




'''simple docstring'''







def 						snake_case_							(_a     :							str					):
    return "".join(chr(ord(_a					) + 3_2					) if '''A''' <= char <= '''Z''' else char for char in word					)


if __name__ == "__main__":
   from doctest import testmod

   testmod()











'''simple docstring'''







import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

A			   =logging.get_logger(__name__)





def 						snake_case_							(_a     :							bytes   ,       _a     :							int					):
    UpperCAmelCase      	=		F"{sampling_rate}"
    UpperCAmelCase      	=		'''1'''
    UpperCAmelCase      	=		'''f32le'''
    UpperCAmelCase      	=		[
        '''ffmpeg''',
        '''-i''',
        '''pipe:0''',
        '''-ac''',
        ac,
        '''-ar''',
        ar,
        '''-f''',
        format_for_conversion,
        '''-hide_banner''',
        '''-loglevel''',
        '''quiet''',
        '''pipe:1''',
    ]

    try:
        UpperCAmelCase      	=		subprocess.Popen(_a   ,       stdin=subprocess.PIPE   ,       stdout=subprocess.PIPE					)
    except FileNotFoundError:
        raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''					)
    UpperCAmelCase      	=		ffmpeg_process.communicate(_a					)
    UpperCAmelCase      	=		output_stream[0]

    UpperCAmelCase      	=		np.frombuffer(_a   ,       np.floataa					)
    if audio.shape[0] == 0:
        raise ValueError('''Malformed soundfile'''					)
    return audio







@add_end_docstrings(__a			)
class       _a  (     __a			):
       def __init__(     self						:     Dict       ,    *lowercase						:     Dict       ,    **lowercase						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		5
           super().__init__(*lowercase       ,    **lowercase    )

           if self.framework != "pt":
               raise ValueError(f"The {self.__class__} is only available in PyTorch."    )

           self.check_model_type(lowercase    )
       def __call__(     self						:     List[Any]       ,    lowercase						:     Union[np.ndarray, bytes, str]       ,    **lowercase						:     Dict       ,    ):



           '''simple docstring'''

           return super().__call__(lowercase       ,    **lowercase    )
       def    A				(     self						:     Tuple       ,    lowercase						:     Dict=None       ,    **lowercase						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		{}
           if top_k is not None:
               if top_k > self.model.config.num_labels:
                   UpperCAmelCase      	=		self.model.config.num_labels
               UpperCAmelCase      	=		top_k
           return {}, {}, postprocess_params
       def    A				(     self						:     List[Any]       ,    lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           if isinstance(lowercase       ,    lowercase    ):
               if inputs.startswith('''http://'''    ) or inputs.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      	=		requests.get(lowercase    ).content
               else:
                   with open(lowercase       ,    '''rb'''    ) as f:
                       UpperCAmelCase      	=		f.read()

           if isinstance(lowercase       ,    lowercase    ):
               UpperCAmelCase      	=		ffmpeg_read(lowercase       ,    self.feature_extractor.sampling_rate    )

           if isinstance(lowercase       ,    lowercase    ):
               # Accepting `"array"` which is the key defined in `datasets` for
               # better integration
               if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                   raise ValueError(
                       '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
                       '''"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '''
                       '''containing the sampling_rate associated with that array'''    )

               UpperCAmelCase      	=		inputs.pop('''raw'''       ,    lowercase    )
               if _inputs is None:
                   # Remove path which will not be used from `datasets`.
                   inputs.pop('''path'''       ,    lowercase    )
                   UpperCAmelCase      	=		inputs.pop('''array'''       ,    lowercase    )
               UpperCAmelCase      	=		inputs.pop('''sampling_rate'''    )
               UpperCAmelCase      	=		_inputs
               if in_sampling_rate != self.feature_extractor.sampling_rate:
                   import torch

                   if is_torchaudio_available():
                       from torchaudio import functional as F
                   else:
                       raise ImportError(
                           '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
                           '''The torchaudio package can be installed through: `pip install torchaudio`.'''    )

                   UpperCAmelCase      	=		F.resample(
                       torch.from_numpy(lowercase    )       ,    lowercase       ,    self.feature_extractor.sampling_rate    ).numpy()

           if not isinstance(lowercase       ,    np.ndarray    ):
               raise ValueError('''We expect a numpy ndarray as input'''    )
           if len(inputs.shape    ) != 1:
               raise ValueError('''We expect a single channel audio input for AudioClassificationPipeline'''    )

           UpperCAmelCase      	=		self.feature_extractor(
               lowercase       ,    sampling_rate=self.feature_extractor.sampling_rate       ,    return_tensors='''pt'''    )
           return processed
       def    A				(     self						:     Optional[Any]       ,    lowercase						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.model(**lowercase    )
           return model_outputs







       def    A				(     self						:     Union[str, Any]       ,    lowercase						:     Optional[Any]       ,    lowercase						:     List[str]=5    ):



           '''simple docstring'''

           UpperCAmelCase      	=		model_outputs.logits[0].softmax(-1    )
           UpperCAmelCase	,					UpperCAmelCase      	=		probs.topk(lowercase    )

           UpperCAmelCase      	=		scores.tolist()
           UpperCAmelCase      	=		ids.tolist()

           UpperCAmelCase      	=		[{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowercase       ,    lowercase    )]

           return labels




