

class      snake_case_      :


							"""simple docstring"""





							def __init__(			self       ,    _A				):
												__lowerCAmelCase       =				data
												__lowerCAmelCase       =				None
												__lowerCAmelCase       =				None


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					if node is None:
										__lowerCAmelCase       =				BinaryTreeNode(UpperCAmelCase__   )
										return node

					# binary search tree is not empty,
					# so we will insert it into the tree
					# if new_value is less than value of data in node,
					#  add it to left subtree and proceed recursively
					if new_value < node.data:
										__lowerCAmelCase       =				insert(node.left_child		,       UpperCAmelCase__   )
					else:
										# if new_value is greater than value of data in node,
										#  add it to right subtree and proceed recursively
										__lowerCAmelCase       =				insert(node.right_child		,       UpperCAmelCase__   )
					return node


def 							__lowercase     (	UpperCAmelCase__   ):  # if node is None,return

					"""simple docstring"""






					if node:
										__lowerCAmelCase       =				inorder(node.left_child   )
										__lowerCAmelCase       =				[*inorder_array, node.data]
										__lowerCAmelCase       =				inorder_array + inorder(node.right_child   )
					else:
										__lowerCAmelCase       =				[]
					return inorder_array


def 							__lowercase     (	):

					"""simple docstring"""






					__lowerCAmelCase       =				insert(UpperCAmelCase__		,       15   )
					insert(UpperCAmelCase__		,       10   )
					insert(UpperCAmelCase__		,       25   )
					insert(UpperCAmelCase__		,       6   )
					insert(UpperCAmelCase__		,       14   )
					insert(UpperCAmelCase__		,       20   )
					insert(UpperCAmelCase__		,       60   )
					return root


def 							__lowercase     (	):

					"""simple docstring"""






					__lowerCAmelCase       =				make_tree()
					print('Printing values of binary search tree in Inorder Traversal.'   )
					inorder(UpperCAmelCase__   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()









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 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""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(_a				)
class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    *_A       ,    **_A				):
												# Default, might be overriden by the model.config.
												__lowerCAmelCase       =				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       ,    _A       ,    **_A       ,    ):
												return super().__call__(_A       ,    **_A				)




							def 							A__	(			self       ,    _A=None       ,    **_A				):
												# 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 							A__	(			self       ,    _A				):
												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
																						__lowerCAmelCase       =				requests.get(_A				).content
																	else:
																						with open(_A       ,    'rb'				) as f:
																											__lowerCAmelCase       =				f.read()

												if isinstance(_A       ,    _A				):
																	__lowerCAmelCase       =				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'				)

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

												__lowerCAmelCase       =				self.feature_extractor(
												    _A       ,    sampling_rate=self.feature_extractor.sampling_rate       ,    return_tensors='pt'				)
												return processed




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				self.model(**_A				)
												return model_outputs




							def 							A__	(			self       ,    _A       ,    _A=5				):
												__lowerCAmelCase       =				model_outputs.logits[0].softmax(-1				)
												__lowerCAmelCase,						__lowerCAmelCase       =				probs.topk(_A				)

												__lowerCAmelCase       =				scores.tolist()
												__lowerCAmelCase       =				ids.tolist()

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

												return labels






