake_case__,   """wb"""  ) as fi:
																							lowercase_	:   List[str]  				=     self.sp_model.serialized_model_proto()
																							fi.write(snake_case__  )

									return (out_vocab_file,)






from string import ascii_uppercase

UpperCAmelCase_							=				{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_							=				dict(enumerate(ascii_uppercase))





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Dict  				=     len(lowercase				)
							lowercase_	:   Tuple  				=     0
							while True:
														if x == i:
																					lowercase_	:   Dict  				=     0
														if len(lowercase				) == len(lowercase				):
																					break
														key += key[i]
														i += 1
							return key





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Any  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in message:
														if letter == " ":
																					cipher_text += " "
														else:
																					lowercase_	:   Any  				=     (dicta[letter] - dicta[key_new[i]]) % 26
																					i += 1
																					cipher_text += dicta[x]
							return cipher_text





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in cipher_text:
														if letter == " ":
																					or_txt += " "
														else:
																					lowercase_	:   Optional[int]  				=     (dicta[letter] + dicta[key_new[i]] + 26) % 26
																					i += 1
																					or_txt += dicta[x]
							return or_txt





def  __magic_name__  (				)				-> None:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     """THE GERMAN ATTACK"""
							lowercase_	:   str  				=     """SECRET"""
							lowercase_	:   int  				=     generate_key(lowercase				,       lowercase				)
							lowercase_	:   List[str]  				=     cipher_text(lowercase				,       lowercase				)
							print(f"""Encrypted Text = {s}"""				)
							print(f"""Original Text = {original_text(lowercase				,       lowercase				)}"""				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()