


'''simple docstring'''



import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__A	=imread(R'digital_image_processing/image_data/lena_small.jpg')
__A	=cvtColor(img, COLOR_BGR2GRAY)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     cn.convert_to_negative(UpperCamelCase__					)
			# assert negative_img array for at least one True
			assert negative_img.any()







def     _UpperCamelCase      (		):
			with Image.open("""digital_image_processing/image_data/lena_small.jpg"""					) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(UpperCamelCase__ ,      1_1_0					)					).startswith(
						    """<PIL.Image.Image image mode=RGB size=100x100 at"""					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Dict        =     canny.gen_gaussian_kernel(9 ,      sigma=1.4					)
			# Assert ambiguous array
			assert resp.all()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					int        =     imread("""digital_image_processing/image_data/lena_small.jpg""" ,      0					)
			# assert ambiguous array for all == True
			assert canny_img.all()
			UpperCAmelCase__  :					List[str]        =     canny.canny(UpperCamelCase__					)
			# assert canny array for at least one True
			assert canny_array.any()







def     _UpperCamelCase      (		):
			assert gg.gaussian_filter(UpperCamelCase__ ,      5 ,      sigma=0.9					).all()







def     _UpperCamelCase      (		):
			# laplace diagonals
			UpperCAmelCase__  :					Optional[int]        =     array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]					)
			UpperCAmelCase__  :					Tuple        =     conv.img_convolve(UpperCamelCase__ ,      UpperCamelCase__					).astype(UpperCamelCase__					)
			assert res.any()







def     _UpperCamelCase      (		):
			assert med.median_filter(UpperCamelCase__ ,      3					).any()







def     _UpperCamelCase      (		):
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     sob.sobel_filter(UpperCamelCase__					)
			assert grad.any() and theta.any()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					int        =     sp.make_sepia(UpperCamelCase__ ,      2_0					)
			assert sepia.all()







def     _UpperCamelCase      (		UpperCamelCase__ = "digital_image_processing/image_data/lena_small.jpg"					):
			UpperCAmelCase__  :					Union[str, Any]        =     bs.Burkes(imread(UpperCamelCase__ ,      1					) ,      1_2_0					)
			burkes.process()
			assert burkes.output_img.any()







def     _UpperCamelCase      (		UpperCamelCase__ = "digital_image_processing/image_data/lena_small.jpg" ,      ):
			UpperCAmelCase__  :					List[str]        =     rs.NearestNeighbour(imread(UpperCamelCase__ ,      1					) ,      4_0_0 ,      2_0_0					)
			nn.process()
			assert nn.output.any()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Dict        =     """digital_image_processing/image_data/lena.jpg"""

			# Reading the image and converting it to grayscale.
			UpperCAmelCase__  :					Dict        =     imread(UpperCamelCase__ ,      0					)

			# Test for get_neighbors_pixel function() return not None
			UpperCAmelCase__  :					Optional[Any]        =     0
			UpperCAmelCase__  :					Any        =     0
			UpperCAmelCase__  :					List[str]        =     image[x_coordinate][y_coordinate]

			UpperCAmelCase__  :					str        =     lbp.get_neighbors_pixel(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			UpperCAmelCase__  :					List[str]        =     np.zeros((image.shape[0], image.shape[1])					)

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0 ,      image.shape[0]					):
						for j in range(0 ,      image.shape[1]					):
									UpperCAmelCase__  :					int        =     lbp.local_binary_value(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

			assert lbp_image.any()



'''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


__A	=logging.get_logger(__name__)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :int   =					['''input_features''', '''is_longer''']




