






'''simple docstring'''



import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =BlenderbotSmallTokenizer
      __a	       =False




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         super().setUp()

         __a					  =     ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
         __a					  =     dict(zip(lowerCamelCase			, range(len(lowerCamelCase   )   )   )   )

         __a					  =     ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
         __a					  =     {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

         __a					  =     os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES['vocab_file']   )
         __a					  =     os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES['merges_file']   )
         with open(self.vocab_file			, 'w'			, encoding='utf-8'   ) as fp:
            fp.write(json.dumps(lowerCamelCase   ) + '\n'   )
         with open(self.merges_file			, 'w'			, encoding='utf-8'   ) as fp:
            fp.write('\n'.join(lowerCamelCase   )   )




      def 			__UpperCamelCase				(	self			, **lowerCamelCase   )				->Optional[Any]:



         '''simple docstring'''
         kwargs.update(self.special_tokens_map   )
         return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			, **lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->int:



         '''simple docstring'''
         __a					  =     'adapt act apte'
         __a					  =     'adapt act apte'
         return input_text, output_text




      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     BlenderbotSmallTokenizer(self.vocab_file			, self.merges_file			, **self.special_tokens_map   )
         __a					  =     'adapt act apte'
         __a					  =     ['adapt', 'act', 'ap@@', 'te']
         __a					  =     tokenizer.tokenize(lowerCamelCase   )
         self.assertListEqual(lowerCamelCase			, lowerCamelCase   )

         __a					  =     [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

         __a					  =     [0, 1, 2, 3, 4, 5]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase   )			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
         assert tok('sam'   ).input_ids == [1384]
         __a					  =     'I am a small frog.'
         __a					  =     tok([src_text]			, padding=lowerCamelCase			, truncation=lowerCamelCase   )['input_ids']
         __a					  =     tok.batch_decode(lowerCamelCase			, skip_special_tokens=lowerCamelCase			, clean_up_tokenization_spaces=lowerCamelCase   )[0]
         assert src_text != decoded  # I wish it did!
         assert decoded == "i am a small frog ."






      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
         __a					  =     'I am a small frog .'
         __a					  =     '.'
         __a					  =     tok(lowerCamelCase   )['input_ids']
         __a					  =     tok(lowerCamelCase   )['input_ids']

         assert encoded[-1] == encoded_dot[0]







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						PriorityQueue,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						float | int,       )	->      float | int:

   """simple docstring"""
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      __a					  =     cst_fwd.get(SCREAMING_SNAKE_CASE__,       np.inf     )
      __a					  =     cst_fwd[v] + d
      if new_cost_f < old_cost_f:
         queue.put((new_cost_f, nxt)     )
         __a					  =     new_cost_f
         __a					  =     v
      if nxt in visited_backward:
         if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
            __a					  =     cst_fwd[v] + d + cst_bwd[nxt]
   return shortest_distance



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict     )	->      int:

   """simple docstring"""
   __a					  =     -1

   __a					  =     set()
   __a					  =     set()
   __a					  =     {source: 0}
   __a					  =     {destination: 0}
   __a					  =     {source: None}
   __a					  =     {destination: None}
   __a					  =     PriorityQueue()
   __a					  =     PriorityQueue()

   __a					  =     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():
      __a  ,      __a					  =     queue_forward.get()
      visited_forward.add(SCREAMING_SNAKE_CASE__     )

      __a  ,      __a					  =     queue_backward.get()
      visited_backward.add(SCREAMING_SNAKE_CASE__     )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

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

   if shortest_distance != np.inf:
      __a					  =     shortest_distance
   return shortest_path_distance


__UpperCamelCase   :							Dict         =   {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__UpperCamelCase   :							int         =   {
    """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()