



'''simple docstring'''






class 							a__:







    def __init__(   self				:						Any		,    __snake_case				:						Any		,    __snake_case				:						Optional[int]			):
      a		:      Dict       =  name
      a		:      Optional[Any]       =  val







    def __str__(   self				:						Optional[int]			):
      return F"""{self.__class__.__name__}({self.name}, {self.val})"""







    def __lt__(   self				:						List[Any]		,    __snake_case				:						List[Any]			):
      return self.val < other.val

class 							a__:







    def __init__(   self				:						Union[str, Any]		,    __snake_case				:						List[Any]			):
      a		:      Dict       =  {}
      a		:      Optional[Any]       =  {}
      a		:      Any       =  self.build_heap(__snake_case			)







    def __getitem__(   self				:						Optional[Any]		,    __snake_case				:						str			):
      return self.get_value(__snake_case			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						int			):
      return (idx - 1) // 2







    def     lowercase_ (   self				:						str		,    __snake_case				:						List[str]			):
      return idx * 2 + 1







    def     lowercase_ (   self				:						str		,    __snake_case				:						Tuple			):
      return idx * 2 + 2







    def     lowercase_ (   self				:						int		,    __snake_case				:						Tuple			):
      return self.heap_dict[key]







    def     lowercase_ (   self				:						str		,    __snake_case				:						Tuple			):
      a		:      int       =  len(__snake_case			) - 1
      a		:      Optional[int]       =  self.get_parent_idx(__snake_case			)

      for idx, i in enumerate(__snake_case			):
        a		:      Tuple       =  idx
        a		:      Union[str, Any]       =  i.val

      for i in range(__snake_case		,    -1		,    -1			):
        self.sift_down(__snake_case		,    __snake_case			)
      return array







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Optional[Any]			):
      while True:
        a		:      Optional[Any]       =  self.get_left_child_idx(__snake_case			)  # noqa: E741
        a		:      Tuple       =  self.get_right_child_idx(__snake_case			)

        a		:      str       =  idx
        if l < len(__snake_case			) and array[l] < array[idx]:
          a		:      Any       =  l
        if r < len(__snake_case			) and array[r] < array[smallest]:
          a		:      Optional[Any]       =  r

        if smallest != idx:
          a     ,			a		:      Optional[int]       =  array[smallest], array[idx]
          (
              (
              a
          )     ,			(
              a
          )     ,			
          )		:      Dict       =  (
              self.idx_of_element[array[smallest]],
              self.idx_of_element[array[idx]],
          )
          a		:      Dict       =  smallest
        else:
          break







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						List[Any]			):
      a		:      Dict       =  self.get_parent_idx(__snake_case			)
      while p >= 0 and self.heap[p] > self.heap[idx]:
        a     ,			a		:      Optional[Any]       =  self.heap[idx], self.heap[p]
        a     ,			a		:      Union[str, Any]       =  (
            self.idx_of_element[self.heap[idx]],
            self.idx_of_element[self.heap[p]],
        )
        a		:      Tuple       =  p
        a		:      Tuple       =  self.get_parent_idx(__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      return self.heap[0]







    def     lowercase_ (   self				:						int			):
      a     ,			a		:      List[str]       =  self.heap[-1], self.heap[0]
      a     ,			a		:      Tuple       =  (
          self.idx_of_element[self.heap[-1]],
          self.idx_of_element[self.heap[0]],
      )

      a		:      List[str]       =  self.heap.pop()
      del self.idx_of_element[x]
      self.sift_down(0		,    self.heap			)
      return x







    def     lowercase_ (   self				:						str		,    __snake_case				:						Union[str, Any]			):
      self.heap.append(__snake_case			)
      a		:      Optional[int]       =  len(self.heap			) - 1
      a		:      int       =  node.val
      self.sift_up(len(self.heap			) - 1			)







    def     lowercase_ (   self				:						List[Any]			):
      return len(self.heap			) == 0







