
'''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


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


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Union[str, Any]       =					["""input_features""", """is_longer"""]




		def __init__(				self					,						UpperCamelCase=64					,						UpperCamelCase=48_000					,						UpperCamelCase=480					,						UpperCamelCase=10					,						UpperCamelCase=1_024					,						UpperCamelCase=0.0					,						UpperCamelCase=False					,						UpperCamelCase = 0					,						UpperCamelCase = 14_000					,						UpperCamelCase = None					,						UpperCamelCase = "fusion"					,						UpperCamelCase = "repeatpad"					,						**UpperCamelCase					,						):
								super().__init__(
								    feature_size=UpperCamelCase					,						sampling_rate=UpperCamelCase					,						padding_value=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						**UpperCamelCase					,						)
								_SCREAMING_SNAKE_CASE				= top_db
								_SCREAMING_SNAKE_CASE				= truncation
								_SCREAMING_SNAKE_CASE				= padding
								_SCREAMING_SNAKE_CASE				= fft_window_size
								_SCREAMING_SNAKE_CASE				= (fft_window_size >> 1) + 1
								_SCREAMING_SNAKE_CASE				= hop_length
								_SCREAMING_SNAKE_CASE				= max_length_s
								_SCREAMING_SNAKE_CASE				= max_length_s * sampling_rate
								_SCREAMING_SNAKE_CASE				= sampling_rate
								_SCREAMING_SNAKE_CASE				= frequency_min
								_SCREAMING_SNAKE_CASE				= frequency_max
								_SCREAMING_SNAKE_CASE				= mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins					,						num_mel_filters=UpperCamelCase					,						min_frequency=UpperCamelCase					,						max_frequency=UpperCamelCase					,						sampling_rate=UpperCamelCase					,						norm=UpperCamelCase					,						mel_scale="htk"					,						)
								_SCREAMING_SNAKE_CASE				= mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins					,						num_mel_filters=UpperCamelCase					,						min_frequency=UpperCamelCase					,						max_frequency=UpperCamelCase					,						sampling_rate=UpperCamelCase					,						norm="slaney"					,						mel_scale="slaney"					,						)




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= copy.deepcopy(self.__dict__      )
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								_SCREAMING_SNAKE_CASE				= spectrogram(
								    UpperCamelCase					,						window_function(self.fft_window_size					,						"hann"      )					,						frame_length=self.fft_window_size					,						hop_length=self.hop_length					,						power=2.0					,						mel_filters=UpperCamelCase					,						log_mel="dB"					,						)
								return log_mel_spectrogram.T




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= 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
														_SCREAMING_SNAKE_CASE				= [0]
								if len(ranges[2]      ) == 0:
														# if the audio is too short, we just use the first chunk
														_SCREAMING_SNAKE_CASE				= [0]
								# randomly choose index for each part
								_SCREAMING_SNAKE_CASE				= np.random.choice(ranges[0]      )
								_SCREAMING_SNAKE_CASE				= np.random.choice(ranges[1]      )
								_SCREAMING_SNAKE_CASE				= np.random.choice(ranges[2]      )

								_SCREAMING_SNAKE_CASE				= mel[idx_front : idx_front + chunk_frames, :]
								_SCREAMING_SNAKE_CASE				= mel[idx_middle : idx_middle + chunk_frames, :]
								_SCREAMING_SNAKE_CASE				= mel[idx_back : idx_back + chunk_frames, :]

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								if waveform.shape[0] > max_length:
														if truncation == "rand_trunc":
																				_SCREAMING_SNAKE_CASE				= True
																				# random crop to max_length (for compatibility) -> this should be handled by self.pad
																				_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      ) - max_length
																				_SCREAMING_SNAKE_CASE				= np.random.randint(0					,						overflow + 1      )
																				_SCREAMING_SNAKE_CASE				= waveform[idx : idx + max_length]
																				_SCREAMING_SNAKE_CASE				= self._np_extract_fbank_features(UpperCamelCase					,						self.mel_filters_slaney      )[None, :]
														elif truncation == "fusion":
																				_SCREAMING_SNAKE_CASE				= self._np_extract_fbank_features(UpperCamelCase					,						self.mel_filters      )
																				_SCREAMING_SNAKE_CASE				= max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																				_SCREAMING_SNAKE_CASE				= 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.
																										_SCREAMING_SNAKE_CASE				= np.stack([mel, mel, mel, mel]					,						axis=0      )
																										_SCREAMING_SNAKE_CASE				= False
																				else:
																										_SCREAMING_SNAKE_CASE				= self._random_mel_fusion(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )
																										_SCREAMING_SNAKE_CASE				= True
														else:
																				raise NotImplementedError(F'data_truncating {truncation} not implemented'      )

