




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

import cva
import numpy as np

# Parrameters
__A        =    (7_20, 12_80)  # Height, Width
__A        =    (0.4, 0.6)  # if height or width lower than this scale, drop it.
__A        =    1 / 1_00
__A        =    ""
__A        =    ""
__A        =    ""
__A        =    2_50







def 			lowerCamelCase_		(       )     ->       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 			lowerCamelCase_		(       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 			lowerCamelCase_		(       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 			lowerCamelCase_		(       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 ✅")








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A        =    {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    [
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					__A        =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


