
'''simple docstring'''







import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


_lowercase			:  Any   							=	logging.get_logger(__name__)






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					['''input_values''', '''attention_mask''']






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 1    ,							__SCREAMING_SNAKE_CASE = 1_60_00    ,							__SCREAMING_SNAKE_CASE = 0.0    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = 80    ,							__SCREAMING_SNAKE_CASE = 16    ,							__SCREAMING_SNAKE_CASE = 64    ,							__SCREAMING_SNAKE_CASE = "hann_window"    ,							__SCREAMING_SNAKE_CASE = 1.0    ,							__SCREAMING_SNAKE_CASE = 80    ,							__SCREAMING_SNAKE_CASE = 76_00    ,							__SCREAMING_SNAKE_CASE = 1E-1_0    ,							__SCREAMING_SNAKE_CASE = 2    ,							__SCREAMING_SNAKE_CASE = True    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											super().__init__(feature_size=__SCREAMING_SNAKE_CASE    ,							sampling_rate=__SCREAMING_SNAKE_CASE    ,							padding_value=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	do_normalize
											lowercase_     :					Union[str, Any]													=	return_attention_mask

											lowercase_     :					Union[str, Any]													=	num_mel_bins
											lowercase_     :					List[Any]													=	hop_length
											lowercase_     :					str													=	win_length
											lowercase_     :					Union[str, Any]													=	win_function
											lowercase_     :					Union[str, Any]													=	frame_signal_scale
											lowercase_     :					Optional[int]													=	fmin
											lowercase_     :					Optional[Any]													=	fmax
											lowercase_     :					Dict													=	mel_floor
											lowercase_     :					Any													=	reduction_factor

											lowercase_     :					str													=	win_length * sampling_rate // 10_00
											lowercase_     :					Dict													=	hop_length * sampling_rate // 10_00
											lowercase_     :					int													=	optimal_fft_length(self.sample_size		)
											lowercase_     :					int													=	(self.n_fft // 2) + 1

											lowercase_     :					Optional[int]													=	window_function(window_length=self.sample_size    ,							name=self.win_function    ,							periodic=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Dict													=	mel_filter_bank(
											    num_frequency_bins=self.n_freqs    ,							num_mel_filters=self.num_mel_bins    ,							min_frequency=self.fmin    ,							max_frequency=self.fmax    ,							sampling_rate=self.sampling_rate    ,							norm='''slaney'''    ,							mel_scale='''slaney'''    ,							)

											if frame_signal_scale != 1.0:
																	warnings.warn(
																	    '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''    ,							__SCREAMING_SNAKE_CASE    ,							)
											if reduction_factor != 2.0:
																	warnings.warn(
																	    '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''    ,							__SCREAMING_SNAKE_CASE    ,							)






					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def      _snake_case   (       __SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 0.0		):


											"""simple docstring"""





											if attention_mask is not None:
																	lowercase_     :					List[Any]													=	np.array(__SCREAMING_SNAKE_CASE    ,							np.intaa		)
																	lowercase_     :					Union[str, Any]													=	[]

																	for vector, length in zip(__SCREAMING_SNAKE_CASE    ,							attention_mask.sum(-1		)		):
																							lowercase_     :					Dict													=	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7		)
																							if length < normed_slice.shape[0]:
																													lowercase_     :					Any													=	padding_value

																							normed_input_values.append(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_     :					Optional[Any]													=	[(x - x.mean()) / np.sqrt(x.var() + 1E-7		) for x in input_values]

											return normed_input_values






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Dict													=	spectrogram(
											    __SCREAMING_SNAKE_CASE    ,							window=self.window    ,							frame_length=self.sample_size    ,							hop_length=self.sample_stride    ,							fft_length=self.n_fft    ,							mel_filters=self.mel_filters    ,							mel_floor=self.mel_floor    ,							log_mel='''log10'''    ,							)
											return log_mel_spec.T






					def __call__(       self    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__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    ,							):


											"""simple docstring"""





											if audio is None and audio_target is None:
																	raise ValueError('''You must provide either `audio` or `audio_target` values.'''		)

