


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

snake_case__			 =						logging.get_logger(__name__)


snake_case__			 =						{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

snake_case__			 =						{
    '''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'''
    },
}

snake_case__			 =						{'''facebook/blenderbot-3B''': 128}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     VOCAB_FILES_NAMES
						lowerCamelCase_         =     PRETRAINED_VOCAB_FILES_MAP
						lowerCamelCase_         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCamelCase_         =     ['input_ids', 'attention_mask']
						lowerCamelCase_         =     BlenderbotTokenizer




						def __init__(	self						:   List[Any]   ,     __A						:   Union[str, Any]=None   ,     __A						:   List[Any]=None   ,     __A						:   Optional[Any]=None   ,     __A						:   List[str]="replace"   ,     __A						:   Dict="<s>"   ,     __A						:   Any="</s>"   ,     __A						:   Dict="</s>"   ,     __A						:   int="<s>"   ,     __A						:   str="<unk>"   ,     __A						:   Union[str, Any]="<pad>"   ,     __A						:   List[str]="<mask>"   ,     __A						:   List[Any]=False   ,     __A						:   Optional[Any]=True   ,     **__A						:   List[Any]   ,     )      ->Tuple:






									"""simple docstring"""






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

									a__ :Dict						  =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
									if pre_tok_state.get("add_prefix_space"   ,     __A						) != add_prefix_space:
												a__ :Optional[Any]						  =    getattr(__A   ,     pre_tok_state.pop("type"						)						)
												a__ :List[str]						  =    add_prefix_space
												a__ :Tuple						  =    pre_tok_class(**__A						)

									a__ :Dict						  =    add_prefix_space

									a__ :Optional[Any]						  =    "post_processor"
									a__ :List[str]						  =    getattr(self.backend_tokenizer   ,     __A   ,     __A						)
									if tokenizer_component_instance:
												a__ :Tuple						  =    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__ :Optional[Any]						  =    tuple(state["sep"]						)
												if "cls" in state:
															a__ :List[Any]						  =    tuple(state["cls"]						)

												a__ :Tuple						  =    False

												if state.get("add_prefix_space"   ,     __A						) != add_prefix_space:
															a__ :int						  =    add_prefix_space
															a__ :Dict						  =    True

												if state.get("trim_offsets"   ,     __A						) != trim_offsets:
															a__ :List[str]						  =    trim_offsets
															a__ :int						  =    True

												if changes_to_apply:
															a__ :int						  =    getattr(__A   ,     state.pop("type"						)						)
															a__ :Dict						  =    component_class(**__A						)
															setattr(self.backend_tokenizer   ,     __A   ,     __A						)




						@property
						# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
						def  _snake_case					(	self						:   Any						)      ->str:






									"""simple docstring"""






									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  _snake_case					(	self						:   int   ,     __A						:   List[Any]						)      ->Tuple:






									"""simple docstring"""






									a__ :Any						  =    AddedToken(__A   ,     lstrip=__A   ,     rstrip=__A						) if isinstance(__A   ,     __A						) else value
									a__ :Optional[int]						  =    value




						def  _snake_case					(	self						:   Optional[Any]   ,     *__A						:   List[Any]   ,     **__A						:   Dict						)      ->BatchEncoding:






									"""simple docstring"""






									a__ :Optional[int]						  =    kwargs.get("is_split_into_words"   ,     __A						)
									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(*__A   ,     **__A						)




						def  _snake_case					(	self						:   Tuple   ,     *__A						:   Union[str, Any]   ,     **__A						:   str						)      ->BatchEncoding:






									"""simple docstring"""






									a__ :Optional[Any]						  =    kwargs.get("is_split_into_words"   ,     __A						)

									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(*__A   ,     **__A						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   str   ,     __A						:   Optional[str] = None						)      ->Tuple[str]:






									"""simple docstring"""






									a__ :List[str]						  =    self._tokenizer.model.save(__A   ,     name=__A						)
									return tuple(__A						)




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   List[int]   ,     __A						:   Optional[List[int]] = None						)      ->List[int]:






									"""simple docstring"""






									a__ :Any						  =    [self.sep_token_id]
									a__ :List[str]						  =    [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  _snake_case					(	self						:   Tuple   ,     __A						:   List[int]   ,     __A						:   Optional[List[int]] = None						)      ->Tuple:






									"""simple docstring"""






									return token_ids_a + [self.eos_token_id]






						def  _snake_case					(	self						:   Tuple   ,     __A						:   "Conversation"						)      ->List[int]:






									"""simple docstring"""






									a__ :str						  =    []
									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(__A						)

									a__ :List[Any]						  =    "  ".join(__A						)
									a__ :Dict						  =    self.encode(__A						)
									if len(__A						) > self.model_max_length:
												a__ :Any						  =    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






class 					lowerCAmelCase_ :




						def __init__(	self						:   Optional[int]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    {}




						def  _snake_case					(	self						:   Tuple						)      ->None:






									"""simple docstring"""






									print(self.vertex						)
									for i in self.vertex:
												print(__A   ,     " -> "   ,     " -> ".join([str(__A						) for j in self.vertex[i]]						)						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   int   ,     __A						:   int						)      ->None:






									"""simple docstring"""






									if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__A						)
									else:
												# else make a new vertex
												a__ :Any						  =    [to_vertex]




						def  _snake_case					(	self						:   Optional[int]						)      ->None:






									"""simple docstring"""






									a__ :List[Any]						  =    [False] * len(self.vertex						)

									# call the recursive helper function
									for i in range(len(self.vertex						)						):
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)






						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   int   ,     __A						:   list						)      ->None:






									"""simple docstring"""






									a__ :Dict						  =    True

									print(__A   ,     end=" "						)

									# Recur for all the vertices that are adjacent to this node
									for i in self.vertex:
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)


if __name__ == "__main__":
		snake_case__			 =						Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('''DFS:''')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3


