





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


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_file""": """tokenizer.json"""}

snake_case								=							{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

snake_case								=							{
    """gpt2""": 1_0_2_4,
    """gpt2-medium""": 1_0_2_4,
    """gpt2-large""": 1_0_2_4,
    """gpt2-xl""": 1_0_2_4,
    """distilgpt2""": 1_0_2_4,
}





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_   :			Tuple  			=	PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_   :			Union[str, Any]  			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_   :			Dict  			=	['''input_ids''', '''attention_mask''']
       SCREAMING_SNAKE_CASE_   :			Any  			=	GPTaTokenizer







       def __init__(     self						:						int    ,__A						:						Optional[int]=None    ,__A						:						Optional[int]=None    ,__A						:						List[str]=None    ,__A						:						Any="<|endoftext|>"    ,__A						:						int="<|endoftext|>"    ,__A						:						List[str]="<|endoftext|>"    ,__A						:						str=False    ,**__A						:						Union[str, Any]    ,)    ->       int:
         super().__init__(
             __A    ,__A    ,tokenizer_file=__A    ,unk_token=__A    ,bos_token=__A    ,eos_token=__A    ,add_prefix_space=__A    ,**__A    ,)

         _lowercase						    =			kwargs.pop('add_bos_token'    ,__A	)

         _lowercase						    =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
         if pre_tok_state.get('add_prefix_space'    ,__A	) != add_prefix_space:
           _lowercase						    =			getattr(__A    ,pre_tok_state.pop('type'	)	)
           _lowercase						    =			add_prefix_space
           _lowercase						    =			pre_tok_class(**__A	)

         _lowercase						    =			add_prefix_space







       def  __UpperCAmelCase			(     self						:						Any    ,*__A						:						List[Any]    ,**__A						:						Tuple	)    ->       BatchEncoding:
         _lowercase						    =			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  __UpperCAmelCase			(     self						:						int    ,*__A						:						Any    ,**__A						:						Any	)    ->       BatchEncoding:
         _lowercase						    =			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  __UpperCAmelCase			(     self						:						int    ,__A						:						str    ,__A						:						Optional[str] = None	)    ->       Tuple[str]:
         _lowercase						    =			self._tokenizer.model.save(__A    ,name=__A	)
         return tuple(__A	)







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						"Conversation"	)    ->       List[int]:
         _lowercase						    =			[]
         for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(__A    ,add_special_tokens=__A	) + [self.eos_token_id]	)

         if len(__A	) > self.model_max_length:
           _lowercase						    =			input_ids[-self.model_max_length :]
         return input_ids







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np


def 				lowercase_					(     __A			:							dict       ,   __A			:							str       ,   __A			:							set       ,   __A			:							set       ,   __A			:							dict       ,   __A			:							dict       ,   __A			:							PriorityQueue       ,   __A			:							dict       ,   __A			:							float | int       ,   )							->   float | int:



 """simple docstring"""




 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  lowercase       : str						=cst_fwd.get(__A       ,   np.inf      )
  lowercase       : str						=cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)      )
   lowercase       : int						=new_cost_f
   lowercase       : Optional[int]						=v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    lowercase       : List[Any]						=cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance


def 				lowercase_					(     __A			:							str       ,   __A			:							str       ,   __A			:							dict       ,   __A			:							dict      )							->   int:



 """simple docstring"""




 lowercase       : Optional[Any]						=-1

 lowercase       : Tuple						=set()
 lowercase       : str						=set()
 lowercase       : List[str]						={source: 0}
 lowercase       : List[str]						={destination: 0}
 lowercase       : Tuple						={source: None}
 lowercase       : List[Any]						={destination: None}
 lowercase       : PriorityQueue[Any]						=PriorityQueue()
 lowercase       : PriorityQueue[Any]						=PriorityQueue()

 lowercase       : Any						=np.inf

 queue_forward.put((0, source)      )
 queue_backward.put((0, destination)      )

 if source == destination:
  return 0

 while not queue_forward.empty() and not queue_backward.empty():
  lowercase	,		lowercase       : int						=queue_forward.get()
  visited_forward.add(__A      )

  lowercase	,		lowercase       : int						=queue_backward.get()
  visited_backward.add(__A      )

  lowercase       : Dict						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  lowercase       : List[Any]						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
   break

 if shortest_distance != np.inf:
  lowercase       : Union[str, Any]						=shortest_distance
 return shortest_path_distance


SCREAMING_SNAKE_CASE							=						{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
SCREAMING_SNAKE_CASE							=						{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()



