




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


__SCREAMING_SNAKE_CASE 						="""▁"""

__SCREAMING_SNAKE_CASE 						={"""vocab_file""": """spiece.model"""}

__SCREAMING_SNAKE_CASE 						={
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

__SCREAMING_SNAKE_CASE 						={
    """google/pegasus-xsum""": 512,
}


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					VOCAB_FILES_NAMES

				SCREAMING_SNAKE_CASE__					:							List[str]	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							int	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["input_ids", "attention_mask"]


				def __init__(							self:   Tuple		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   str="<pad>"		,					_lowerCamelCase:   int="</s>"		,					_lowerCamelCase:   Tuple="<unk>"		,					_lowerCamelCase:   List[Any]="<mask_2>"		,					_lowerCamelCase:   Union[str, Any]="<mask_1>"		,					_lowerCamelCase:   List[Any]=None		,					_lowerCamelCase:   int=1_03		,					_lowerCamelCase:   Optional[Dict[str, Any]] = None		,					**_lowerCamelCase:   List[str]		,					):
											SCREAMING_SNAKE_CASE_   	=    offset
											if additional_special_tokens is not None:
																		if not isinstance(_lowerCamelCase		,					_lowerCamelCase		):
																									raise TypeError(
																									    f"additional_special_tokens should be of type {type(_lowerCamelCase		)}, but is"
																									    f" {type(_lowerCamelCase		)}"		)

																		SCREAMING_SNAKE_CASE_   	=    (
																		    ([mask_token_sent] + additional_special_tokens)
																		    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																		    else additional_special_tokens
																		)
																		# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																		additional_special_tokens_extended += [
																		    f"<unk_{i}>" for i in range(len(_lowerCamelCase		)		,					self.offset - 1		)
																		]

																		if len(set(_lowerCamelCase		)		) != len(_lowerCamelCase		):
																									raise ValueError(
																									    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																									    f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."		)
																		SCREAMING_SNAKE_CASE_   	=    additional_special_tokens_extended
											else:
																		SCREAMING_SNAKE_CASE_   	=    [mask_token_sent] if mask_token_sent is not None else []
																		additional_special_tokens += [f"<unk_{i}>" for i in range(2		,					self.offset		)]

											SCREAMING_SNAKE_CASE_   	=    {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    eos_token=_lowerCamelCase		,					unk_token=_lowerCamelCase		,					mask_token=_lowerCamelCase		,					pad_token=_lowerCamelCase		,					mask_token_sent=_lowerCamelCase		,					offset=_lowerCamelCase		,					additional_special_tokens=_lowerCamelCase		,					sp_model_kwargs=self.sp_model_kwargs		,					**_lowerCamelCase		,					)
											SCREAMING_SNAKE_CASE_   	=    mask_token_sent
											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(_lowerCamelCase		)

											# add special tokens to encoder dict
											SCREAMING_SNAKE_CASE_   	=    {
											    0: self.pad_token,
											    1: self.eos_token,
											}

											if self.mask_token_sent is not None:
																		self.encoder.update(
																		    {
																		        2: self.mask_token_sent,
																		        3: self.mask_token,
																		    }		)

											if self.offset > 0:
																		# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																		# mask_token_sent is already added to list -> so start at 1
																		self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1		,					self.offset - 1		)}		)

											SCREAMING_SNAKE_CASE_   	=    {v: k for k, v in self.encoder.items()}


				@property
				def     _A							(							self:   Tuple		):
											return len(self.sp_model		) + self.offset


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    {self.convert_ids_to_tokens(_lowerCamelCase		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


				def __getstate__(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.__dict__.copy()
											SCREAMING_SNAKE_CASE_   	=    None
											return state


				def __setstate__(							self:   str		,					_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    d

											# for backward compatibility
											if not hasattr(self		,					'''sp_model_kwargs'''		):
																		SCREAMING_SNAKE_CASE_   	=    {}

