


from math import ceil
from typing import List, Optional, Union

import numpy as np

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


__lowerCAmelCase	: Dict  				=logging.get_logger(__name__)
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Union[str, Any]						=		['''audio_values''', '''audio_mask''']


				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:str=2_048			,			lowerCAmelCase__			:str=1			,			lowerCAmelCase__			:List[Any]=[16, 16]			,			lowerCAmelCase__			:List[str]=128			,			lowerCAmelCase__			:Dict=44_100			,			lowerCAmelCase__			:Tuple=86			,			lowerCAmelCase__			:List[str]=2_048			,			lowerCAmelCase__			:Union[str, Any]=0.0			,			**lowerCAmelCase__			:int			,			)		->			str:
										super().__init__(
										    feature_size=lowerCAmelCase__			,			sampling_rate=lowerCAmelCase__			,			padding_value=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		spectrogram_length
										__SCREAMING_SNAKE_CASE						:     Dict					=		num_channels
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		patch_size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		feature_size // self.patch_size[1]
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		n_fft
										__SCREAMING_SNAKE_CASE						:     int					=		sampling_rate // hop_length_to_sampling_rate
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		sampling_rate
										__SCREAMING_SNAKE_CASE						:     Tuple					=		padding_value
										__SCREAMING_SNAKE_CASE						:     Dict					=		mel_filter_bank(
										    num_frequency_bins=1 + n_fft // 2			,			num_mel_filters=lowerCAmelCase__			,			min_frequency=0.0			,			max_frequency=2_2050.0			,			sampling_rate=lowerCAmelCase__			,			norm='''slaney'''			,			mel_scale='''slaney'''			,			).T


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:np.array	)		->			np.ndarray:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		spectrogram(
										    lowerCAmelCase__			,			window_function(self.n_fft			,			'''hann'''	)			,			frame_length=self.n_fft			,			hop_length=self.hop_length			,			power=2.0			,			mel_filters=self.mel_filters.T			,			log_mel='''dB'''			,			db_range=80.0			,			)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		log_spec[:, :-1]
										__SCREAMING_SNAKE_CASE						:     Tuple					=		log_spec - 20.0
										__SCREAMING_SNAKE_CASE						:     List[str]					=		np.clip(log_spec / 40.0			,			-2.0			,			0.0	) + 1.0
										return log_spec



				def __call__(       self			:Dict			,			lowerCAmelCase__			:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,			lowerCAmelCase__			:Optional[Union[str, TensorType]] = None			,			lowerCAmelCase__			:Optional[bool] = True			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			**lowerCAmelCase__			:Union[str, Any]			,			)		->			BatchFeature:

										if sampling_rate is not None:
																if sampling_rate != self.sampling_rate:
																						raise ValueError(
																						    '''This feature extractor is set to support sampling rate'''
																						    f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
																						    f''' 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						:     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}'''	)
										__SCREAMING_SNAKE_CASE						:     str					=		is_batched_numpy or (
										    isinstance(lowerCAmelCase__			,			(list, tuple)	) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)	))
										)
										if is_batched:
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[np.asarray([speech]			,			dtype=np.floataa	).T for speech in raw_speech]
										elif not is_batched and not isinstance(lowerCAmelCase__			,			np.ndarray	):
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		np.asarray(lowerCAmelCase__			,			dtype=np.floataa	)
										elif isinstance(lowerCAmelCase__			,			np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		raw_speech.astype(np.floataa	)
										# always return batch
										if not is_batched:
																__SCREAMING_SNAKE_CASE						:     str					=		[np.asarray([raw_speech]	).T]

										# Convert audio signals to log mel spectrograms, truncate by time axis
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[
										    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
										]
										if isinstance(audio_features[0]			,			lowerCAmelCase__	):
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[np.asarray(lowerCAmelCase__			,			dtype=np.floataa	) for feature in audio_features]

										# Create audio attention mask
										__SCREAMING_SNAKE_CASE						:     Tuple					=		max(
										    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
										if return_attention_mask:
																__SCREAMING_SNAKE_CASE						:     Dict					=		[
																    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
																    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
																    for feature in audio_features
																]
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.array(lowerCAmelCase__	).astype(np.floataa	)

										# convert into correct format for padding
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
										__SCREAMING_SNAKE_CASE						:     List[str]					=		np.ones([len(lowerCAmelCase__	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		padded_audio_features * self.padding_value
										for i in range(len(lowerCAmelCase__	)	):
																__SCREAMING_SNAKE_CASE						:     Dict					=		audio_features[i]
																__SCREAMING_SNAKE_CASE						:     str					=		feature

										# return as BatchFeature
										if return_attention_mask:
																__SCREAMING_SNAKE_CASE						:     Dict					=		{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
										else:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		{'''audio_values''': padded_audio_features}

										__SCREAMING_SNAKE_CASE						:     Any					=		BatchFeature(data=lowerCAmelCase__			,			tensor_type=lowerCAmelCase__	)
										return encoded_inputs

import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''		,  [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''		,  num_bytes=1_337		,  num_examples=42		,  dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''		,  num_bytes=1_337		,  num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]		,  )
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							split_dict._to_yaml_list()
						assert len(_lowercase						) == len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							SplitDict._from_yaml_list(_lowercase						)
						for split_name, split_info in split_dict.items():
												# dataset_name field is deprecated, and is therefore not part of the YAML dump
												SCREAMING_SNAKE_CASE							:    Any							=							None
												# the split name of split_dict takes over the name of the split info object
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							split_name
						assert split_dict == reloaded



@pytest.mark.parametrize(
    '''split_info'''		,  [SplitInfo(), SplitInfo(dataset_name=_lowercase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def       A	(    _lowercase						):
						# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
						# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
						SCREAMING_SNAKE_CASE							:    List[Any]							=							asdict(SplitDict({'''train''': split_info}						)						)
						assert "dataset_name" in split_dict_asdict["train"]
						assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





