





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


__UpperCamelCase:	List[Any] = logging.get_logger(__name__)

__UpperCamelCase:	Optional[int] = {"""vocab_file""": """sentencepiece.bpe.model"""}

__UpperCamelCase:	str = {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

__UpperCamelCase:	Optional[int] = {
    """camembert-base""": 5_1_2,
}

__UpperCamelCase:	int = """▁"""


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				VOCAB_FILES_NAMES
	_A													=				PRETRAINED_VOCAB_FILES_MAP
	_A													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A													=				["input_ids", "attention_mask"]

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

			lowercase__					:      List[Any]      =		{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=lowerCamelCase_,					eos_token=lowerCamelCase_,					unk_token=lowerCamelCase_,					sep_token=lowerCamelCase_,					cls_token=lowerCamelCase_,					pad_token=lowerCamelCase_,					mask_token=lowerCamelCase_,					additional_special_tokens=lowerCamelCase_,					sp_model_kwargs=self.sp_model_kwargs,					**lowerCamelCase_,					)
			lowercase__					:      Union[str, Any]      =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
			self.sp_model.Load(str(lowerCamelCase_			)			)
			lowercase__					:      Tuple      =		vocab_file
			# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
			# sentencepiece vocabulary (this is the case for <s> and </s>
			lowercase__					:      Dict      =		{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
			lowercase__					:      Tuple      =		len(self.fairseq_tokens_to_ids			)
			lowercase__					:      List[str]      =		len(self.sp_model			) + len(self.fairseq_tokens_to_ids			)
			lowercase__					:      Tuple      =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						List[int],					lowerCamelCase_:						Optional[List[int]] = None			):

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

	def    snake_case__(						self:						Dict,					lowerCamelCase_:						List[int],					lowerCamelCase_:						Optional[List[int]] = None,					lowerCamelCase_:						bool = False			):
			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=lowerCamelCase_,					token_ids_a=lowerCamelCase_,					already_has_special_tokens=lowerCamelCase_			)

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

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						List[int],					lowerCamelCase_:						Optional[List[int]] = None			):
			lowercase__					:      Any      =		[self.sep_token_id]
			lowercase__					:      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    snake_case__(						self:						Tuple			):
			return len(self.fairseq_tokens_to_ids			) + len(self.sp_model			)

	def    snake_case__(						self:						int			):
			lowercase__					:      Dict      =		{self.convert_ids_to_tokens(lowerCamelCase_			): i for i in range(self.vocab_size			)}
			vocab.update(self.added_tokens_encoder			)
			return vocab

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

	def    snake_case__(						self:						Dict,					lowerCamelCase_:						int			):
			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			elif self.sp_model.PieceToId(lowerCamelCase_			) == 0:
					# Convert sentence piece unk token to fairseq unk token index
					return self.unk_token_id
			return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase_			)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						Optional[int]			):
			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:						Optional[int],					lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      Optional[Any]      =		[]
			lowercase__					:      List[str]      =		''
			lowercase__					:      str      =		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(lowerCamelCase_			) + token
							lowercase__					:      int      =		True
							lowercase__					:      Dict      =		[]
					else:
							current_sub_tokens.append(lowerCamelCase_			)
							lowercase__					:      str      =		False
			out_string += self.sp_model.decode(lowerCamelCase_			)
			return out_string.strip()

	def __getstate__(						self:						Tuple			):
			lowercase__					:      Union[str, Any]      =		self.__dict__.copy()
			lowercase__					:      Union[str, Any]      =		None
			return state

	def __setstate__(						self:						Optional[int],					lowerCamelCase_:						Dict			):
			lowercase__					:      Dict      =		d

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

			lowercase__					:      Dict      =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
			self.sp_model.Load(self.vocab_file			)





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						str,					lowerCamelCase_:						Optional[str] = None			):
			if not os.path.isdir(lowerCamelCase_			):
					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
					return
			lowercase__					:      List[Any]      =		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:
							lowercase__					:      Tuple      =		self.sp_model.serialized_model_proto()
							fi.write(lowerCamelCase_			)

			return (out_vocab_file,)









from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(graph[j][k] for k in ['src', 'dst', 'weight'])
				if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
						return True
		return False


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

			for i in range(E):
						print("""Edge """, i + 1)
						__UpperCamelCase,	__UpperCamelCase,	__UpperCamelCase:	List[str] = (
						    int(x)
						    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
						)
						__UpperCamelCase:	List[Any] = {"""src""": src, """dst""": dest, """weight""": weight}

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


