





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


snake_case__					:							Dict													=     logging.get_logger(__name__)





@add_end_docstrings(_lowerCamelCase   )
class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[Any]    ,   **_UpperCamelCase	:Optional[Any] )->					List[Any]:
         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	:Tuple    ,   _UpperCamelCase	:Union[np.ndarray, bytes, str]    ,   **_UpperCamelCase	:Dict )->					List[str]:
         return super().__call__(_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:Tuple    ,   **_UpperCamelCase	:Tuple )->					str:
         __A          =				{}
         if "candidate_labels" in kwargs:
               __A          =				kwargs['''candidate_labels''']
         if "hypothesis_template" in kwargs:
               __A          =				kwargs['''hypothesis_template''']

         return preprocess_params, {}, {}




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:str=None    ,   _UpperCamelCase	:Optional[Any]="This is a sound of {}." )->					List[str]:
         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
                     __A          =				requests.get(_UpperCamelCase ).content
               else:
                     with open(_UpperCamelCase    ,   '''rb''' ) as f:
                           __A          =				f.read()

         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ):
               __A          =				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''' )

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




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:Optional[int] )->					Dict:
         __A          =				model_inputs.pop('''candidate_labels''' )
         __A          =				model_inputs.pop('''text_inputs''' )
         if isinstance(text_inputs[0]    ,   _UpperCamelCase ):
               __A          =				text_inputs[0]
         else:
               # Batching case.
               __A          =				text_inputs[0][0]

         __A          =				self.model(**_UpperCamelCase    ,   **_UpperCamelCase )

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






   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:Tuple )->					Tuple:
         __A          =				model_outputs.pop('''candidate_labels''' )
         __A          =				model_outputs['''logits'''][0]

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

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












from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

snake_case__					:							Union[str, Any]													=     (3, 9, -11, 0, 7, 5, 1, -1)
snake_case__					:							int													=     (4, 6, 2, 0, 8, 10, 3, -2)





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42





class A_					:




   def __init__(self	:Dict    ,   _UpperCamelCase	:Iterable[int] )->					None:
         __A          =				None
         for i in sorted(_UpperCamelCase    ,   reverse=_UpperCamelCase ):
               __A          =				Node(_UpperCamelCase    ,   self.head )




   def __iter__(self	:List[str] )->					Iterator[int]:
         __A          =				self.head
         while node:
               yield node.data
               __A          =				node.next_node




   def __len__(self	:Union[str, Any] )->					int:
         return sum(1 for _ in self )






   def __str__(self	:List[Any] )->					str:
         return " -> ".join([str(_UpperCamelCase ) for node in self] )





def _a				(	lowerCamelCase:   SortedLinkedList					,				lowerCamelCase:   SortedLinkedList )	->						SortedLinkedList:







      '''simple docstring'''






      return SortedLinkedList(list(lowerCamelCase ) + list(lowerCamelCase ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       snake_case__					:							Any													=     SortedLinkedList
       print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





