





"""simple docstring"""




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a						: List[Any]     	=			logging.get_logger(__name__)


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					List[str]      					=["""input_features""", """is_longer"""]






							def __init__(   self      ,					lowerCAmelCase__=64      ,					lowerCAmelCase__=4_8000      ,					lowerCAmelCase__=480      ,					lowerCAmelCase__=10      ,					lowerCAmelCase__=1024      ,					lowerCAmelCase__=0.0      ,					lowerCAmelCase__=False      ,					lowerCAmelCase__ = 0      ,					lowerCAmelCase__ = 1_4000      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = "fusion"      ,					lowerCAmelCase__ = "repeatpad"      ,					**lowerCAmelCase__      ,					)	-> str:
								super().__init__(
								    feature_size=lowerCAmelCase__      ,					sampling_rate=lowerCAmelCase__      ,					padding_value=lowerCAmelCase__      ,					return_attention_mask=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)
								a      :		List[str]	=				top_db
								a      :		str	=				truncation
								a      :		List[Any]	=				padding
								a      :		Dict	=				fft_window_size
								a      :		Tuple	=				(fft_window_size >> 1) + 1
								a      :		Optional[Any]	=				hop_length
								a      :		List[str]	=				max_length_s
								a      :		Union[str, Any]	=				max_length_s * sampling_rate
								a      :		Optional[int]	=				sampling_rate
								a      :		List[Any]	=				frequency_min
								a      :		Dict	=				frequency_max
								a      :		int	=				mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins      ,					num_mel_filters=lowerCAmelCase__      ,					min_frequency=lowerCAmelCase__      ,					max_frequency=lowerCAmelCase__      ,					sampling_rate=lowerCAmelCase__      ,					norm=lowerCAmelCase__      ,					mel_scale="htk"      ,					)
								a      :		str	=				mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins      ,					num_mel_filters=lowerCAmelCase__      ,					min_frequency=lowerCAmelCase__      ,					max_frequency=lowerCAmelCase__      ,					sampling_rate=lowerCAmelCase__      ,					norm="slaney"      ,					mel_scale="slaney"      ,					)






							def     __a   (   self      )	-> Dict[str, Any]:
								a      :		Optional[Any]	=				copy.deepcopy(self.__dict__      )
								a      :		Dict	=				self.__class__.__name__
								if "mel_filters" in output:
									del output["mel_filters"]
								if "mel_filters_slaney" in output:
									del output["mel_filters_slaney"]
								return output






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> np.ndarray:
								a      :		Dict	=				spectrogram(
								    lowerCAmelCase__      ,					window_function(self.fft_window_size      ,					"hann"      )      ,					frame_length=self.fft_window_size      ,					hop_length=self.hop_length      ,					power=2.0      ,					mel_filters=lowerCAmelCase__      ,					log_mel="dB"      ,					)
								return log_mel_spectrogram.T






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Optional[int]:
								a      :		Optional[int]	=				np.array_split(list(range(0      ,					total_frames - chunk_frames + 1      )      )      ,					3      )
								if len(ranges[1]      ) == 0:
									# if the audio is too short, we just use the first chunk
									a      :		Any	=				[0]
								if len(ranges[2]      ) == 0:
									# if the audio is too short, we just use the first chunk
									a      :		int	=				[0]
								# randomly choose index for each part
								a      :		Optional[int]	=				np.random.choice(ranges[0]      )
								a      :		List[Any]	=				np.random.choice(ranges[1]      )
								a      :		List[str]	=				np.random.choice(ranges[2]      )

								a      :		Union[str, Any]	=				mel[idx_front : idx_front + chunk_frames, :]
								a      :		List[str]	=				mel[idx_middle : idx_middle + chunk_frames, :]
								a      :		Optional[Any]	=				mel[idx_back : idx_back + chunk_frames, :]

								a      :		Any	=				torch.tensor(mel[None, None, :]      )
								a      :		Any	=				torch.nn.functional.interpolate(
								    lowerCAmelCase__      ,					size=[chunk_frames, 64]      ,					mode="bilinear"      ,					align_corners=lowerCAmelCase__      )
								a      :		Optional[int]	=				mel_shrink[0][0].numpy()
								a      :		List[Any]	=				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,					axis=0      )
								return mel_fusion






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> np.array:
								if waveform.shape[0] > max_length:
									if truncation == "rand_trunc":
										a      :		Optional[Any]	=				True
										# random crop to max_length (for compatibility) -> this should be handled by self.pad
										a      :		Optional[int]	=				len(lowerCAmelCase__      ) - max_length
										a      :		List[str]	=				np.random.randint(0      ,					overflow + 1      )
										a      :		int	=				waveform[idx : idx + max_length]
										a      :		int	=				self._np_extract_fbank_features(lowerCAmelCase__      ,					self.mel_filters_slaney      )[None, :]
									elif truncation == "fusion":
										a      :		Optional[Any]	=				self._np_extract_fbank_features(lowerCAmelCase__      ,					self.mel_filters      )
										a      :		str	=				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
										a      :		Union[str, Any]	=				mel.shape[0]
										if chunk_frames == total_frames:
											# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
											# In this case, we just use the whole audio.
											a      :		Optional[int]	=				np.stack([mel, mel, mel, mel]      ,					axis=0      )
											a      :		str	=				False
										else:
											a      :		Optional[int]	=				self._random_mel_fusion(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )
											a      :		Tuple	=				True
									else:
										raise NotImplementedError(f"""data_truncating {truncation} not implemented"""      )

