






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

lowerCamelCase_						     =  logging.get_logger(__name__)
def      lowerCamelCase					(						a_ ,						a_	)	->			np.array:
							lowerCAmelCase_										=     F'''{sampling_rate}'''
							lowerCAmelCase_										=     '1'
							lowerCAmelCase_										=     'f32le'
							lowerCAmelCase_										=     [
							    'ffmpeg',
							    '-i',
							    'pipe:0',
							    '-ac',
							    ac,
							    '-ar',
							    ar,
							    '-f',
							    format_for_conversion,
							    '-hide_banner',
							    '-loglevel',
							    'quiet',
							    'pipe:1',
							]

							try:
														lowerCAmelCase_										=     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'	)
							lowerCAmelCase_										=     ffmpeg_process.communicate(a_	)
							lowerCAmelCase_										=     output_stream[0]

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




@add_end_docstrings(a_		)
class 							a_ (						a_		):


							'''simple docstring'''






							def __init__(		self			,			*lowercase_			,			**lowercase_   )					->					Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     5
														super().__init__(*lowercase_			,			**lowercase_   )

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

														self.check_model_type(lowercase_   )

							def __call__(		self			,			lowercase_			,			**lowercase_			,			)					->					int:
														'''simple docstring'''

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

							def      _lowercase       (		self			,			lowercase_=None			,			**lowercase_   )					->					List[Any]:
														'''simple docstring'''

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

							def      _lowercase       (		self			,			lowercase_   )					->					int:
														'''simple docstring'''

														if isinstance(lowercase_			,			lowercase_   ):
																					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
																												lowerCAmelCase_										=     requests.get(lowercase_   ).content
																					else:
																												with open(lowercase_			,			'rb'   ) as f:
																																			lowerCAmelCase_										=     f.read()

														if isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     ffmpeg_read(lowercase_			,			self.feature_extractor.sampling_rate   )

														if isinstance(lowercase_			,			lowercase_   ):
																					# 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'   )

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

																												lowerCAmelCase_										=     F.resample(
																												    torch.from_numpy(lowercase_   )			,			lowercase_			,			self.feature_extractor.sampling_rate   ).numpy()

														if not isinstance(lowercase_			,			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'   )

														lowerCAmelCase_										=     self.feature_extractor(
														    lowercase_			,			sampling_rate=self.feature_extractor.sampling_rate			,			return_tensors='pt'   )
														return processed

							def      _lowercase       (		self			,			lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.model(**lowercase_   )
														return model_outputs






							def      _lowercase       (		self			,			lowercase_			,			lowercase_=5   )					->					Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     model_outputs.logits[0].softmax(-1   )
														lowerCAmelCase_						,   lowerCAmelCase_										=     probs.topk(lowercase_   )

														lowerCAmelCase_										=     scores.tolist()
														lowerCAmelCase_										=     ids.tolist()

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

														return labels











from dataclasses import dataclass
from typing import Callable, Optional

import datasets




@dataclass
class 							a_ (						datasets.BuilderConfig		):


							'''simple docstring'''






							__a:	Optional[Callable]     =    None
							__a:	Optional[dict]     =    None
							__a:	Optional[datasets.Features]     =    None

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														assert self.generator is not None, "generator must be specified"

														if self.gen_kwargs is None:
																					lowerCAmelCase_										=     {}




class 							a_ (						datasets.GeneratorBasedBuilder		):


							'''simple docstring'''






							__a:	Optional[Any]     =    GeneratorConfig

							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														return datasets.DatasetInfo(features=self.config.features   )

							def      _lowercase       (		self			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs=self.config.gen_kwargs   )]






							def      _lowercase       (		self			,			**lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														for idx, ex in enumerate(self.config.generator(**lowercase_   )   ):
																					yield idx, ex



