






'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class a_    (    UpperCamelCase_							):
   _snake_case        =							["""image_processor""", """tokenizer"""]
   _snake_case        =							"""AutoImageProcessor"""
   _snake_case        =							"""AutoTokenizer"""







   def __init__(self					,   __a=None					,   __a=None					,   **__a)			->      Any:
      """simple docstring"""






      __snake_case				:      str     							= None
      if "feature_extractor" in kwargs:
         warnings.warn(
             'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
             ' instead.'					,   __a					,   )
         __snake_case				:      Any     							= kwargs.pop('feature_extractor')

      __snake_case				:      Any     							= image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
         raise ValueError('You need to specify an `image_processor`.')
      if tokenizer is None:
         raise ValueError('You need to specify a `tokenizer`.')

      super().__init__(__a					,   __a)
      __snake_case				:      List[str]     							= self.image_processor
      __snake_case				:      int     							= False







   def __call__(self					,   *__a					,   **__a)			->      List[Any]:
      """simple docstring"""






      if self._in_target_context_manager:
         return self.current_processor(*__a					,   **__a)

      __snake_case				:      Tuple     							= kwargs.pop('images'					,   __a)
      __snake_case				:      str     							= kwargs.pop('text'					,   __a)
      if len(__a) > 0:
         __snake_case				:      Union[str, Any]     							= args[0]
         __snake_case				:      Optional[Any]     							= args[1:]

      if images is None and text is None:
         raise ValueError('You need to specify either an `images` or `text` input to process.')

      if images is not None:
         __snake_case				:      int     							= self.image_processor(__a					,   *__a					,   **__a)
      if text is not None:
         __snake_case				:      Tuple     							= self.tokenizer(__a					,   **__a)

      if text is None:
         return inputs
      elif images is None:
         return encodings
      else:
         __snake_case				:      List[Any]     							= encodings['input_ids']
         return inputs







   def     SCREAMING_SNAKE_CASE__					(self					,   *__a					,   **__a)			->      Tuple:
      """simple docstring"""






      return self.tokenizer.batch_decode(*__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   *__a					,   **__a)			->      int:
      """simple docstring"""






      return self.tokenizer.decode(*__a					,   **__a)







   @contextmanager
   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      warnings.warn(
          '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
          'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
          'your images inputs, or in a separate call.')
      __snake_case				:      List[str]     							= True
      __snake_case				:      Optional[Any]     							= self.tokenizer
      yield
      __snake_case				:      Dict     							= self.image_processor
      __snake_case				:      List[Any]     							= False







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=False					,   __a=None)			->      Optional[Any]:
      """simple docstring"""






      if added_vocab is None:
         __snake_case				:      int     							= self.tokenizer.get_added_vocab()

      __snake_case				:      Any     							= {}

      while tokens:
         __snake_case				:      Any     							= re.search(R'<s_(.*?)>'					,   __a					,   re.IGNORECASE)
         if start_token is None:
            break
         __snake_case				:      int     							= start_token.group(1)
         __snake_case				:      Tuple     							= re.search(RF"""</s_{key}>"""					,   __a					,   re.IGNORECASE)
         __snake_case				:      Any     							= start_token.group()
         if end_token is None:
            __snake_case				:      int     							= tokens.replace(__a					,   '')
         else:
            __snake_case				:      str     							= end_token.group()
            __snake_case				:      Optional[int]     							= re.escape(__a)
            __snake_case				:      Tuple     							= re.escape(__a)
            __snake_case				:      Optional[int]     							= re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""					,   __a					,   re.IGNORECASE)
            if content is not None:
               __snake_case				:      Union[str, Any]     							= content.group(1).strip()
               if r"<s_" in content and r"</s_" in content:  # non-leaf node
                  __snake_case				:      Union[str, Any]     							= self.tokenajson(__a					,   is_inner_value=__a					,   added_vocab=__a)
                  if value:
                     if len(__a) == 1:
                        __snake_case				:      Optional[Any]     							= value[0]
                     __snake_case				:      Union[str, Any]     							= value
               else:  # leaf nodes
                  __snake_case				:      Any     							= []
                  for leaf in content.split(R'<sep/>'):
                     __snake_case				:      List[Any]     							= leaf.strip()
                     if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                        __snake_case				:      Optional[Any]     							= leaf[1:-2]  # for categorical special tokens
                     output[key].append(__a)
                  if len(output[key]) == 1:
                     __snake_case				:      Optional[Any]     							= output[key][0]

            __snake_case				:      str     							= tokens[tokens.find(__a) + len(__a) :].strip()
            if tokens[:6] == r"<sep/>":  # non-leaf nodes
               return [output] + self.tokenajson(tokens[6:]					,   is_inner_value=__a					,   added_vocab=__a)

      if len(__a):
         return [output] if is_inner_value else output
      else:
         return [] if is_inner_value else {"text_sequence": tokens}







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,   __a					,   )
      return self.image_processor_class







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,   __a					,   )
      return self.image_processor







'''simple docstring'''






import math




class a_    :







   def __init__(self					,   __a=0)			->      Any:  # a graph with Node 0,1,...,N-1
      """simple docstring"""






      __snake_case				:      List[str]     							= n
      __snake_case				:      Tuple     							= [
          [math.inf for j in range(0					,   __a)] for i in range(0					,   __a)
      ]  # adjacency matrix for weight
      __snake_case				:      Union[str, Any]     							= [
          [math.inf for j in range(0					,   __a)] for i in range(0					,   __a)
      ]  # dp[i][j] stores minimum distance from i to j







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= w







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      for k in range(0					,   self.n):
         for i in range(0					,   self.n):
            for j in range(0					,   self.n):
               __snake_case				:      List[Any]     							= min(self.dp[i][j]					,   self.dp[i][k] + self.dp[k][j])







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      Optional[int]:
      """simple docstring"""






      return self.dp[u][v]


if __name__ == "__main__":
      __A    	=   Graph(5)
      graph.add_edge(0, 2, 9)
      graph.add_edge(0, 4, 1_0)
      graph.add_edge(1, 3, 5)
      graph.add_edge(2, 3, 7)
      graph.add_edge(3, 0, 1_0)
      graph.add_edge(3, 1, 2)
      graph.add_edge(3, 2, 1)
      graph.add_edge(3, 4, 6)
      graph.add_edge(4, 1, 3)
      graph.add_edge(4, 2, 4)
      graph.add_edge(4, 3, 9)
      graph.floyd_warshall()
      graph.show_min(1, 4)
      graph.show_min(0, 3)