					def __init__(      self							,							_lowerCamelCase=64							,							_lowerCamelCase=4_8000							,							_lowerCamelCase=480							,							_lowerCamelCase=10							,							_lowerCamelCase=1024							,							_lowerCamelCase=0.0							,							_lowerCamelCase=False							,							_lowerCamelCase = 0							,							_lowerCamelCase = 1_4000							,							_lowerCamelCase = None							,							_lowerCamelCase = "fusion"							,							_lowerCamelCase = "repeatpad"							,							**_lowerCamelCase							,							):
								super().__init__(
								    feature_size=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							padding_value=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							**_lowerCamelCase							,							)
								UpperCAmelCase__  :					Dict        =     top_db
								UpperCAmelCase__  :					int        =     truncation
								UpperCAmelCase__  :					Optional[Any]        =     padding
								UpperCAmelCase__  :					Union[str, Any]        =     fft_window_size
								UpperCAmelCase__  :					List[Any]        =     (fft_window_size >> 1) + 1
								UpperCAmelCase__  :					Optional[int]        =     hop_length
								UpperCAmelCase__  :					int        =     max_length_s
								UpperCAmelCase__  :					Tuple        =     max_length_s * sampling_rate
								UpperCAmelCase__  :					List[Any]        =     sampling_rate
								UpperCAmelCase__  :					Tuple        =     frequency_min
								UpperCAmelCase__  :					Dict        =     frequency_max
								UpperCAmelCase__  :					Union[str, Any]        =     mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins							,							num_mel_filters=_lowerCamelCase							,							min_frequency=_lowerCamelCase							,							max_frequency=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							norm=_lowerCamelCase							,							mel_scale="""htk"""							,							)
								UpperCAmelCase__  :					Optional[int]        =     mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins							,							num_mel_filters=_lowerCamelCase							,							min_frequency=_lowerCamelCase							,							max_frequency=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							norm="""slaney"""							,							mel_scale="""slaney"""							,							)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     copy.deepcopy(self.__dict__)
								UpperCAmelCase__  :					Optional[int]        =     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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					str        =     spectrogram(
								    _lowerCamelCase							,							window_function(self.fft_window_size							,							"""hann""")							,							frame_length=self.fft_window_size							,							hop_length=self.hop_length							,							power=2.0							,							mel_filters=_lowerCamelCase							,							log_mel="""dB"""							,							)
								return log_mel_spectrogram.T




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     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
											UpperCAmelCase__  :					List[Any]        =     [0]
								if len(ranges[2]) == 0:
											# if the audio is too short, we just use the first chunk
											UpperCAmelCase__  :					Union[str, Any]        =     [0]
								# randomly choose index for each part
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[0])
								UpperCAmelCase__  :					Optional[int]        =     np.random.choice(ranges[1])
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[2])

								UpperCAmelCase__  :					str        =     mel[idx_front : idx_front + chunk_frames, :]
								UpperCAmelCase__  :					Optional[int]        =     mel[idx_middle : idx_middle + chunk_frames, :]
								UpperCAmelCase__  :					Tuple        =     mel[idx_back : idx_back + chunk_frames, :]

								UpperCAmelCase__  :					int        =     torch.tensor(mel[None, None, :])
								UpperCAmelCase__  :					Optional[int]        =     torch.nn.functional.interpolate(
								    _lowerCamelCase							,							size=[chunk_frames, 64]							,							mode="""bilinear"""							,							align_corners=_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     mel_shrink[0][0].numpy()
								UpperCAmelCase__  :					Optional[Any]        =     np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]							,							axis=0)
								return mel_fusion




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

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

											if truncation == "fusion":
														UpperCAmelCase__  :					List[Any]        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters)
														UpperCAmelCase__  :					Dict        =     np.stack([input_mel, input_mel, input_mel, input_mel]							,							axis=0)
											else:
														UpperCAmelCase__  :					int        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters_slaney)[None, :]

								return input_mel, longer




					def __call__(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Dict        =     truncation if truncation is not None else self.truncation
								UpperCAmelCase__  :					List[Any]        =     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.""")

								UpperCAmelCase__  :					int        =     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}''')
								UpperCAmelCase__  :					List[str]        =     is_batched_numpy or (
								    isinstance(_lowerCamelCase							,							(list, tuple)) and (isinstance(raw_speech[0]							,							(np.ndarray, tuple, list)))
								)

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

								# always return batch
								if not is_batched:
											UpperCAmelCase__  :					int        =     [np.asarray(_lowerCamelCase)]

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

								UpperCAmelCase__  :					int        =     []
								UpperCAmelCase__  :					List[str]        =     []
								for mel, longer in padded_inputs:
											input_mel.append(_lowerCamelCase)
											is_longer.append(_lowerCamelCase)

								if truncation == "fusion" and sum(_lowerCamelCase) == 0:
											# if no audio is longer than 10s, then randomly select one audio to be longer
											UpperCAmelCase__  :					Optional[int]        =     np.random.randint(0							,							len(_lowerCamelCase))
											UpperCAmelCase__  :					Tuple        =     True

								if isinstance(input_mel[0]							,							_lowerCamelCase):
											UpperCAmelCase__  :					str        =     [np.asarray(_lowerCamelCase							,							dtype=np.floataa) for feature in input_mel]

								# is_longer is a list of bool
								UpperCAmelCase__  :					str        =     [[longer] for longer in is_longer]

								UpperCAmelCase__  :					Optional[int]        =     {"""input_features""": input_mel, """is_longer""": is_longer}
								UpperCAmelCase__  :					Optional[int]        =     BatchFeature(_lowerCamelCase)

								if return_tensors is not None:
											UpperCAmelCase__  :					str        =     input_features.convert_to_tensors(_lowerCamelCase)

								return input_features