								else:
									a      :		Optional[int]	=				False
									# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
									if waveform.shape[0] < max_length:
										if padding == "repeat":
											a      :		Optional[int]	=				int(max_length / len(lowerCAmelCase__      )      )
											a      :		List[str]	=				np.stack(np.tile(lowerCAmelCase__      ,					n_repeat + 1      )      )[:max_length]
										if padding == "repeatpad":
											a      :		Union[str, Any]	=				int(max_length / len(lowerCAmelCase__      )      )
											a      :		Optional[Any]	=				np.stack(np.tile(lowerCAmelCase__      ,					lowerCAmelCase__      )      )
										a      :		Optional[int]	=				np.pad(lowerCAmelCase__      ,					(0, max_length - waveform.shape[0])      ,					mode="constant"      ,					constant_values=0      )

									if truncation == "fusion":
										a      :		List[str]	=				self._np_extract_fbank_features(lowerCAmelCase__      ,					self.mel_filters      )
										a      :		int	=				np.stack([input_mel, input_mel, input_mel, input_mel]      ,					axis=0      )
									else:
										a      :		str	=				self._np_extract_fbank_features(lowerCAmelCase__      ,					self.mel_filters_slaney      )[None, :]

								return input_mel, longer






							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> BatchFeature:
								a      :		Union[str, Any]	=				truncation if truncation is not None else self.truncation
								a      :		Dict	=				padding if padding else self.padding

								if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
										raise ValueError(
										    f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
										    f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
										    f""" was sampled with {self.sampling_rate} and not {sampling_rate}."""      )
								else:
									logger.warning(
									    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
									    "Failing to do so can result in silent errors that might be hard to debug."      )

								a      :		List[Any]	=				isinstance(lowerCAmelCase__      ,					np.ndarray      ) and len(raw_speech.shape      ) > 1
								if is_batched_numpy and len(raw_speech.shape      ) > 2:
									raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""      )
								a      :		int	=				is_batched_numpy or (
								    isinstance(lowerCAmelCase__      ,					(list, tuple)      ) and (isinstance(raw_speech[0]      ,					(np.ndarray, tuple, list)      ))
								)

								if is_batched:
									a      :		List[str]	=				[np.asarray(lowerCAmelCase__      ,					dtype=np.floataa      ) for speech in raw_speech]
								elif not is_batched and not isinstance(lowerCAmelCase__      ,					np.ndarray      ):
									a      :		int	=				np.asarray(lowerCAmelCase__      ,					dtype=np.floataa      )
								elif isinstance(lowerCAmelCase__      ,					np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
									a      :		List[Any]	=				raw_speech.astype(np.floataa      )

								# always return batch
								if not is_batched:
									a      :		int	=				[np.asarray(lowerCAmelCase__      )]

								# convert to mel spectrogram, truncate and pad if needed.
								a      :		List[Any]	=				[
								    self._get_input_mel(lowerCAmelCase__      ,					max_length if max_length else self.nb_max_samples      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )
								    for waveform in raw_speech
								]

								a      :		int	=				[]
								a      :		int	=				[]
								for mel, longer in padded_inputs:
									input_mel.append(lowerCAmelCase__      )
									is_longer.append(lowerCAmelCase__      )

								if truncation == "fusion" and sum(lowerCAmelCase__      ) == 0:
									# if no audio is longer than 10s, then randomly select one audio to be longer
									a      :		Tuple	=				np.random.randint(0      ,					len(lowerCAmelCase__      )      )
									a      :		Union[str, Any]	=				True

								if isinstance(input_mel[0]      ,					lowerCAmelCase__      ):
									a      :		Dict	=				[np.asarray(lowerCAmelCase__      ,					dtype=np.floataa      ) for feature in input_mel]

								# is_longer is a list of bool
								a      :		Optional[int]	=				[[longer] for longer in is_longer]

								a      :		Tuple	=				{"input_features": input_mel, "is_longer": is_longer}
								a      :		Any	=				BatchFeature(lowerCAmelCase__      )

								if return_tensors is not None:
									a      :		str	=				input_features.convert_to_tensors(lowerCAmelCase__      )

								return input_features










import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def __A    (      )->  Tuple:





						"""simple docstring"""



						raise RuntimeError('CUDA out of memory.'	)





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			nn.Linear(3       ,    4    )
							_UpperCAmelCase									=			nn.BatchNormad(4    )
							_UpperCAmelCase									=			nn.Linear(4       ,    5    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase    )    )    )





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()

							mock_training_loop_function()
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()
													return batch_size, arga

							_UpperCAmelCase					, _UpperCAmelCase									=			mock_training_loop_function('hello'    )
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )
							self.assertListEqual([bs, arga]       ,    [8, 'hello']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=0    )
							def mock_training_loop_function(UpperCAmelCase    ):
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													if batch_size > 0:
																			raise_fake_out_of_memory()
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													if batch_size != 8:
																			raise raise_fake_out_of_memory()

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function(128       ,    'hello'       ,    'world'    )
													self.assertIn('Batch size was passed into `f`'       ,    cm.exception.args[0]    )
													self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													raise ValueError('Oops, we had an error!'    )

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('Oops, we had an error!'       ,    cm.exception.args[0]    )

	@require_cuda
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.cuda.memory_allocated()
							_UpperCAmelCase									=			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			release_memory(UpperCAmelCase    )
							self.assertEqual(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
