






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

a_							=      TypeVar('''T''', bound=bool)


class        lowercase__       (     Generic[T]		):

	def __init__(							self						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  value
							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  None

	@property
	def 						UpperCAmelCase					(							self    )->   T:







							'''simple docstring'''





							return self._value


	@staticmethod
	def 						UpperCAmelCase					(							__UpperCAmelCase						,      __UpperCAmelCase    )->   RandomizedHeapNode[T] | None:







							'''simple docstring'''





							if not roota:
													return roota

							if not roota:
													return roota

							if roota.value > roota.value:
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  roota, roota

							if random.choice([True, False]    ):
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  roota.right, roota.left

							lowerCAmelCase__	 =  RandomizedHeapNode.merge(roota.left						,      __UpperCAmelCase    )

							return roota





class        lowercase__       (     Generic[T]		):

	def __init__(							self						,      __UpperCAmelCase = ()    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  None

							if data:
													for item in data:
																			self.insert(__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  RandomizedHeapNode.merge(self._root						,      RandomizedHeapNode(__UpperCAmelCase    )    )

	def 						UpperCAmelCase					(							self    )->   T | None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.top()

							if self._root is None:
													return None

							lowerCAmelCase__	 =  RandomizedHeapNode.merge(self._root.left						,      self._root.right    )

							return result

	def 						UpperCAmelCase					(							self    )->   T:







							'''simple docstring'''





							if not self._root:
													raise IndexError("Can't get top element for the empty heap."    )
							return self._root.value

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  None

	def 						UpperCAmelCase					(							self    )->   list[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  []
							while self:
													result.append(self.pop()    )

							return result


	def __bool__(							self    )->   bool:







							'''simple docstring'''





							return self._root is not None


if __name__ == "__main__":
	import doctest

	doctest.testmod()














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				)	->				np.array:
						"""simple docstring"""







						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(UpperCamelCase_      ,				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(UpperCamelCase_				)
						lowerCAmelCase__	 =  output_stream[0]

						lowerCAmelCase__	 =  np.frombuffer(UpperCamelCase_      ,				np.floataa				)
						if audio.shape[0] == 0:
												raise ValueError("Malformed soundfile"				)
						return audio





@add_end_docstrings(_UpperCAmelCase		)
class        lowercase__       (     _UpperCAmelCase		):

	def __init__(							self						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  5
							super().__init__(*__UpperCAmelCase						,      **__UpperCAmelCase    )

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

							self.check_model_type(__UpperCAmelCase    )

	def __call__(							self						,      __UpperCAmelCase						,      **__UpperCAmelCase						,      )->   List[str]:







							'''simple docstring'''





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

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Dict:







							'''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 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   str:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													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(__UpperCAmelCase    ).content
													else:
																			with open(__UpperCAmelCase						,      "rb"    ) as f:
																									lowerCAmelCase__	 =  f.read()

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													lowerCAmelCase__	 =  ffmpeg_read(__UpperCAmelCase						,      self.feature_extractor.sampling_rate    )

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													# 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"						,      __UpperCAmelCase    )
													if _inputs is None:
																			# Remove path which will not be used from `datasets`.
																			inputs.pop("path"						,      __UpperCAmelCase    )
																			lowerCAmelCase__	 =  inputs.pop("array"						,      __UpperCAmelCase    )
													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(__UpperCAmelCase    )						,      __UpperCAmelCase						,      self.feature_extractor.sampling_rate    ).numpy()

							if not isinstance(__UpperCAmelCase						,      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(
							    __UpperCAmelCase						,      sampling_rate=self.feature_extractor.sampling_rate						,      return_tensors="pt"    )
							return processed

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model(**__UpperCAmelCase    )
							return model_outputs


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=5    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  model_outputs.logits[0].softmax(-1    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  probs.topk(__UpperCAmelCase    )

							lowerCAmelCase__	 =  scores.tolist()
							lowerCAmelCase__	 =  ids.tolist()

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

							return labels






