




'''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

UpperCamelCase     =				logging.get_logger(__name__)
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   np.array:
     A:		Optional[int]        =		F"""{sampling_rate}"""
     A:		Dict        =		'''1'''
     A:		Union[str, Any]        =		'''f32le'''
     A:		List[Any]        =		[
         '''ffmpeg''',
         '''-i''',
         '''pipe:0''',
         '''-ac''',
         ac,
         '''-ar''',
         ar,
         '''-f''',
         format_for_conversion,
         '''-hide_banner''',
         '''-loglevel''',
         '''quiet''',
         '''pipe:1''',
     ]

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

     A:		List[str]        =		np.frombuffer(__lowercase		,							np.floataa  )
     if audio.shape[0] == 0:
          raise ValueError('''Malformed soundfile'''  )
     return audio






@add_end_docstrings(UpperCAmelCase_       )
class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def __init__(   self							:       str				,     *SCREAMING_SNAKE_CASE_							:       int				,     **SCREAMING_SNAKE_CASE_							:       Dict  ) ->		List[str]:







       '''simple docstring'''



       A:		Optional[int]        =		5
       super().__init__(*SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

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

       self.check_model_type(SCREAMING_SNAKE_CASE_  )



  def __call__(   self							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Union[np.ndarray, bytes, str]				,     **SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     ) ->		List[str]:







       '''simple docstring'''



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



  def 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       List[Any]=None				,     **SCREAMING_SNAKE_CASE_							:       Any  ) ->		Any:







       '''simple docstring'''



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



  def 				_snake_case    (   self							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Optional[int]  ) ->		List[str]:







       '''simple docstring'''



       if isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ):
            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:		int        =		requests.get(SCREAMING_SNAKE_CASE_  ).content
            else:
                 with open(SCREAMING_SNAKE_CASE_				,     '''rb'''  ) as f:
                      A:		int        =		f.read()

       if isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ):
            A:		Union[str, Any]        =		ffmpeg_read(SCREAMING_SNAKE_CASE_				,     self.feature_extractor.sampling_rate  )

       if isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ):
            # 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:		Union[str, Any]        =		inputs.pop('''raw'''				,     SCREAMING_SNAKE_CASE_  )
            if _inputs is None:
                 # Remove path which will not be used from `datasets`.
                 inputs.pop('''path'''				,     SCREAMING_SNAKE_CASE_  )
                 A:		str        =		inputs.pop('''array'''				,     SCREAMING_SNAKE_CASE_  )
            A:		Tuple        =		inputs.pop('''sampling_rate'''  )
            A:		Union[str, Any]        =		_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:		str        =		F.resample(
                     torch.from_numpy(SCREAMING_SNAKE_CASE_  )				,     SCREAMING_SNAKE_CASE_				,     self.feature_extractor.sampling_rate  ).numpy()

       if not isinstance(SCREAMING_SNAKE_CASE_				,     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:		str        =		self.feature_extractor(
           SCREAMING_SNAKE_CASE_				,     sampling_rate=self.feature_extractor.sampling_rate				,     return_tensors='''pt'''  )
       return processed



  def 				_snake_case    (   self							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       Tuple  ) ->		Optional[Any]:







       '''simple docstring'''



       A:		Optional[Any]        =		self.model(**SCREAMING_SNAKE_CASE_  )
       return model_outputs




  def 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=5  ) ->		List[Any]:







       '''simple docstring'''



       A:		int        =		model_outputs.logits[0].softmax(-1  )
       A    ,						A:		Optional[int]        =		probs.topk(SCREAMING_SNAKE_CASE_  )

       A:		Optional[Any]        =		scores.tolist()
       A:		Optional[int]        =		ids.tolist()

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

       return labels












'''simple docstring'''


def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase  ) ->   float:
     A:		str        =		[1 / first_term]
     A:		Optional[int]        =		1 / first_term
     for _ in range(number_of_terms - 1  ):
          first_term += common_difference
          arithmetic_progression.append(__lowercase  )
     A:		int        =		[1 / step for step in arithmetic_progression]
     return sum(__lowercase  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))






