

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

lowercase_									=    logging.get_logger(__name__)
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      bytes       ,     SCREAMING_SNAKE_CASE__     :      int )				-> np.array:





    '''simple docstring'''






    A__        =  f'{sampling_rate}'
    A__        =  '1'
    A__        =  'f32le'
    A__        =  [
        'ffmpeg',
        '-i',
        'pipe:0',
        '-ac',
        ac,
        '-ar',
        ar,
        '-f',
        format_for_conversion,
        '-hide_banner',
        '-loglevel',
        'quiet',
        'pipe:1',
    ]

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

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


@add_end_docstrings(_UpperCAmelCase					)
class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def __init__( self					:			List[Any],*lowercase_					:			str,**lowercase_					:			Any						)->       Dict:





        '''simple docstring'''







        A__        =  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					:			Optional[Any],lowercase_					:			Union[np.ndarray, bytes, str],**lowercase_					:			str,)->       Tuple:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			List[Any],lowercase_					:			int=None,**lowercase_					:			Union[str, Any]						)->       int:





        '''simple docstring'''







        A__        =  {}
        if top_k is not None:
            if top_k > self.model.config.num_labels:
                A__        =  self.model.config.num_labels
            A__        =  top_k
        return {}, {}, postprocess_params


    def 		snake_case__	( self					:			Optional[Any],lowercase_					:			Any						)->       List[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
                A__        =  requests.get(lowercase_						).content
            else:
                with open(lowercase_,'rb'						) as f:
                    A__        =  f.read()

        if isinstance(lowercase_,lowercase_						):
            A__        =  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'						)

            A__        =  inputs.pop('raw',lowercase_						)
            if _inputs is None:
                # Remove path which will not be used from `datasets`.
                inputs.pop('path',lowercase_						)
                A__        =  inputs.pop('array',lowercase_						)
            A__        =  inputs.pop('sampling_rate'						)
            A__        =  _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`.'						)

                A__        =  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'						)

        A__        =  self.feature_extractor(
            lowercase_,sampling_rate=self.feature_extractor.sampling_rate,return_tensors='pt'						)
        return processed


    def 		snake_case__	( self					:			Optional[int],lowercase_					:			List[Any]						)->       str:





        '''simple docstring'''







        A__        =  self.model(**lowercase_						)
        return model_outputs


    def 		snake_case__	( self					:			List[str],lowercase_					:			int,lowercase_					:			Any=5						)->       List[str]:





        '''simple docstring'''







        A__        =  model_outputs.logits[0].softmax(-1						)
        A__ ,		A__        =  probs.topk(lowercase_						)

        A__        =  scores.tolist()
        A__        =  ids.tolist()

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

        return labels




import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def __init__( self					:			str,*lowercase_					:			List[Any],**lowercase_					:			Dict						)->       None:





        '''simple docstring'''







        warnings.warn(
            'The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use MobileNetV1ImageProcessor instead.',lowercase_,)
        super().__init__(*lowercase_,**lowercase_						)

