


'''simple docstring'''





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

import cva
import numpy as np

# Parrameters
UpperCAmelCase__  			=				(7_2_0, 1_2_8_0)  # Height, Width
UpperCAmelCase__  			=				(0.4, 0.6)  # if height or width lower than this scale, drop it.
UpperCAmelCase__  			=				1 / 1_0_0
UpperCAmelCase__  			=				''''''
UpperCAmelCase__  			=				''''''
UpperCAmelCase__  			=				''''''
UpperCAmelCase__  			=				2_5_0






def 						UpperCAmelCase__(				):






			"""simple docstring"""




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

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






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




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

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

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






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: list,_SCREAMING_SNAKE_CASE					: list,_SCREAMING_SNAKE_CASE					: list[int],_SCREAMING_SNAKE_CASE					: tuple[int, int],_SCREAMING_SNAKE_CASE					: tuple[float, float],_SCREAMING_SNAKE_CASE					: float = 0.0,):






			"""simple docstring"""




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

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






			"""simple docstring"""




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


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





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""  )
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""  )

									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

									if text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if text is not None and images is not None:
															_lowercase					       =					image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCamelCase  )				,			tensor_type=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class