


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 :				List[Any]						    =       logging.get_logger(__name__)
def 							UpperCamelCase_  (				__snake_case			:						bytes	,			__snake_case			:						int     )->      np.array:




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

						try:
												__magic_name__ 		=			subprocess.Popen(__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'     )
						__magic_name__ 		=			ffmpeg_process.communicate(__snake_case     )
						__magic_name__ 		=			output_stream[0]

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




@add_end_docstrings(_UpperCAmelCase)
class     __snake_case							(      _UpperCAmelCase):
					def __init__(	self					:	Any  ,			*_lowerCAmelCase					:	Union[str, Any]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				List[Any]:



											'''simple docstring'''





											__magic_name__ 		=			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					:	List[str]  ,			_lowerCAmelCase					:	Union[np.ndarray, bytes, str]  ,			**_lowerCAmelCase					:	Tuple  ,			)       ->				Optional[Any]:



											'''simple docstring'''





											return super().__call__(_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Optional[Any]  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	Tuple     )       ->				str:



											'''simple docstring'''





											__magic_name__ 		=			{}
											if top_k is not None:
																	if top_k > self.model.config.num_labels:
																							__magic_name__ 		=			self.model.config.num_labels
																	__magic_name__ 		=			top_k
											return {}, {}, postprocess_params
					def   a (	self					:	int  ,			_lowerCAmelCase					:	Union[str, Any]     )       ->				Dict:



											'''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
																							__magic_name__ 		=			requests.get(_lowerCAmelCase     ).content
																	else:
																							with open(_lowerCAmelCase  ,			'rb'     ) as f:
																													__magic_name__ 		=			f.read()

											if isinstance(_lowerCAmelCase  ,			_lowerCAmelCase     ):
																	__magic_name__ 		=			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'     )

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

																							__magic_name__ 		=			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'     )

											__magic_name__ 		=			self.feature_extractor(
											    _lowerCAmelCase  ,			sampling_rate=self.feature_extractor.sampling_rate  ,			return_tensors='pt'     )
											return processed
					def   a (	self					:	str  ,			_lowerCAmelCase					:	List[Any]     )       ->				Dict:



											'''simple docstring'''





											__magic_name__ 		=			self.model(**_lowerCAmelCase     )
											return model_outputs







					def   a (	self					:	List[str]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Optional[Any]=5     )       ->				List[Any]:



											'''simple docstring'''





											__magic_name__ 		=			model_outputs.logits[0].softmax(-1     )
											__magic_name__		,			__magic_name__ 		=			probs.topk(_lowerCAmelCase     )

											__magic_name__ 		=			scores.tolist()
											__magic_name__ 		=			ids.tolist()

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

											return labels





def 							UpperCamelCase_  (				__snake_case			:						list[int]     )->      tuple[int, int]:




						'''simple docstring'''
						# base case
						if len(__snake_case     ) == 1:
												return a[0], a[0]
						else:
												# split A into half.
												__magic_name__ 		=			a[: len(__snake_case     ) // 2]
												__magic_name__ 		=			a[len(__snake_case     ) // 2 :]

												# 2 sub problems, 1/2 of original size.
												__magic_name__		,			__magic_name__ 		=			max_difference(__snake_case     )
												__magic_name__		,			__magic_name__ 		=			max_difference(__snake_case     )

												# get min of first and max of second
												# linear time
												__magic_name__ 		=			min(__snake_case     )
												__magic_name__ 		=			max(__snake_case     )

												# 3 cases, either (small1, big1),
												# (min_first, max_second), (small2, big2)
												# constant comparisons
												if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
																		return smalla, biga
												elif biga - smalla > max_second - min_first:
																		return smalla, biga
												else:
																		return min_first, max_second


if __name__ == "__main__":
					import doctest

					doctest.testmod()