    def     lowercase_ (   self				:						Any		,    __snake_case				:						Optional[int]		,    __snake_case				:						Tuple			):
      assert (
          self.heap[self.idx_of_element[node]].val > new_value
      ), "newValue must be less that current value"
      a		:      Any       =  new_value
      a		:      Tuple       =  new_value
      self.sift_up(self.idx_of_element[node]			)
lowerCAmelCase:      List[Any]		 =		Node('R', -1)
lowerCAmelCase:      Union[str, Any]		 =		Node('B', 6)
lowerCAmelCase:      Union[str, Any]		 =		Node('A', 3)
lowerCAmelCase:      int		 =		Node('X', 1)
lowerCAmelCase:      int		 =		Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCAmelCase:      str		 =		MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
     print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
     print(i)

if __name__ == "__main__":
     import doctest

     doctest.testmod()




'''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


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

@add_end_docstrings(lowerCamelCase__			)
class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    **__snake_case				:						List[str]			):
      super().__init__(**__snake_case			)

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







    def __call__(   self				:						Any		,    __snake_case				:						Union[np.ndarray, bytes, str]		,    **__snake_case				:						Optional[Any]			):
      return super().__call__(__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Dict		,    **__snake_case				:						Optional[int]			):
      a		:      str       =  {}
      if "candidate_labels" in kwargs:
        a		:      Optional[int]       =  kwargs['candidate_labels']
      if "hypothesis_template" in kwargs:
        a		:      Any       =  kwargs['hypothesis_template']

      return preprocess_params, {}, {}







    def     lowercase_ (   self				:						int		,    __snake_case				:						Optional[Any]		,    __snake_case				:						List[Any]=None		,    __snake_case				:						List[str]="This is a sound of {}."			):
      if isinstance(__snake_case		,    __snake_case			):
        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
          a		:      List[Any]       =  requests.get(__snake_case			).content
        else:
          with open(__snake_case		,    'rb'			) as f:
            a		:      List[str]       =  f.read()

      if isinstance(__snake_case		,    __snake_case			):
        a		:      Optional[Any]       =  ffmpeg_read(__snake_case		,    self.feature_extractor.sampling_rate			)

      if not isinstance(__snake_case		,    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'			)

      a		:      str       =  self.feature_extractor(
          [audio]		,    sampling_rate=self.feature_extractor.sampling_rate		,    return_tensors='pt'			)
      a		:      Tuple       =  candidate_labels
      a		:      Union[str, Any]       =  [hypothesis_template.format(__snake_case			) for x in candidate_labels]
      a		:      int       =  self.tokenizer(__snake_case		,    return_tensors=self.framework		,    padding=__snake_case			)
      a		:      Any       =  [text_inputs]
      return inputs







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str			):
      a		:      Any       =  model_inputs.pop('candidate_labels'			)
      a		:      List[Any]       =  model_inputs.pop('text_inputs'			)
      if isinstance(text_inputs[0]		,    __snake_case			):
        a		:      str       =  text_inputs[0]
      else:
        # Batching case.
        a		:      Dict       =  text_inputs[0][0]

      a		:      List[Any]       =  self.model(**__snake_case		,    **__snake_case			)

      a		:      Any       =  {
          'candidate_labels': candidate_labels,
          'logits': outputs.logits_per_audio,
      }
      return model_outputs







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						str			):
      a		:      Optional[int]       =  model_outputs.pop('candidate_labels'			)
      a		:      List[str]       =  model_outputs['logits'][0]

      if self.framework == "pt":
        a		:      str       =  logits.softmax(dim=0			)
        a		:      List[Any]       =  probs.tolist()
      else:
        raise ValueError('`tf` framework not supported.'			)

      a		:      Dict       =  [
          {'score': score, 'label': candidate_label}
          for score, candidate_label in sorted(zip(__snake_case		,    __snake_case			)		,    key=lambda __snake_case			: -x[0]			)
      ]
      return result