											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 audio 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.'''		)

											if audio is not None:
																	lowercase_     :					Any													=	self._process_audio(
																	    __SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							)
											else:
																	lowercase_     :					Union[str, Any]													=	None

											if audio_target is not None:
																	lowercase_     :					int													=	self._process_audio(
																	    __SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							)

																	if inputs is None:
																							return inputs_target
																	else:
																							lowercase_     :					Optional[Any]													=	inputs_target['''input_values''']
																							lowercase_     :					Any													=	inputs_target.get('''attention_mask'''		)
																							if decoder_attention_mask is not None:
																													lowercase_     :					int													=	decoder_attention_mask

											return inputs






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = False    ,							__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    ,							):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray		) and len(speech.shape		) > 1
											if is_batched_numpy and len(speech.shape		) > 2:
																	raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''		)
											lowercase_     :					Optional[int]													=	is_batched_numpy or (
											    isinstance(__SCREAMING_SNAKE_CASE    ,							(list, tuple)		) and (isinstance(speech[0]    ,							(np.ndarray, tuple, list)		))
											)

											if is_batched:
																	lowercase_     :					Tuple													=	[np.asarray(__SCREAMING_SNAKE_CASE    ,							dtype=np.floataa		) for speech in speech]
											elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray		):
																	lowercase_     :					Tuple													=	np.asarray(__SCREAMING_SNAKE_CASE    ,							dtype=np.floataa		)
											elif isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
																	lowercase_     :					List[str]													=	speech.astype(np.floataa		)

											# always return batch
											if not is_batched:
																	lowercase_     :					Union[str, Any]													=	[speech]

											# needed to make pad() work on spectrogram inputs
											lowercase_     :					List[Any]													=	self.feature_size

											# convert into correct format for padding
											if is_target:
																	lowercase_     :					Optional[int]													=	[self._extract_mel_features(__SCREAMING_SNAKE_CASE		) for waveform in speech]
																	lowercase_     :					Optional[int]													=	BatchFeature({'''input_values''': features}		)
																	lowercase_     :					Optional[Any]													=	self.num_mel_bins
											else:
																	lowercase_     :					int													=	BatchFeature({'''input_values''': speech}		)

											lowercase_     :					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    ,							)

											lowercase_     :					Dict													=	feature_size_hack

											# convert input values to correct format
											lowercase_     :					Union[str, Any]													=	padded_inputs['''input_values''']
											if not isinstance(input_values[0]    ,							np.ndarray		):
																	lowercase_     :					int													=	[np.asarray(__SCREAMING_SNAKE_CASE    ,							dtype=np.floataa		) for array in input_values]
											elif (
											    not isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray		)
											    and isinstance(input_values[0]    ,							np.ndarray		)
											    and input_values[0].dtype is np.dtype(np.floataa		)
											):
																	lowercase_     :					Union[str, Any]													=	[array.astype(np.floataa		) for array in input_values]
											elif isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
																	lowercase_     :					List[str]													=	input_values.astype(np.floataa		)

											# convert attention_mask to correct format
											lowercase_     :					Optional[int]													=	padded_inputs.get('''attention_mask'''		)
											if attention_mask is not None:
																	lowercase_     :					List[str]													=	[np.asarray(__SCREAMING_SNAKE_CASE    ,							dtype=np.intaa		) for array in attention_mask]

											# zero-mean and unit-variance normalization
											if not is_target and self.do_normalize:
																	lowercase_     :					Optional[int]													=	(
																	    attention_mask
																	    if self._get_padding_strategies(__SCREAMING_SNAKE_CASE    ,							max_length=__SCREAMING_SNAKE_CASE		) is not PaddingStrategy.DO_NOT_PAD
																	    else None
																	)
																	lowercase_     :					Any													=	self.zero_mean_unit_var_norm(
																	    padded_inputs['''input_values''']    ,							attention_mask=__SCREAMING_SNAKE_CASE    ,							padding_value=self.padding_value		)

											if return_tensors is not None:
																	lowercase_     :					str													=	padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE		)

											return padded_inputs






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	super().to_dict()

											# Don't serialize these as they are derived from the other properties.
											lowercase_     :					Union[str, Any]													=	['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
											for name in names:
																	if name in output:
																							del output[name]

											return output





'''simple docstring'''







from PIL import Image
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Image				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	(259 * (level + 255)) / (255 * (259 - level))

						def contrast(__SCREAMING_SNAKE_CASE :			int							) -> int:
												return int(128 + factor * (c - 128)							)

						return img.point(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change contrast to 170
														_lowercase			:  Union[str, Any]   							=	change_contrast(img, 1_7_0)
														cont_img.save("image_data/lena_high_contrast.png", format="png")



