






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case    :						int  =						logging.get_logger(__name__)

snake_case    :						List[str]  =						{'vocab_file': 'sentencepiece.bpe.model'}

snake_case    :						Dict  =						{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

snake_case    :						List[Any]  =						{
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

snake_case    :						str  =						'▁'




class 							lowerCAmelCase__  (	__lowerCamelCase						):
					__A     :    str           =						VOCAB_FILES_NAMES
					__A     :    Tuple           =						PRETRAINED_VOCAB_FILES_MAP
					__A     :    Optional[Any]           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A     :    List[str]           =						['''input_ids''', '''attention_mask''']







					def __init__(       self      :     Any			,		_A      :     Any			,		_A      :     Any="<s>"			,		_A      :     Dict="</s>"			,		_A      :     List[str]="</s>"			,		_A      :     Optional[Any]="<s>"			,		_A      :     str="<unk>"			,		_A      :     Union[str, Any]="<pad>"			,		_A      :     int="<mask>"			,		_A      :     str = None			,		**_A      :     List[Any]			,		):
											# Mask token behave like a normal word, i.e. include the space before it
											A__	:							str						    = AddedToken(a_			,		lstrip=a_			,		rstrip=a_) if isinstance(a_			,		a_) else mask_token

											A__	:							str						    = {} if sp_model_kwargs is None else sp_model_kwargs

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

											A__	:							List[Any]						    = vocab_file
											A__	:							Union[str, Any]						    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(str(a_))

											A__	:							Union[str, Any]						    = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

											A__	:							List[Any]						    = len(self.sp_model) - 1
											A__	:							Union[str, Any]						    = {v: k for k, v in self.fairseq_tokens_to_ids.items()}







					def  _lowercase			(       self      :     int			,		_A      :     Tuple			,		_A      :     Optional[int] = None):

											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											A__	:							Any						    = [self.cls_token_id]
											A__	:							List[str]						    = [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep







					def  _lowercase			(       self      :     Tuple			,		_A      :     Dict			,		_A      :     Any = None			,		_A      :     int = False):
											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=a_			,		token_ids_a=a_			,		already_has_special_tokens=a_)

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







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     str			,		_A      :     Union[str, Any] = None):
											A__	:							List[Any]						    = [self.sep_token_id]
											A__	:							Optional[Any]						    = [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]







					@property
					def  _lowercase			(       self      :     Dict):
											return len(self.sp_model)







					def  _lowercase			(       self      :     Any):
											A__	:							int						    = {self.convert_ids_to_tokens(a_): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab







					def  _lowercase			(       self      :     Tuple			,		_A      :     Optional[Any]):
											return self.sp_model.encode(a_			,		out_type=a_)







					def  _lowercase			(       self      :     List[str]			,		_A      :     Dict):
											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											A__	:							List[Any]						    = self.sp_model.PieceToId(a_)

											return spm_id if spm_id else self.unk_token_id







					def  _lowercase			(       self      :     Any			,		_A      :     int):
											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(a_)







					def  _lowercase			(       self      :     List[Any]			,		_A      :     Tuple):
											A__	:							Any						    = []
											A__	:							str						    = ""
											A__	:							Dict						    = False
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							if not prev_is_special:
																													out_string += " "
																							out_string += self.sp_model.decode(a_) + token
																							A__	:							Dict						    = True
																							A__	:							Any						    = []
																	else:
																							current_sub_tokens.append(a_)
																							A__	:							Optional[int]						    = False
											out_string += self.sp_model.decode(a_)
											return out_string.strip()







					def __getstate__(       self      :     Optional[Any]):
											A__	:							Union[str, Any]						    = self.__dict__.copy()
											A__	:							Dict						    = None
											return state







					def __setstate__(       self      :     Dict			,		_A      :     int):
											A__	:							Dict						    = d

											# for backward compatibility
											if not hasattr(self			,		"sp_model_kwargs"):
																	A__	:							List[str]						    = {}

											A__	:							List[Any]						    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(self.vocab_file)







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     Dict			,		_A      :     Tuple = None):
											if not os.path.isdir(a_):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory')
																	return
											A__	:							Optional[int]						    = os.path.join(
											    a_			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

											if os.path.abspath(self.vocab_file) != os.path.abspath(a_) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file			,		a_)
											elif not os.path.isfile(self.vocab_file):
																	with open(a_			,		"wb") as fi:
																							A__	:							Optional[int]						    = self.sp_model.serialized_model_proto()
																							fi.write(a_)

											return (out_vocab_file,)




from __future__ import annotations

snake_case    :						Optional[Any]  =						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}




class 							lowerCAmelCase__  :







					def __init__(       self      :     Optional[Any]			,		_A      :     dict[str, list[str]]			,		_A      :     str):
											A__	:							Optional[Any]						    = graph
											# mapping node to its parent in resulting breadth first tree
											A__	:							dict[str, str | None]						    = {}
											A__	:							List[str]						    = source_vertex







					def  _lowercase			(       self      :     List[Any]):
											A__	:							str						    = {self.source_vertex}
											A__	:							List[str]						    = None
											A__	:							List[str]						    = [self.source_vertex]  # first in first out queue

											while queue:
																	A__	:							int						    = queue.pop(0)
																	for adjacent_vertex in self.graph[vertex]:
																							if adjacent_vertex not in visited:
																													visited.add(_A)
																													A__	:							Any						    = vertex
																													queue.append(_A)







					def  _lowercase			(       self      :     int			,		_A      :     str):
											if target_vertex == self.source_vertex:
																	return self.source_vertex

											A__	:							List[Any]						    = self.parent.get(_A)
											if target_vertex_parent is None:
																	A__	:							Union[str, Any]						    = (
																	    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																	)
																	raise ValueError(_A)

											return self.shortest_path(_A) + F'->{target_vertex}'


if __name__ == "__main__":
		snake_case    :						Any  =						Graph(graph, 'G')
		g.breath_first_search()
		print(g.shortest_path('D'))
		print(g.shortest_path('G'))
		print(g.shortest_path('Foo'))