								else:
														_SCREAMING_SNAKE_CASE				= 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":
																										_SCREAMING_SNAKE_CASE				= int(max_length / len(UpperCamelCase      )      )
																										_SCREAMING_SNAKE_CASE				= np.stack(np.tile(UpperCamelCase					,						n_repeat + 1      )      )[:max_length]
																				if padding == "repeatpad":
																										_SCREAMING_SNAKE_CASE				= int(max_length / len(UpperCamelCase      )      )
																										_SCREAMING_SNAKE_CASE				= np.stack(np.tile(UpperCamelCase					,						UpperCamelCase      )      )
																				_SCREAMING_SNAKE_CASE				= np.pad(UpperCamelCase					,						(0, max_length - waveform.shape[0])					,						mode="constant"					,						constant_values=0      )

														if truncation == "fusion":
																				_SCREAMING_SNAKE_CASE				= self._np_extract_fbank_features(UpperCamelCase					,						self.mel_filters      )
																				_SCREAMING_SNAKE_CASE				= np.stack([input_mel, input_mel, input_mel, input_mel]					,						axis=0      )
														else:
																				_SCREAMING_SNAKE_CASE				= self._np_extract_fbank_features(UpperCamelCase					,						self.mel_filters_slaney      )[None, :]

								return input_mel, longer




		def __call__(				self					,						UpperCamelCase					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= truncation if truncation is not None else self.truncation
								_SCREAMING_SNAKE_CASE				= 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."      )

								_SCREAMING_SNAKE_CASE				= isinstance(UpperCamelCase					,						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}'      )
								_SCREAMING_SNAKE_CASE				= is_batched_numpy or (
								    isinstance(UpperCamelCase					,						(list, tuple)      ) and (isinstance(raw_speech[0]					,						(np.ndarray, tuple, list)      ))
								)

								if is_batched:
														_SCREAMING_SNAKE_CASE				= [np.asarray(UpperCamelCase					,						dtype=np.floataa      ) for speech in raw_speech]
								elif not is_batched and not isinstance(UpperCamelCase					,						np.ndarray      ):
														_SCREAMING_SNAKE_CASE				= np.asarray(UpperCamelCase					,						dtype=np.floataa      )
								elif isinstance(UpperCamelCase					,						np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
														_SCREAMING_SNAKE_CASE				= raw_speech.astype(np.floataa      )

								# always return batch
								if not is_batched:
														_SCREAMING_SNAKE_CASE				= [np.asarray(UpperCamelCase      )]

								# convert to mel spectrogram, truncate and pad if needed.
								_SCREAMING_SNAKE_CASE				= [
								    self._get_input_mel(UpperCamelCase					,						max_length if max_length else self.nb_max_samples					,						UpperCamelCase					,						UpperCamelCase      )
								    for waveform in raw_speech
								]

								_SCREAMING_SNAKE_CASE				= []
								_SCREAMING_SNAKE_CASE				= []
								for mel, longer in padded_inputs:
														input_mel.append(UpperCamelCase      )
														is_longer.append(UpperCamelCase      )

								if truncation == "fusion" and sum(UpperCamelCase      ) == 0:
														# if no audio is longer than 10s, then randomly select one audio to be longer
														_SCREAMING_SNAKE_CASE				= np.random.randint(0					,						len(UpperCamelCase      )      )
														_SCREAMING_SNAKE_CASE				= True

								if isinstance(input_mel[0]					,						UpperCamelCase      ):
														_SCREAMING_SNAKE_CASE				= [np.asarray(UpperCamelCase					,						dtype=np.floataa      ) for feature in input_mel]

								# is_longer is a list of bool
								_SCREAMING_SNAKE_CASE				= [[longer] for longer in is_longer]

								_SCREAMING_SNAKE_CASE				= {"input_features": input_mel, "is_longer": is_longer}
								_SCREAMING_SNAKE_CASE				= BatchFeature(UpperCamelCase      )

								if return_tensors is not None:
														_SCREAMING_SNAKE_CASE				= input_features.convert_to_tensors(UpperCamelCase      )

								return input_features

'''simple docstring'''







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_snake_case				:   Union[str, Any]     =				"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
						raise ImportWarning(
						    """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
						    """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_snake_case				:   Tuple     =				concatenate_datasets
_snake_case				:   Any     =				DownloadConfig
_snake_case				:   List[Any]     =				DownloadManager
_snake_case				:   Union[str, Any]     =				DownloadMode
_snake_case				:   Dict     =				DownloadConfig
_snake_case				:   List[str]     =				DownloadMode
_snake_case				:   Union[str, Any]     =				DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager