



"""simple docstring"""




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

import cva
import numpy as np

# Parrameters
_lowercase		:				Optional[Any]      		=(720, 1280)  # Height, Width
_lowercase		:				Tuple      		=(0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowercase		:				Dict      		=1 / 100
_lowercase		:				Dict      		=""
_lowercase		:				Optional[Any]      		=""
_lowercase		:				Tuple      		=""
_lowercase		:				List[str]      		=250

def      lowerCAmelCase_					(  )							->	Optional[Any]:







			"""simple docstring"""
			a__	,						a__       :						Optional[int]							=  get_dataset(_lowercase						,       _lowercase)
			for index in range(_lowercase):
						a__       :						Optional[int]							=  random.sample(range(len(_lowercase))						,       4)
						a__	,						a__	,						a__       :						Union[str, Any]							=  update_image_and_anno(
						    _lowercase						,       _lowercase						,       _lowercase						,       _lowercase						,       _lowercase						,       filter_scale=_lowercase						,       )

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

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       Dict)							->	str:







			"""simple docstring"""
			a__       :						Optional[Any]							=  []
			a__       :						Any							=  []
			for label_file in glob.glob(os.path.join(_lowercase						,       """*.txt""")):
						a__       :						List[str]							=  label_file.split(os.sep)[-1].rsplit("""."""						,       1)[0]
						with open(_lowercase) as in_file:
									a__       :						Union[str, Any]							=  in_file.readlines()
						a__       :						List[str]							=  os.path.join(_lowercase						,       F'''{label_name}.jpg''')

						a__       :						List[str]							=  []
						for obj_list in obj_lists:
									a__       :						List[Any]							=  obj_list.rstrip("""\n""").split(""" """)
									a__       :						Tuple							=  float(obj[1]) - float(obj[3]) / 2
									a__       :						Optional[int]							=  float(obj[2]) - float(obj[4]) / 2
									a__       :						Optional[Any]							=  float(obj[1]) + float(obj[3]) / 2
									a__       :						int							=  float(obj[2]) + float(obj[4]) / 2

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

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any]						,       _lowercase    :       Tuple						,       _lowercase    :       Optional[int]						,       _lowercase    :       List[Any]						,       _lowercase    :       Union[str, Any]						,       _lowercase    :       Union[str, Any] = 0.0						,       )							->	str:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  np.zeros([output_size[0], output_size[1], 3]						,       dtype=np.uinta)
			a__       :						Any							=  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
			a__       :						Dict							=  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
			a__       :						Optional[int]							=  int(scale_x * output_size[1])
			a__       :						Optional[int]							=  int(scale_y * output_size[0])

			a__       :						Dict							=  []
			a__       :						List[str]							=  []
			for i, index in enumerate(_lowercase):
						a__       :						str							=  all_img_list[index]
						path_list.append(_lowercase)
						a__       :						List[str]							=  all_annos[index]
						a__       :						Tuple							=  cva.imread(_lowercase)
						if i == 0:  # top-left
									a__       :						Any							=  cva.resize(_lowercase						,       (divid_point_x, divid_point_y))
									a__       :						Union[str, Any]							=  img
									for bbox in img_annos:
												a__       :						Optional[Any]							=  bbox[1] * scale_x
												a__       :						Tuple							=  bbox[2] * scale_y
												a__       :						int							=  bbox[3] * scale_x
												a__       :						Any							=  bbox[4] * scale_y
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
						elif i == 1:  # top-right
									a__       :						Optional[Any]							=  cva.resize(_lowercase						,       (output_size[1] - divid_point_x, divid_point_y))
									a__       :						List[str]							=  img
									for bbox in img_annos:
												a__       :						Optional[Any]							=  scale_x + bbox[1] * (1 - scale_x)
												a__       :						Union[str, Any]							=  bbox[2] * scale_y
												a__       :						Optional[Any]							=  scale_x + bbox[3] * (1 - scale_x)
												a__       :						int							=  bbox[4] * scale_y
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
						elif i == 2:  # bottom-left
									a__       :						Optional[Any]							=  cva.resize(_lowercase						,       (divid_point_x, output_size[0] - divid_point_y))
									a__       :						Optional[int]							=  img
									for bbox in img_annos:
												a__       :						List[str]							=  bbox[1] * scale_x
												a__       :						List[str]							=  scale_y + bbox[2] * (1 - scale_y)
												a__       :						Tuple							=  bbox[3] * scale_x
												a__       :						Dict							=  scale_y + bbox[4] * (1 - scale_y)
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
						else:  # bottom-right
									a__       :						List[Any]							=  cva.resize(
									    _lowercase						,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y))
									a__       :						Tuple							=  img
									for bbox in img_annos:
												a__       :						int							=  scale_x + bbox[1] * (1 - scale_x)
												a__       :						Dict							=  scale_y + bbox[2] * (1 - scale_y)
												a__       :						str							=  scale_x + bbox[3] * (1 - scale_x)
												a__       :						Tuple							=  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:
						a__       :						Tuple							=  [
						    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_					(  _lowercase    :       List[str])							->	str:







			"""simple docstring"""
			assert number_char > 1, "The number of character should greater than 1"
			a__       :						List[Any]							=  ascii_lowercase + digits
			return "".join(random.choice(_lowercase) for _ in range(_lowercase))


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









from typing import TYPE_CHECKING

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


_lowercase		:				int      		={
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				Optional[Any]      		=[
		    "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "LongT5EncoderModel",
		    "LongT5ForConditionalGeneration",
		    "LongT5Model",
		    "LongT5PreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				List[Any]      		=[
		    "FlaxLongT5ForConditionalGeneration",
		    "FlaxLongT5Model",
		    "FlaxLongT5PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longta import (
				    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongTaEncoderModel,
				    LongTaForConditionalGeneration,
				    LongTaModel,
				    LongTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_longta import (
				    FlaxLongTaForConditionalGeneration,
				    FlaxLongTaModel,
				    FlaxLongTaPreTrainedModel,
				)


else:
		import sys

		_lowercase		:				Any      		=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




