



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             =logging.get_logger(__name__)

def 	a						(				_UpperCAmelCase							: bytes			,       _UpperCAmelCase							: int						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[int]													=     f'{sampling_rate}'
		__UpperCAmelCase   :					str													=     '''1'''
		__UpperCAmelCase   :					Dict													=     '''f32le'''
		__UpperCAmelCase   :					str													=     [
		    '''ffmpeg''',
		    '''-i''',
		    '''pipe:0''',
		    '''-ac''',
		    ac,
		    '''-ar''',
		    ar,
		    '''-f''',
		    format_for_conversion,
		    '''-hide_banner''',
		    '''-loglevel''',
		    '''quiet''',
		    '''pipe:1''',
		]

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

		__UpperCAmelCase   :					Optional[Any]													=     np.frombuffer(_UpperCAmelCase			,       np.floataa						)
		if audio.shape[0] == 0:
				raise ValueError('''Malformed soundfile'''						)
		return audio

@add_end_docstrings(__UpperCamelCase       )
class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			def __init__(       self							:			Optional[int]	,	*a_							:			Tuple	,	**a_							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     5
					super().__init__(*a_	,	**a_			)

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

					self.check_model_type(a_			)




			def __call__(       self							:			Optional[Any]	,	a_							:			Union[np.ndarray, bytes, str]	,	**a_							:			List[str]	,	):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			Tuple	,	a_							:			Dict=None	,	**a_							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     {}
					if top_k is not None:
							if top_k > self.model.config.num_labels:
									__UpperCAmelCase   :					Optional[int]													=     self.model.config.num_labels
							__UpperCAmelCase   :					Optional[int]													=     top_k
					return {}, {}, postprocess_params




			def 			snake_case__					(       self							:			Optional[int]	,	a_							:			Dict			):
					'''simple docstring'''





					if isinstance(a_	,	a_			):
							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
									__UpperCAmelCase   :					Tuple													=     requests.get(a_			).content
							else:
									with open(a_	,	'''rb'''			) as f:
											__UpperCAmelCase   :					str													=     f.read()

					if isinstance(a_	,	a_			):
							__UpperCAmelCase   :					Optional[int]													=     ffmpeg_read(a_	,	self.feature_extractor.sampling_rate			)

					if isinstance(a_	,	a_			):
							# 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'''			)

							__UpperCAmelCase   :					Union[str, Any]													=     inputs.pop('''raw'''	,	a_			)
							if _inputs is None:
									# Remove path which will not be used from `datasets`.
									inputs.pop('''path'''	,	a_			)
									__UpperCAmelCase   :					Optional[int]													=     inputs.pop('''array'''	,	a_			)
							__UpperCAmelCase   :					Any													=     inputs.pop('''sampling_rate'''			)
							__UpperCAmelCase   :					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`.'''			)

									__UpperCAmelCase   :					int													=     F.resample(
									    torch.from_numpy(a_			)	,	a_	,	self.feature_extractor.sampling_rate			).numpy()

					if not isinstance(a_	,	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'''			)

					__UpperCAmelCase   :					Optional[int]													=     self.feature_extractor(
					    a_	,	sampling_rate=self.feature_extractor.sampling_rate	,	return_tensors='''pt'''			)
					return processed




			def 			snake_case__					(       self							:			Optional[Any]	,	a_							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					Tuple													=     self.model(**a_			)
					return model_outputs




			def 			snake_case__					(       self							:			Union[str, Any]	,	a_							:			int	,	a_							:			List[Any]=5			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     model_outputs.logits[0].softmax(-1			)
					__UpperCAmelCase							, __UpperCAmelCase   :					List[Any]													=     probs.topk(a_			)

					__UpperCAmelCase   :					Union[str, Any]													=     scores.tolist()
					__UpperCAmelCase   :					Dict													=     ids.tolist()

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

					return labels










def 	a						(				_UpperCAmelCase							: str						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[Any]													=     53_81
		for x in s:
				__UpperCAmelCase   :					str													=     ((hash_value << 5) + hash_value) + ord(_UpperCAmelCase						)
		return hash_value & 0xFFFF_FFFF





