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

lowercase__      :    Any          =     logging.get_logger(__name__)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     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(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'''							)
							lowerCAmelCase     		=		ffmpeg_process.communicate(snake_case__							)
							lowerCAmelCase     		=		output_stream[0]

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






@add_end_docstrings(UpperCamelCase_						)
class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	def __init__(							self		,      *__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )						->List[Any]:
								# Default, might be overriden by the model.config.
								lowerCAmelCase     		=		5
								super().__init__(*__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )

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

								self.check_model_type(__SCREAMING_SNAKE_CASE    )


	def __call__(							self		,      __SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE		,      )						->Optional[Any]:
								return super().__call__(__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE=None		,      **__SCREAMING_SNAKE_CASE    )						->int:
								# No parameters on this pipeline right now
								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       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								if isinstance(__SCREAMING_SNAKE_CASE		,      __SCREAMING_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
																						lowerCAmelCase     		=		requests.get(__SCREAMING_SNAKE_CASE    ).content
															else:
																						with open(__SCREAMING_SNAKE_CASE		,      '''rb'''    ) as f:
																													lowerCAmelCase     		=		f.read()

								if isinstance(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ):
															lowerCAmelCase     		=		ffmpeg_read(__SCREAMING_SNAKE_CASE		,      self.feature_extractor.sampling_rate    )

								if isinstance(__SCREAMING_SNAKE_CASE		,      __SCREAMING_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'''    )

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

								if not isinstance(__SCREAMING_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'''    )

								lowerCAmelCase     		=		self.feature_extractor(
								    __SCREAMING_SNAKE_CASE		,      sampling_rate=self.feature_extractor.sampling_rate		,      return_tensors='''pt'''    )
								return processed


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		self.model(**__SCREAMING_SNAKE_CASE    )
								return model_outputs




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=5    )						->Tuple:
								lowerCAmelCase     		=		model_outputs.logits[0].softmax(-1    )
								lowerCAmelCase		,				lowerCAmelCase     		=		probs.topk(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		scores.tolist()
								lowerCAmelCase     		=		ids.tolist()

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

								return labels




import warnings

from .generation import FlaxGenerationMixin




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	warnings.warn(
	    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
	    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""				,					UpperCamelCase_				,					)



