
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


lowerCamelCase__    :		Optional[Any]          =      logging.get_logger(__name__)



@add_end_docstrings(snake_case__							)
class       _snake_case   (	snake_case__							):


 def __init__(					self					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(**UpperCAmelCase_)

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


 def __call__(					self					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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


 def      lowercase__     (					self					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      {}
   if "candidate_labels" in kwargs:
     lowercase__  :   Tuple    		=      kwargs["""candidate_labels"""]
   if "hypothesis_template" in kwargs:
     lowercase__  :   List[str]    		=      kwargs["""hypothesis_template"""]

   return preprocess_params, {}, {}


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_="This is a sound of {}."):


   '''simple docstring'''




   if isinstance(UpperCAmelCase_					,  UpperCAmelCase_):
     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
       lowercase__  :   int    		=      requests.get(UpperCAmelCase_).content
     else:
       with open(UpperCAmelCase_					,  """rb""") as f:
         lowercase__  :   Dict    		=      f.read()

   if isinstance(UpperCAmelCase_					,  UpperCAmelCase_):
     lowercase__  :   Tuple    		=      ffmpeg_read(UpperCAmelCase_					,  self.feature_extractor.sampling_rate)

   if not isinstance(UpperCAmelCase_					,  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""")

   lowercase__  :   Optional[Any]    		=      self.feature_extractor(
       [audio]					,  sampling_rate=self.feature_extractor.sampling_rate					,  return_tensors="""pt""")
   lowercase__  :   Union[str, Any]    		=      candidate_labels
   lowercase__  :   Optional[int]    		=      [hypothesis_template.format(UpperCAmelCase_) for x in candidate_labels]
   lowercase__  :   int    		=      self.tokenizer(UpperCAmelCase_					,  return_tensors=self.framework					,  padding=UpperCAmelCase_)
   lowercase__  :   Union[str, Any]    		=      [text_inputs]
   return inputs


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      model_inputs.pop("""candidate_labels""")
   lowercase__  :   Tuple    		=      model_inputs.pop("""text_inputs""")
   if isinstance(text_inputs[0]					,  UpperCAmelCase_):
     lowercase__  :   List[str]    		=      text_inputs[0]
   else:
     # Batching case.
     lowercase__  :   str    		=      text_inputs[0][0]

   lowercase__  :   Tuple    		=      self.model(**UpperCAmelCase_					,  **UpperCAmelCase_)

   lowercase__  :   List[Any]    		=      {
       """candidate_labels""": candidate_labels,
       """logits""": outputs.logits_per_audio,
   }
   return model_outputs



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      model_outputs.pop("""candidate_labels""")
   lowercase__  :   Optional[Any]    		=      model_outputs["""logits"""][0]

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

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








lowerCamelCase__    :		str          =   [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   Any    		=      [False] * len(lowercase_   )
  lowercase__  :   List[Any]    		=      [s]
  lowercase__  :   int    		=      True

  while queue:
    lowercase__  :   Dict    		=      queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase_   )
        lowercase__  :   Optional[Any]    		=      True
        lowercase__  :   int    		=      u

  return visited[t]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      [-1] * (len(lowercase_   ))
  lowercase__  :   Optional[Any]    		=      0
  lowercase__  :   List[Any]    		=      []
  lowercase__  :   Optional[Any]    		=      [i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase_ , lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   Optional[int]    		=      float("""Inf"""   )
    lowercase__  :   Tuple    		=      sink

    while s != source:
      # Find the minimum value in select path
      lowercase__  :   List[str]    		=      min(lowercase_ , graph[parent[s]][s]   )
      lowercase__  :   List[str]    		=      parent[s]

    max_flow += path_flow
    lowercase__  :   str    		=      sink

    while v != source:
      lowercase__  :   int    		=      parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowercase__  :   Union[str, Any]    		=      parent[v]

  for i in range(len(lowercase_   )   ):
    for j in range(len(graph[0]   )   ):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)   )

  return res


if __name__ == "__main__":
     print(mincut(test_graph, source=0, sink=5))






