
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

snake_case_      :					Optional[int]				      =     logging.get_logger(__name__)



def      __UpperCAmelCase    (  snake_case_							:		bytes				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     F'{sampling_rate}'
	UpperCAmelCase:					Optional[int] 						=     "1"
	UpperCAmelCase:					Union[str, Any] 						=     "f32le"
	UpperCAmelCase:					Optional[int] 						=     [
	    "ffmpeg",
	    "-i",
	    "pipe:0",
	    "-ac",
	    ac,
	    "-ar",
	    ar,
	    "-f",
	    format_for_conversion,
	    "-hide_banner",
	    "-loglevel",
	    "quiet",
	    "pipe:1",
	]

	try:
		UpperCAmelCase:					Optional[int] 						=     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"				)
	UpperCAmelCase:					Dict 						=     ffmpeg_process.communicate(snake_case_				)
	UpperCAmelCase:					Tuple 						=     output_stream[0]

	UpperCAmelCase:					Optional[Any] 						=     np.frombuffer(snake_case_				,					np.floataa				)
	if audio.shape[0] == 0:
		raise ValueError("Malformed soundfile"				)
	return audio





@add_end_docstrings(lowercase		)
class 		__lowerCamelCase     (   lowercase		):



							def __init__(     self	,				*__snake_case	,				**__snake_case							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     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	,				__snake_case	,				**__snake_case	,				)						->     Optional[int]:






								"""simple docstring"""



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



							def     A__ (     self	,				__snake_case=None	,				**__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     {}
								if top_k is not None:
									if top_k > self.model.config.num_labels:
										UpperCAmelCase:					Union[str, Any] 						=     self.model.config.num_labels
									UpperCAmelCase:					str 						=     top_k
								return {}, {}, postprocess_params



							def     A__ (     self	,				__snake_case							)						->     str:






								"""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
										UpperCAmelCase:					List[Any] 						=     requests.get(__snake_case							).content
									else:
										with open(__snake_case	,				"rb"							) as f:
											UpperCAmelCase:					int 						=     f.read()

								if isinstance(__snake_case	,				__snake_case							):
									UpperCAmelCase:					Tuple 						=     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"							)

									UpperCAmelCase:					Tuple 						=     inputs.pop("raw"	,				__snake_case							)
									if _inputs is None:
										# Remove path which will not be used from `datasets`.
										inputs.pop("path"	,				__snake_case							)
										UpperCAmelCase:					List[str] 						=     inputs.pop("array"	,				__snake_case							)
									UpperCAmelCase:					Tuple 						=     inputs.pop("sampling_rate"							)
									UpperCAmelCase:					List[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:					Dict 						=     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"							)

								UpperCAmelCase:					Optional[Any] 						=     self.feature_extractor(
								    __snake_case	,				sampling_rate=self.feature_extractor.sampling_rate	,				return_tensors="pt"							)
								return processed



							def     A__ (     self	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     self.model(**__snake_case							)
								return model_outputs




							def     A__ (     self	,				__snake_case	,				__snake_case=5							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     model_outputs.logits[0].softmax(-1							)
								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     probs.topk(__snake_case							)

								UpperCAmelCase:					Any 						=     scores.tolist()
								UpperCAmelCase:					Optional[Any] 						=     ids.tolist()

								UpperCAmelCase:					Any 						=     [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__snake_case	,				__snake_case							)]

								return labels


import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
