




'''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				: Dict					      =			logging.get_logger(__name__)
def 							snake_case_    (  a__						:      bytes       ,a__						:      int     ):



    """simple docstring"""




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

    try:
        __lowercase     							=		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"""     )
    __lowercase     							=		ffmpeg_process.communicate(a__     )
    __lowercase     							=		output_stream[0]

    __lowercase     							=		np.frombuffer(a__       ,np.floataa     )
    if audio.shape[0] == 0:
        raise ValueError("""Malformed soundfile"""     )
    return audio






@add_end_docstrings(__A	)
class 			SCREAMING_SNAKE_CASE(							__A	):

       def __init__(       self   ,						*lowerCamelCase__   ,						**lowerCamelCase__	)       ->   Optional[Any]:

           """simple docstring"""



           __lowercase     							=		5
           super().__init__(*lowerCamelCase__   ,						**lowerCamelCase__	)

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

           self.check_model_type(lowerCamelCase__	)

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

           """simple docstring"""



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

       def        snake_case__     (       self   ,						lowerCamelCase__=None   ,						**lowerCamelCase__	)       ->   List[Any]:

           """simple docstring"""



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

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

           """simple docstring"""



           if isinstance(lowerCamelCase__   ,						lowerCamelCase__	):
               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
                   __lowercase     							=		requests.get(lowerCamelCase__	).content
               else:
                   with open(lowerCamelCase__   ,						"""rb"""	) as f:
                       __lowercase     							=		f.read()

           if isinstance(lowerCamelCase__   ,						lowerCamelCase__	):
               __lowercase     							=		ffmpeg_read(lowerCamelCase__   ,						self.feature_extractor.sampling_rate	)

           if isinstance(lowerCamelCase__   ,						lowerCamelCase__	):
               # 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"""	)

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

                   __lowercase     							=		F.resample(
                       torch.from_numpy(lowerCamelCase__	)   ,						lowerCamelCase__   ,						self.feature_extractor.sampling_rate	).numpy()

           if not isinstance(lowerCamelCase__   ,						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"""	)

           __lowercase     							=		self.feature_extractor(
               lowerCamelCase__   ,						sampling_rate=self.feature_extractor.sampling_rate   ,						return_tensors="""pt"""	)
           return processed

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		self.model(**lowerCamelCase__	)
           return model_outputs




       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__=5	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		model_outputs.logits[0].softmax(-1	)
           __lowercase							,__lowercase     							=		probs.topk(lowerCamelCase__	)

           __lowercase     							=		scores.tolist()
           __lowercase     							=		ids.tolist()

           __lowercase     							=		[{"""score""": score, """label""": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase__   ,						lowerCamelCase__	)]

           return labels










'''simple docstring'''
def 							snake_case_    (  a__						:      List[str]       ,a__						:      Optional[int]     ):



    """simple docstring"""




    __lowercase     							=		(boundary[1] - boundary[0]) / steps
    __lowercase     							=		boundary[0]
    __lowercase     							=		boundary[1]
    __lowercase     							=		make_points(a__       ,a__       ,a__     )
    __lowercase     							=		0.0
    y += (h / 3.0) * f(a__     )
    __lowercase     							=		2
    for i in x_i:
        y += (h / 3) * (4 - 2 * (cnt % 2)) * f(a__     )
        cnt += 1
    y += (h / 3.0) * f(a__     )
    return y
def 							snake_case_    (  a__						:      Optional[Any]       ,a__						:      List[str]       ,a__						:      Optional[Any]     ):



    """simple docstring"""




    __lowercase     							=		a + h
    while x < (b - h):
        yield x
        __lowercase     							=		x + h
def 							snake_case_    (  a__						:      List[Any]     ):  # enter your function here



    """simple docstring"""




    __lowercase     							=		(x - 0) * (x - 0)
    return y
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		0.0  # Lower bound of integration
    __lowercase     							=		1.0  # Upper bound of integration
    __lowercase     							=		1_0.0  # define number of steps or resolution
    __lowercase     							=		[a, b]  # define boundary of integration
    __lowercase     							=		method_a(a__       ,a__     )
    print(f'y = {y}'     )


if __name__ == "__main__":
  main()




