



import json
from typing import 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_longformer import LongformerTokenizer


snake_case     :    Union[str, Any]						=    logging.get_logger(__name__)

snake_case     :    Any						=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case     :    int						=    {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

snake_case     :    Union[str, Any]						=    {
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}




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







					def __init__(							self   :  int      ,		_A   :  Union[str, Any]=None      ,		_A   :  List[Any]=None      ,		_A   :  Optional[int]=None      ,		_A   :  int="replace"      ,		_A   :  List[Any]="<s>"      ,		_A   :  Dict="</s>"      ,		_A   :  Union[str, Any]="</s>"      ,		_A   :  Tuple="<s>"      ,		_A   :  Tuple="<unk>"      ,		_A   :  Dict="<pad>"      ,		_A   :  List[Any]="<mask>"      ,		_A   :  Union[str, Any]=False      ,		_A   :  str=True      ,		**_A   :  Tuple      ,		):
											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__						:				Optional[int]         =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
											if pre_tok_state.get("add_prefix_space"      ,		_A) != add_prefix_space:
																	A__						:				Union[str, Any]         =			getattr(_A      ,		pre_tok_state.pop("type"))
																	A__						:				List[str]         =			add_prefix_space
																	A__						:				List[Any]         =			pre_tok_class(**_A)

											A__						:				Union[str, Any]         =			add_prefix_space

											A__						:				str         =			"""post_processor"""
											A__						:				Tuple         =			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__						:				str         =			tuple(state["sep"])
																	if "cls" in state:
																							A__						:				List[str]         =			tuple(state["cls"])

																	A__						:				List[Any]         =			False

																	if state.get("add_prefix_space"      ,		_A) != add_prefix_space:
																							A__						:				Any         =			add_prefix_space
																							A__						:				Dict         =			True

																	if state.get("trim_offsets"      ,		_A) != trim_offsets:
																							A__						:				List[str]         =			trim_offsets
																							A__						:				Any         =			True

																	if changes_to_apply:
																							A__						:				Union[str, Any]         =			getattr(_A      ,		state.pop("type"))
																							A__						:				List[Any]         =			component_class(**_A)
																							setattr(self.backend_tokenizer      ,		_A      ,		_A)







					@property
					def 			_lowercase      (							self   :  int):





											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 			_lowercase      (							self   :  List[Any]      ,		_A   :  List[str]):
											A__						:				Optional[Any]         =			AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A) if isinstance(_A      ,		_A) else value
											A__						:				Optional[Any]         =			value







					def 			_lowercase      (							self   :  int      ,		*_A   :  Dict      ,		**_A   :  Tuple):
											A__						:				List[str]         =			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 			_lowercase      (							self   :  Tuple      ,		*_A   :  str      ,		**_A   :  Tuple):
											A__						:				Tuple         =			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 			_lowercase      (							self   :  List[str]      ,		_A   :  str      ,		_A   :  Optional[str] = None):
											A__						:				int         =			self._tokenizer.model.save(_A      ,		name=_A)
											return tuple(_A)







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  List[str]      ,		_A   :  List[Any]=None):
											A__						:				str         =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																	return output

											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







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

'''simple docstring'''
import heapq







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Any:





  """simple docstring"""

  __A		:  Dict       				=		[(0, start)]  # cost from start node,end node
  __A		:  Union[str, Any]       				=		set()
  while heap:
    ((__A)				,       (__A))		:  int       				=		heapq.heappop(__SCREAMING_SNAKE_CASE			)
    if u in visited:
      continue
    visited.add(__SCREAMING_SNAKE_CASE			)
    if u == end:
      return cost
    for v, c in graph[u]:
      if v in visited:
        continue
      __A		:  List[str]       				=		cost + c
      heapq.heappush(__SCREAMING_SNAKE_CASE      ,  (next_item, v)			)
  return -1


A__ :		Any					={
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
A__ :		List[str]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
A__ :		List[Any]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

A__ :		List[str]					=dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A__ :		Optional[Any]					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

A__ :		Any					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
     import doctest

     doctest.testmod()



