

"""simple docstring"""




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

import cva
import numpy as np

# Parrameters
A_							:			Any											= (720, 1_280)  # Height, Width
A_							:			Optional[int]											= (0.4, 0.6)  # if height or width lower than this scale, drop it.
A_							:			List[Any]											= 1 / 100
A_							:			Union[str, Any]											= ""
A_							:			List[str]											= ""
A_							:			int											= ""
A_							:			str											= 250

def 	A       (     ):




			'''simple docstring'''


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

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

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


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

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

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

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__ = 0.0		,				):




			'''simple docstring'''


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

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




			'''simple docstring'''


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


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





"""simple docstring"""




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

A_							:			Tuple											= "2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
						raise ImportWarning(
						    "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
						    "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

A_							:			Tuple											= concatenate_datasets
A_							:			List[str]											= DownloadConfig
A_							:			int											= DownloadManager
A_							:			Optional[Any]											= DownloadMode
A_							:			Optional[int]											= DownloadConfig
A_							:			List[Any]											= DownloadMode
A_							:			Any											= DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


