


'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCAmelCase	:		Any		  =						logging.get_logger(__name__)



class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       int         =			["""input_values""", """attention_mask"""]



	def __init__(       self			,       snake_case__=1			,       snake_case__=1_6000			,       snake_case__=128			,       snake_case__=1024			,       snake_case__=0.0			,       snake_case__=True			,       snake_case__=-4.2_6_7_7_3_9_3			,       snake_case__=4.5_6_8_9_9_7_4			,       snake_case__=False			,       **snake_case__			,       ):
		super().__init__(feature_size=snake_case__			,       sampling_rate=snake_case__			,       padding_value=snake_case__			,       **snake_case__     )
		lowerCAmelCase       :     Dict   				=			num_mel_bins
		lowerCAmelCase       :     Union[str, Any]   				=			max_length
		lowerCAmelCase       :     Any   				=			do_normalize
		lowerCAmelCase       :     Any   				=			mean
		lowerCAmelCase       :     int   				=			std
		lowerCAmelCase       :     Optional[int]   				=			return_attention_mask



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       ):
		lowerCAmelCase       :     Optional[Any]   				=			torch.from_numpy(snake_case__     ).unsqueeze(0     )
		lowerCAmelCase       :     Optional[int]   				=			ta_kaldi.fbank(
		    snake_case__			,       htk_compat=snake_case__			,       sample_frequency=self.sampling_rate			,       use_energy=snake_case__			,       window_type='hanning'			,       num_mel_bins=self.num_mel_bins			,       dither=0.0			,       frame_shift=10			,       )

		lowerCAmelCase       :     Optional[int]   				=			fbank.shape[0]
		lowerCAmelCase       :     Union[str, Any]   				=			max_length - n_frames

		# pad or truncate, depending on difference
		if difference > 0:
			lowerCAmelCase       :     List[str]   				=			torch.nn.ZeroPadad((0, 0, 0, difference)     )
			lowerCAmelCase       :     Optional[int]   				=			pad_module(snake_case__     )
		elif difference < 0:
			lowerCAmelCase       :     Any   				=			fbank[0:max_length, :]

		lowerCAmelCase       :     Union[str, Any]   				=			fbank.numpy()

		return fbank



	def 					lowercase  (       self			,       snake_case__     ):
		return (input_values - (self.mean)) / (self.std * 2)



	def __call__(       self			,       snake_case__			,       snake_case__ = None			,       snake_case__ = None			,       **snake_case__			,       ):

		if sampling_rate is not None:
			if sampling_rate != self.sampling_rate:
				raise ValueError(
				    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
				    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
				    f" {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.'     )

		lowerCAmelCase       :     Optional[Any]   				=			isinstance(snake_case__			,       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}"     )
		lowerCAmelCase       :     Optional[Any]   				=			is_batched_numpy or (
		    isinstance(snake_case__			,       (list, tuple)     ) and (isinstance(raw_speech[0]			,       (np.ndarray, tuple, list)     ))
		)

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

		# always return batch
		if not is_batched:
			lowerCAmelCase       :     Optional[Any]   				=			[raw_speech]

		# extract fbank features and pad/truncate to max_length
		lowerCAmelCase       :     Optional[int]   				=			[self._extract_fbank_features(snake_case__			,       max_length=self.max_length     ) for waveform in raw_speech]

		# convert into BatchFeature
		lowerCAmelCase       :     List[Any]   				=			BatchFeature({'input_values': features}     )

		# make sure list is in array format
		lowerCAmelCase       :     Dict   				=			padded_inputs.get('input_values'     )
		if isinstance(input_values[0]			,       snake_case__     ):
			lowerCAmelCase       :     Optional[int]   				=			[np.asarray(snake_case__			,       dtype=np.floataa     ) for feature in input_values]

		# normalization
		if self.do_normalize:
			lowerCAmelCase       :     Dict   				=			[self.normalize(snake_case__     ) for feature in input_values]

		if return_tensors is not None:
			lowerCAmelCase       :     Tuple   				=			padded_inputs.convert_to_tensors(snake_case__     )

		return padded_inputs




'''simple docstring'''




import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax



@require_flax
class 			lowerCAmelCase					:



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Union[str, Any]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Any   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     Tuple   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     int   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     List[str]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Optional[int]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Tuple   				=			16
		lowerCAmelCase       :     Dict   				=			(16, 32)

		lowerCAmelCase       :     Dict   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     int   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Any   				=			'kwargs' in inspect.signature(self.model_class.__init__     ).parameters
		lowerCAmelCase       :     Optional[Any]   				=			len(self.model_class._deprecated_kwargs     ) > 0

		if has_kwarg_in_model_class and not has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
			    ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
			    ' [<deprecated_argument>]`'     )

		if not has_kwarg_in_model_class and has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
			    f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
			    ' from `_deprecated_kwargs = [<deprecated_argument>]`'     )
