






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


__snake_case    :    List[str]         =     logging.get_logger(__name__)


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Any   =					['''audio_values''', '''audio_mask''']






						def __init__(	self     ,						_UpperCamelCase=20_48     ,						_UpperCamelCase=1     ,						_UpperCamelCase=[16, 16]     ,						_UpperCamelCase=1_28     ,						_UpperCamelCase=4_41_00     ,						_UpperCamelCase=86     ,						_UpperCamelCase=20_48     ,						_UpperCamelCase=0.0     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(
												    feature_size=A__     ,						sampling_rate=A__     ,						padding_value=A__     ,						**A__     ,						)

												lowerCAmelCase__ 						=						spectrogram_length
												lowerCAmelCase__ 						=						num_channels
												lowerCAmelCase__ 						=						patch_size
												lowerCAmelCase__ 						=						feature_size // self.patch_size[1]
												lowerCAmelCase__ 						=						n_fft
												lowerCAmelCase__ 						=						sampling_rate // hop_length_to_sampling_rate
												lowerCAmelCase__ 						=						sampling_rate
												lowerCAmelCase__ 						=						padding_value
												lowerCAmelCase__ 						=						mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2     ,						num_mel_filters=A__     ,						min_frequency=0.0     ,						max_frequency=2_20_50.0     ,						sampling_rate=A__     ,						norm='slaney'     ,						mel_scale='slaney'     ,						).T






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						spectrogram(
												    A__     ,						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     ,						)
												lowerCAmelCase__ 						=						log_spec[:, :-1]
												lowerCAmelCase__ 						=						log_spec - 20.0
												lowerCAmelCase__ 						=						np.clip(log_spec / 40.0     ,						-2.0     ,						0.0   ) + 1.0
												return log_spec







						def __call__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = True     ,						_UpperCamelCase = None     ,						_UpperCamelCase = False     ,						_UpperCamelCase = False     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""

												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.'   )

												lowerCAmelCase__ 						=						isinstance(A__     ,						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__ 						=						is_batched_numpy or (
												    isinstance(A__     ,						(list, tuple)   ) and (isinstance(raw_speech[0]     ,						(np.ndarray, tuple, list)   ))
												)
												if is_batched:
																		lowerCAmelCase__ 						=						[np.asarray([speech]     ,						dtype=np.floataa   ).T for speech in raw_speech]
												elif not is_batched and not isinstance(A__     ,						np.ndarray   ):
																		lowerCAmelCase__ 						=						np.asarray(A__     ,						dtype=np.floataa   )
												elif isinstance(A__     ,						np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
																		lowerCAmelCase__ 						=						raw_speech.astype(np.floataa   )
												# always return batch
												if not is_batched:
																		lowerCAmelCase__ 						=						[np.asarray([raw_speech]   ).T]

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

												# Create audio attention mask
												lowerCAmelCase__ 						=						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:
																		lowerCAmelCase__ 						=						[
																		    (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
																		]
																		lowerCAmelCase__ 						=						np.array(A__   ).astype(np.floataa   )

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

												# return as BatchFeature
												if return_attention_mask:
																		lowerCAmelCase__ 						=						{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
												else:
																		lowerCAmelCase__ 						=						{"""audio_values""": padded_audio_features}

												lowerCAmelCase__ 						=						BatchFeature(data=A__     ,						tensor_type=A__   )
												return encoded_inputs













import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataset(UpperCamelCase_    ,       UpperCamelCase_		)
						for index in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						random.sample(range(len(UpperCamelCase_		)		)    ,       4		)
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						update_image_and_anno(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       filter_scale=UpperCamelCase_    ,       )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__    						=						random_chars(32		)
												lowerCAmelCase__    						=						path.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												lowerCAmelCase__    						=						F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
												cva.imwrite(F"{file_root}.jpg"    ,       UpperCamelCase_    ,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
												print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"		)
												lowerCAmelCase__    						=						[]
												for anno in new_annos:
																		lowerCAmelCase__    						=						anno[3] - anno[1]
																		lowerCAmelCase__    						=						anno[4] - anno[2]
																		lowerCAmelCase__    						=						anno[1] + width / 2
																		lowerCAmelCase__    						=						anno[2] + height / 2
																		lowerCAmelCase__    						=						F"{anno[0]} {x_center} {y_center} {width} {height}"
																		annos_list.append(UpperCamelCase_		)
												with open(F"{file_root}.txt"    ,       'w'		) as outfile:
																		outfile.write('\n'.join(line for line in annos_list		)		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   tuple[list, list]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for label_file in glob.glob(os.path.join(UpperCamelCase_    ,       '*.txt'		)		):
												lowerCAmelCase__    						=						label_file.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												with open(UpperCamelCase_		) as in_file:
																		lowerCAmelCase__    						=						in_file.readlines()
												lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       F"{label_name}.jpg"		)

												lowerCAmelCase__    						=						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__    						=						obj_list.rstrip('\n'		).split(' '		)
																		lowerCAmelCase__    						=						float(obj[1]		) - float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) - float(obj[4]		) / 2
																		lowerCAmelCase__    						=						float(obj[1]		) + float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) + float(obj[4]		) / 2

																		boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
												if not boxes:
																		continue
												img_paths.append(UpperCamelCase_		)
												labels.append(UpperCamelCase_		)
						return img_paths, labels







def 	_UpperCamelCase   (    UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					tuple[int, int]    ,       UpperCamelCase_       :					tuple[float, float]    ,       UpperCamelCase_       :					float = 0.0    ,       )  ->   tuple[list, list, str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros([output_size[0], output_size[1], 3]    ,       dtype=np.uinta		)
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						int(scale_x * output_size[1]		)
						lowerCAmelCase__    						=						int(scale_y * output_size[0]		)

						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for i, index in enumerate(UpperCamelCase_		):
												lowerCAmelCase__    						=						all_img_list[index]
												path_list.append(UpperCamelCase_		)
												lowerCAmelCase__    						=						all_annos[index]
												lowerCAmelCase__    						=						cva.imread(UpperCamelCase_		)
												if i == 0:  # top-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 1:  # top-right
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (output_size[1] - divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 2:  # bottom-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												else:  # bottom-right
																		lowerCAmelCase__    						=						cva.resize(
																		    UpperCamelCase_    ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)

    # Remove bounding box small than scale of filter
						if filter_scale > 0:
												lowerCAmelCase__    						=						[
												    anno
												    for anno in new_anno
												    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
												]

						return output_img, new_anno, path_list[0]







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   str:

						"""simple docstring"""



						assert number_char > 1, "The number of character should greater than 1"
						lowerCAmelCase__    						=						ascii_lowercase + digits
						return "".join(random.choice(UpperCamelCase_		) for _ in range(UpperCamelCase_		)		)


if __name__ == "__main__":
					main()
					print("""DONE ✅""")





