





'''simple docstring'''

from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


a   	=    logging.get_logger(__name__)







@add_end_docstrings(_snake_case      )
class __a			( _snake_case      ):





   def __init__(       self					:      Optional[Any]					,**lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )

      if self.framework != "pt":
         raise ValueError(f"""The {self.__class__} is only available in PyTorch."""     )
      # No specific FOR_XXX available yet





   def __call__(       self					:      List[Any]					,lowerCamelCase					:      Union[np.ndarray, bytes, str]					,**lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      return super().__call__(lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict					,**lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							{}
      if "candidate_labels" in kwargs:
         __SCREAMING_SNAKE_CASE   				=							kwargs["""candidate_labels"""]
      if "hypothesis_template" in kwargs:
         __SCREAMING_SNAKE_CASE   				=							kwargs["""hypothesis_template"""]

      return preprocess_params, {}, {}





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      int					,lowerCamelCase					:      Tuple=None					,lowerCamelCase					:      int="This is a sound of {}."     ):

      '''simple docstring'''

      if isinstance(lowerCamelCase					,lowerCamelCase     ):
         if audio.startswith("""http://"""     ) or audio.startswith("""https://"""     ):
            # We need to actually check for a real protocol, otherwise it's impossible to use a local file
            # like http_huggingface_co.png
            __SCREAMING_SNAKE_CASE   				=							requests.get(lowerCamelCase     ).content
         else:
            with open(lowerCamelCase					,"""rb"""     ) as f:
               __SCREAMING_SNAKE_CASE   				=							f.read()

      if isinstance(lowerCamelCase					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							ffmpeg_read(lowerCamelCase					,self.feature_extractor.sampling_rate     )

      if not isinstance(lowerCamelCase					,np.ndarray     ):
         raise ValueError("""We expect a numpy ndarray as input"""     )
      if len(audio.shape     ) != 1:
         raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline"""     )

      __SCREAMING_SNAKE_CASE   				=							self.feature_extractor(
          [audio]					,sampling_rate=self.feature_extractor.sampling_rate					,return_tensors="""pt"""     )
      __SCREAMING_SNAKE_CASE   				=							candidate_labels
      __SCREAMING_SNAKE_CASE   				=							[hypothesis_template.format(lowerCamelCase     ) for x in candidate_labels]
      __SCREAMING_SNAKE_CASE   				=							self.tokenizer(lowerCamelCase					,return_tensors=self.framework					,padding=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							[text_inputs]
      return inputs





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							model_inputs.pop("""candidate_labels"""     )
      __SCREAMING_SNAKE_CASE   				=							model_inputs.pop("""text_inputs"""     )
      if isinstance(text_inputs[0]					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							text_inputs[0]
      else:
         # Batching case.
         __SCREAMING_SNAKE_CASE   				=							text_inputs[0][0]

      __SCREAMING_SNAKE_CASE   				=							self.model(**lowerCamelCase					,**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							{
          """candidate_labels""": candidate_labels,
          """logits""": outputs.logits_per_audio,
      }
      return model_outputs





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							model_outputs.pop("""candidate_labels"""     )
      __SCREAMING_SNAKE_CASE   				=							model_outputs["""logits"""][0]

      if self.framework == "pt":
         __SCREAMING_SNAKE_CASE   				=							logits.softmax(dim=0     )
         __SCREAMING_SNAKE_CASE   				=							probs.tolist()
      else:
         raise ValueError("""`tf` framework not supported."""     )

      __SCREAMING_SNAKE_CASE   				=							[
          {"""score""": score, """label""": candidate_label}
          for score, candidate_label in sorted(zip(lowerCamelCase					,lowerCamelCase     )					,key=lambda lowerCamelCase     : -x[0]     )
      ]
      return result












'''simple docstring'''

import numpy as np







class __a			:





   def __init__(       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							(0, 0)
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0





   def __eq__(       self					:      int					,lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      return self.position == cell.position





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      print(self.position     )







class __a			:





   def __init__(       self					:      int					,lowerCamelCase					:      Optional[int]=(5, 5)     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.zeros(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							world_size[0]
      __SCREAMING_SNAKE_CASE   				=							world_size[1]





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      print(self.w     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          (-1, -1),
          (-1, 0),
          (-1, 1),
          (0, -1),
          (0, 1),
          (1, -1),
          (1, 0),
          (1, 1),
      ]
      __SCREAMING_SNAKE_CASE   				=							cell.position[0]
      __SCREAMING_SNAKE_CASE   				=							cell.position[1]
      __SCREAMING_SNAKE_CASE   				=							[]
      for n in neughbour_cord:
         __SCREAMING_SNAKE_CASE   				=							current_x + n[0]
         __SCREAMING_SNAKE_CASE   				=							current_y + n[1]
         if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            __SCREAMING_SNAKE_CASE   				=							Cell()
            __SCREAMING_SNAKE_CASE   				=							(x, y)
            __SCREAMING_SNAKE_CASE   				=							cell
            neighbours.append(lowerCamelCase     )
      return neighbours





def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							[]
   _open.append(__UpperCAmelCase   )

   while _open:
      __SCREAMING_SNAKE_CASE   				=							np.argmin([n.f for n in _open]   )
      __SCREAMING_SNAKE_CASE   				=							_open[min_f]
      _closed.append(_open.pop(__UpperCAmelCase   )   )
      if current == goal:
         break
      for n in world.get_neigbours(__UpperCAmelCase   ):
         for c in _closed:
            if c == n:
               continue
         __SCREAMING_SNAKE_CASE   				=							current.g + 1
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							n.position
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							goal.position
         __SCREAMING_SNAKE_CASE   				=							(ya - ya) ** 2 + (xa - xa) ** 2
         __SCREAMING_SNAKE_CASE   				=							n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							[]
   while current.parent is not None:
      path.append(current.position   )
      __SCREAMING_SNAKE_CASE   				=							current.parent
   path.append(current.position   )
   return path[::-1]


if __name__ == "__main__":
    a   	=    Gridworld()
    # Start position and goal
    a   	=    Cell()
    a   	=    (0, 0)
    a   	=    Cell()
    a   	=    (4, 4)
    print(F'''path from {start.position} to {goal.position}''')
    a   	=    astar(world, start, goal)
    #   Just for visual reasons.
    for i in s:
        a   	=    1
    print(world.w)