											SCREAMING_SNAKE_CASE_   	=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   str		):
											return self.sp_model.encode(_lowerCamelCase		,					out_type=_lowerCamelCase		)


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   str		):
											if token in self.decoder:
																		return self.decoder[token]
											elif token in self.added_tokens_decoder:
																		return self.added_tokens_decoder[token]
											SCREAMING_SNAKE_CASE_   	=    self.sp_model.piece_to_id(_lowerCamelCase		)
											return sp_id + self.offset


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   int		):
											if index in self.encoder:
																		return self.encoder[index]
											elif index in self.added_tokens_encoder:
																		return self.added_tokens_encoder[index]
											else:
																		SCREAMING_SNAKE_CASE_   	=    self.sp_model.IdToPiece(index - self.offset		)
											return token


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    []
											SCREAMING_SNAKE_CASE_   	=    ''''''
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									out_string += self.sp_model.decode(_lowerCamelCase		) + token
																									SCREAMING_SNAKE_CASE_   	=    []
																		else:
																									current_sub_tokens.append(_lowerCamelCase		)
											out_string += self.sp_model.decode(_lowerCamelCase		)
											return out_string.strip()


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   List[Any]=False		):
											return 1


				def     _A							(							self:   Any		,					_lowerCamelCase:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    set(self.all_special_ids		)  # call it once instead of inside list comp
											all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

											return [1 if x in all_special_ids else 0 for x in seq]


				def     _A							(							self:   int		,					_lowerCamelCase:   List		,					_lowerCamelCase:   Optional[List] = None		,					_lowerCamelCase:   bool = False		):
											if already_has_special_tokens:
																		return self._special_token_mask(_lowerCamelCase		)
											elif token_ids_a is None:
																		return self._special_token_mask(_lowerCamelCase		) + [1]
											else:
																		return self._special_token_mask(token_ids_a + token_ids_a		) + [1]


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Tuple=None		):
											if token_ids_a is None:
																		return token_ids_a + [self.eos_token_id]
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return token_ids_a + token_ids_a + [self.eos_token_id]





				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[str] = None		):
											if not os.path.isdir(_lowerCamelCase		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																		return
											SCREAMING_SNAKE_CASE_   	=    os.path.join(
											    _lowerCamelCase		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCamelCase		) and os.path.isfile(self.vocab_file		):
																		copyfile(self.vocab_file		,					_lowerCamelCase		)
											elif not os.path.isfile(self.vocab_file		):
																		with open(_lowerCamelCase		,					'''wb'''		) as fi:
																									SCREAMING_SNAKE_CASE_   	=    self.sp_model.serialized_model_proto()
																									fi.write(_lowerCamelCase		)

											return (out_vocab_file,)








from __future__ import annotations

__SCREAMING_SNAKE_CASE 						={
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   List[Any]		,					_lowerCamelCase:   dict[str, list[str]]		,					_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    graph
											# mapping node to its parent in resulting breadth first tree
											SCREAMING_SNAKE_CASE_   	=    {}
											SCREAMING_SNAKE_CASE_   	=    source_vertex


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    {self.source_vertex}
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    [self.source_vertex]  # first in first out queue

											while queue:
																		SCREAMING_SNAKE_CASE_   	=    queue.pop(0		)
																		for adjacent_vertex in self.graph[vertex]:
																									if adjacent_vertex not in visited:
																																visited.add(_lowerCamelCase		)
																																SCREAMING_SNAKE_CASE_   	=    vertex
																																queue.append(_lowerCamelCase		)





				def     _A							(							self:   List[str]		,					_lowerCamelCase:   str		):
											if target_vertex == self.source_vertex:
																		return self.source_vertex

											SCREAMING_SNAKE_CASE_   	=    self.parent.get(_lowerCamelCase		)
											if target_vertex_parent is None:
																		SCREAMING_SNAKE_CASE_   	=    (
																		    f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
																		)
																		raise ValueError(_lowerCamelCase		)

											return self.shortest_path(_lowerCamelCase		) + f"->{target_vertex}"


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


