



import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)


UpperCAmelCase_							:      Tuple      =  {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_							:      str      =  {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

UpperCAmelCase_							:      int      =  {"facebook/blenderbot-3B": 128}






class      UpperCamelCase    (						lowerCamelCase_     ):
	lowerCAmelCase	:   int				=	VOCAB_FILES_NAMES
	lowerCAmelCase	:   List[Any]				=	PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase	:   Tuple				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase	:   Tuple				=	["""input_ids""", """attention_mask"""]
	lowerCAmelCase	:   str				=	BlenderbotTokenizer



	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__			,	):
							super().__init__(
							    lowerCAmelCase__			,	lowerCAmelCase__			,	tokenizer_file=lowerCAmelCase__			,	errors=lowerCAmelCase__			,	bos_token=lowerCAmelCase__			,	eos_token=lowerCAmelCase__			,	sep_token=lowerCAmelCase__			,	cls_token=lowerCAmelCase__			,	unk_token=lowerCAmelCase__			,	pad_token=lowerCAmelCase__			,	mask_token=lowerCAmelCase__			,	add_prefix_space=lowerCAmelCase__			,	trim_offsets=lowerCAmelCase__			,	**lowerCAmelCase__			,	)

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

							A__							=       add_prefix_space

							A__							=       "post_processor"
							A__							=       getattr(self.backend_tokenizer			,	lowerCAmelCase__			,	lowerCAmelCase__		)
							if tokenizer_component_instance:
													A__							=       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:
																			A__							=       tuple(state["sep"]		)
													if "cls" in state:
																			A__							=       tuple(state["cls"]		)

													A__							=       False

													if state.get("add_prefix_space"			,	lowerCAmelCase__		) != add_prefix_space:
																			A__							=       add_prefix_space
																			A__							=       True

													if state.get("trim_offsets"			,	lowerCAmelCase__		) != trim_offsets:
																			A__							=       trim_offsets
																			A__							=       True

													if changes_to_apply:
																			A__							=       getattr(lowerCAmelCase__			,	state.pop("type"		)		)
																			A__							=       component_class(**lowerCAmelCase__		)
																			setattr(self.backend_tokenizer			,	lowerCAmelCase__			,	lowerCAmelCase__		)



	@property
	# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
	def 			__A							(       self		):
							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__		):
							A__							=       AddedToken(lowerCAmelCase__			,	lstrip=lowerCAmelCase__			,	rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__			,	lowerCAmelCase__		) else value
							A__							=       value



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							A__							=       kwargs.get("is_split_into_words"			,	lowerCAmelCase__		)
							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(*lowerCAmelCase__			,	**lowerCAmelCase__		)



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							A__							=       kwargs.get("is_split_into_words"			,	lowerCAmelCase__		)

							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(*lowerCAmelCase__			,	**lowerCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       self._tokenizer.model.save(lowerCAmelCase__			,	name=lowerCAmelCase__		)
							return tuple(lowerCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       [self.sep_token_id]
							A__							=       [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]



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							return token_ids_a + [self.eos_token_id]



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       []
							for is_user, text in conversation.iter_texts():
													if is_user:
																			# We need to space prefix as it's being done within blenderbot
																			inputs.append(" " + text		)
													else:
																			# Generated responses should contain them already.
																			inputs.append(lowerCAmelCase__		)

							A__							=       "  ".join(lowerCAmelCase__		)
							A__							=       self.encode(lowerCAmelCase__		)
							if len(lowerCAmelCase__		) > self.model_max_length:
													A__							=       input_ids[-self.model_max_length :]
													logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""		)
							return input_ids










def  UpperCamelCase						(       _A			:				str    )->       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  UpperCamelCase						(       _A			:				str    )->       dict[str, 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  UpperCamelCase						(       _A			:				str			,							_A			:				dict[str, str]    )->       str:





						"""simple docstring"""





						return "".join(cipher_map.get(_A			,							_A    ) for ch in message.upper()    )
def  UpperCamelCase						(       _A			:				str			,							_A			:				dict[str, 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  UpperCamelCase						(       )->       None:





						"""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()





