
"""simple docstring"""







from typing import List, Optional, Union

import numpy as np

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


lowerCamelCase__        =		logging.get_logger(__name__)




class 			A__	(     _lowerCamelCase):
		A_						:	str           =     ['input_features', 'attention_mask']

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=80	,		_SCREAMING_SNAKE_CASE=1_60_00	,		_SCREAMING_SNAKE_CASE=0.0	,		_SCREAMING_SNAKE_CASE=10	,		_SCREAMING_SNAKE_CASE=25	,		_SCREAMING_SNAKE_CASE="hamming_window"	,		_SCREAMING_SNAKE_CASE=3_2768.0	,		_SCREAMING_SNAKE_CASE=0.97	,		_SCREAMING_SNAKE_CASE=1.0	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=False	,		**_SCREAMING_SNAKE_CASE	,		):
			super().__init__(feature_size=_SCREAMING_SNAKE_CASE	,		sampling_rate=_SCREAMING_SNAKE_CASE	,		padding_value=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Tuple						      =				feature_size
			__lowerCAmelCase      :						List[Any]						      =				sampling_rate
			__lowerCAmelCase      :						Dict						      =				padding_value
			__lowerCAmelCase      :						int						      =				hop_length
			__lowerCAmelCase      :						Optional[Any]						      =				win_length
			__lowerCAmelCase      :						Optional[Any]						      =				frame_signal_scale
			__lowerCAmelCase      :						Union[str, Any]						      =				preemphasis_coeff
			__lowerCAmelCase      :						Optional[int]						      =				mel_floor
			__lowerCAmelCase      :						Optional[Any]						      =				normalize_means
			__lowerCAmelCase      :						Union[str, Any]						      =				normalize_vars
			__lowerCAmelCase      :						List[Any]						      =				win_function
			__lowerCAmelCase      :						Tuple						      =				return_attention_mask

			__lowerCAmelCase      :						Dict						      =				win_length * sampling_rate // 10_00
			__lowerCAmelCase      :						str						      =				hop_length * sampling_rate // 10_00

			__lowerCAmelCase      :						Union[str, Any]						      =				optimal_fft_length(self.sample_size			)
			__lowerCAmelCase      :						List[str]						      =				(self.n_fft // 2) + 1

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if self.win_function == "hamming_window":
				__lowerCAmelCase      :						Optional[Any]						      =				window_function(window_length=self.sample_size	,		name=self.win_function	,		periodic=_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						List[str]						      =				window_function(window_length=self.sample_size	,		name=self.win_function			)

			__lowerCAmelCase      :						Union[str, Any]						      =				mel_filter_bank(
			    num_frequency_bins=self.n_freqs	,		num_mel_filters=self.feature_size	,		min_frequency=0.0	,		max_frequency=self.sampling_rate / 2.0	,		sampling_rate=self.sampling_rate	,		)

			__lowerCAmelCase      :						int						      =				spectrogram(
			    one_waveform * self.frame_signal_scale	,		window=_SCREAMING_SNAKE_CASE	,		frame_length=self.sample_size	,		hop_length=self.sample_stride	,		fft_length=self.n_fft	,		center=_SCREAMING_SNAKE_CASE	,		preemphasis=self.preemphasis_coeff	,		mel_filters=_SCREAMING_SNAKE_CASE	,		mel_floor=self.mel_floor	,		log_mel='log'	,		)
			return msfc_features.T

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			# make sure we normalize float32 arrays
			if self.normalize_means:
				__lowerCAmelCase      :						Tuple						      =				x[:input_length].mean(axis=0			)
				__lowerCAmelCase      :						str						      =				np.subtract(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			if self.normalize_vars:
				__lowerCAmelCase      :						Optional[int]						      =				x[:input_length].std(axis=0			)
				__lowerCAmelCase      :						List[str]						      =				np.divide(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			if input_length < x.shape[0]:
				__lowerCAmelCase      :						Dict						      =				padding_value

			# make sure array is in float32
			__lowerCAmelCase      :						Dict						      =				x.astype(np.floataa			)

			return x

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None			):
			__lowerCAmelCase      :						Any						      =				attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
			return [self._normalize_one(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		self.padding_value			) for x, n in zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)]







		def __call__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_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      :						Tuple						      =				isinstance(_SCREAMING_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      :						Tuple						      =				is_batched_numpy or (
			    isinstance(_SCREAMING_SNAKE_CASE	,		(list, tuple)			) and (isinstance(raw_speech[0]	,		(np.ndarray, tuple, list)			))
			)

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

			# always return batch
			if not is_batched:
				__lowerCAmelCase      :						Union[str, Any]						      =				[raw_speech]

			# extract fbank features
			__lowerCAmelCase      :						Tuple						      =				[self._extract_mfsc_features(_SCREAMING_SNAKE_CASE			) for one_waveform in raw_speech]

			# convert into correct format for padding
			__lowerCAmelCase      :						str						      =				BatchFeature({'input_features': features}			)

			__lowerCAmelCase      :						str						      =				self.pad(
			    _SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE	,		pad_to_multiple_of=_SCREAMING_SNAKE_CASE	,		return_attention_mask=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
			# make sure list is in array format
			__lowerCAmelCase      :						Tuple						      =				padded_inputs.get('input_features'			)
			if isinstance(input_features[0]	,		_SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase      :						int						      =				[np.asarray(_SCREAMING_SNAKE_CASE	,		dtype=np.floataa			) for feature in input_features]

			__lowerCAmelCase      :						Any						      =				padded_inputs.get('attention_mask'			)
			if attention_mask is not None:
				__lowerCAmelCase      :						List[Any]						      =				[np.asarray(_SCREAMING_SNAKE_CASE	,		dtype=np.intaa			) for array in attention_mask]

			if self.normalize_means or self.normalize_vars:
				__lowerCAmelCase      :						List[Any]						      =				(
				    np.array(_SCREAMING_SNAKE_CASE	,		dtype=np.intaa			)
				    if self._get_padding_strategies(_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE			) is not PaddingStrategy.DO_NOT_PAD
				    and padding
				    else None
				)
				__lowerCAmelCase      :						Dict						      =				self.normalize(
				    padded_inputs['input_features']	,		attention_mask=_SCREAMING_SNAKE_CASE			)

			if return_tensors is not None:
				__lowerCAmelCase      :						Union[str, Any]						      =				padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE			)

			return padded_inputs

"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)