






'''simple docstring'''





class 							A_    :






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				name
     _UpperCAmelCase				:      Tuple			  =				val


 def __str__(	self) ->		Optional[int]:

     """simple docstring"""



     return f'''{self.__class__.__name__}({self.name}, {self.val})'''


 def __lt__(	self							,  _A) ->		Optional[int]:

     """simple docstring"""



     return self.val < other.val



class 							A_    :






 '''simple docstring'''




 def __init__(	self							,  _A) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				{}
     _UpperCAmelCase				:      Tuple			  =				{}
     _UpperCAmelCase				:      str			  =				self.build_heap(_A)


 def __getitem__(	self							,  _A) ->		Tuple:

     """simple docstring"""



     return self.get_value(_A)


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

     """simple docstring"""



     return (idx - 1) // 2


 def  snake_case__				(	self							,  _A) ->		Tuple:

     """simple docstring"""



     return idx * 2 + 1


 def  snake_case__				(	self							,  _A) ->		Dict:

     """simple docstring"""



     return idx * 2 + 2


 def  snake_case__				(	self							,  _A) ->		Optional[Any]:

     """simple docstring"""



     return self.heap_dict[key]


 def  snake_case__				(	self							,  _A) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				len(_A) - 1
     _UpperCAmelCase				:      str			  =				self.get_parent_idx(_A)

     for idx, i in enumerate(_A):
         _UpperCAmelCase				:      int			  =				idx
         _UpperCAmelCase				:      Any			  =				i.val

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


 def  snake_case__				(	self							,  _A							,  _A) ->		str:

     """simple docstring"""



     while True:
         _UpperCAmelCase				:      Optional[Any]			  =				self.get_left_child_idx(_A)  # noqa: E741
         _UpperCAmelCase				:      List[str]			  =				self.get_right_child_idx(_A)

         _UpperCAmelCase				:      List[Any]			  =				idx
         if l < len(_A) and array[l] < array[idx]:
             _UpperCAmelCase				:      str			  =				l
         if r < len(_A) and array[r] < array[smallest]:
             _UpperCAmelCase				:      Union[str, Any]			  =				r

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


 def  snake_case__				(	self							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.get_parent_idx(_A)
     while p >= 0 and self.heap[p] > self.heap[idx]:
         _UpperCAmelCase				:      str			  =				self.heap[idx], self.heap[p]
         _UpperCAmelCase				:      Dict			  =				(
             self.idx_of_element[self.heap[idx]],
             self.idx_of_element[self.heap[p]],
         )
         _UpperCAmelCase				:      str			  =				p
         _UpperCAmelCase				:      Tuple			  =				self.get_parent_idx(_A)


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

     """simple docstring"""



     return self.heap[0]


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

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.heap[-1], self.heap[0]
     _UpperCAmelCase				:      Optional[Any]			  =				(
         self.idx_of_element[self.heap[-1]],
         self.idx_of_element[self.heap[0]],
     )

     _UpperCAmelCase				:      int			  =				self.heap.pop()
     del self.idx_of_element[x]
     self.sift_down(0							,  self.heap)
     return x


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

     """simple docstring"""



     self.heap.append(_A)
     _UpperCAmelCase				:      int			  =				len(self.heap) - 1
     _UpperCAmelCase				:      List[str]			  =				node.val
     self.sift_up(len(self.heap) - 1)


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

     """simple docstring"""



     return len(self.heap) == 0


 def  snake_case__				(	self							,  _A							,  _A) ->		List[Any]:

     """simple docstring"""



     assert (
         self.heap[self.idx_of_element[node]].val > new_value
     ), "newValue must be less that current value"
     _UpperCAmelCase				:      List[str]			  =				new_value
     _UpperCAmelCase				:      int			  =				new_value
     self.sift_up(self.idx_of_element[node])



SCREAMING_SNAKE_CASE 							=						Node('R', -1)
SCREAMING_SNAKE_CASE 							=						Node('B', 6)
SCREAMING_SNAKE_CASE 							=						Node('A', 3)
SCREAMING_SNAKE_CASE 							=						Node('X', 1)
SCREAMING_SNAKE_CASE 							=						Node('E', 4)

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

# Generating Min-Heap from array
SCREAMING_SNAKE_CASE 							=						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, -17)

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

if __name__ == "__main__":
  import doctest

  doctest.testmod()












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


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)
@add_end_docstrings(__lowercase			)
class 							A_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  **_A) ->		int:

     """simple docstring"""



     super().__init__(**_A)

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


 def __call__(	self							,  _A							,  **_A) ->		List[str]:

     """simple docstring"""



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


 def  snake_case__				(	self							,  **_A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				{}
     if "candidate_labels" in kwargs:
         _UpperCAmelCase				:      Union[str, Any]			  =				kwargs['''candidate_labels''']
     if "hypothesis_template" in kwargs:
         _UpperCAmelCase				:      Dict			  =				kwargs['''hypothesis_template''']

     return preprocess_params, {}, {}


 def  snake_case__				(	self							,  _A							,  _A=None							,  _A="This is a sound of {}.") ->		Tuple:

     """simple docstring"""



     if isinstance(_A							,  _A):
         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
             _UpperCAmelCase				:      List[Any]			  =				requests.get(_A).content
         else:
             with open(_A							,  '''rb''') as f:
                 _UpperCAmelCase				:      List[str]			  =				f.read()

     if isinstance(_A							,  _A):
         _UpperCAmelCase				:      List[Any]			  =				ffmpeg_read(_A							,  self.feature_extractor.sampling_rate)

     if not isinstance(_A							,  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''')

     _UpperCAmelCase				:      Optional[Any]			  =				self.feature_extractor(
         [audio]							,  sampling_rate=self.feature_extractor.sampling_rate							,  return_tensors='''pt''')
     _UpperCAmelCase				:      Dict			  =				candidate_labels
     _UpperCAmelCase				:      List[str]			  =				[hypothesis_template.format(_A) for x in candidate_labels]
     _UpperCAmelCase				:      Optional[Any]			  =				self.tokenizer(_A							,  return_tensors=self.framework							,  padding=_A)
     _UpperCAmelCase				:      str			  =				[text_inputs]
     return inputs


 def  snake_case__				(	self							,  _A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				model_inputs.pop('''candidate_labels''')
     _UpperCAmelCase				:      Union[str, Any]			  =				model_inputs.pop('''text_inputs''')
     if isinstance(text_inputs[0]							,  _A):
         _UpperCAmelCase				:      int			  =				text_inputs[0]
     else:
         # Batching case.
         _UpperCAmelCase				:      Dict			  =				text_inputs[0][0]

     _UpperCAmelCase				:      str			  =				self.model(**_A							,  **_A)

     _UpperCAmelCase				:      str			  =				{
         '''candidate_labels''': candidate_labels,
         '''logits''': outputs.logits_per_audio,
     }
     return model_outputs


 def  snake_case__				(	self							,  _A) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				model_outputs.pop('''candidate_labels''')
     _UpperCAmelCase				:      str			  =				model_outputs['''logits'''][0]

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

     _UpperCAmelCase				:      List[str]			  =				[
         {'''score''': score, '''label''': candidate_label}
         for score, candidate_label in sorted(zip(_A							,  _A)							,  key=lambda _A: -x[0])
     ]
     return result




