


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 :				int						    =       logging.get_logger(__name__)
def 							UpperCamelCase_  (				__snake_case			:						Optional[Any]	,			__snake_case			:						List[str]     )->      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(a_)
class     __snake_case							(      a_):
					def __init__(	self					:	List[str]  ,			*_lowerCAmelCase					:	Optional[Any]  ,			**_lowerCAmelCase					:	Tuple     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			5
											super().__init__(*_snake_case  ,			**_snake_case     )

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

											self.check_model_type(_snake_case     )
					def __call__(	self					:	int  ,			_lowerCAmelCase					:	Optional[Any]  ,			**_lowerCAmelCase					:	List[Any]  ,			)       ->				int:



											'''simple docstring'''





											return super().__call__(_snake_case  ,			**_snake_case     )
					def   a (	self					:	int  ,			_lowerCAmelCase					:	List[Any]=None  ,			**_lowerCAmelCase					:	Dict     )       ->				Union[str, Any]:



											'''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					:	List[str]  ,			_lowerCAmelCase					:	List[Any]     )       ->				Optional[Any]:



											'''simple docstring'''





											if isinstance(_snake_case  ,			_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
																							__magic_name__ 		=			requests.get(_snake_case     ).content
																	else:
																							with open(_snake_case  ,			'rb'     ) as f:
																													__magic_name__ 		=			f.read()

											if isinstance(_snake_case  ,			_snake_case     ):
																	__magic_name__ 		=			ffmpeg_read(_snake_case  ,			self.feature_extractor.sampling_rate     )

											if isinstance(_snake_case  ,			_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'     )

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

											if not isinstance(_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'     )

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



											'''simple docstring'''





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







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



											'''simple docstring'''





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

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

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

											return labels





def 							UpperCamelCase_  (				__snake_case			:						str     )->      int:




						'''simple docstring'''
						__magic_name__ 		=			__magic_name__ 		=			0
						for letter in data:
												__magic_name__ 		=			(hash_ << 4) + ord(__snake_case     )
												__magic_name__ 		=			hash_ & 0xf000_0000
												if x != 0:
																		hash_ ^= x >> 24
												hash_ &= ~x
						return hash_


if __name__ == "__main__":
					import doctest

					doctest.testmod()

