






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

import cva
import numpy as np

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





def 	lowerCAmelCase_		()		->    None:


				'''simple docstring'''




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

								# 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'     ,					lowercase__     ,					[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(lowercase__       )
								with open(f'{file_root}.txt'     ,					'''w'''       ) as outfile:
												outfile.write('''\n'''.join(line for line in annos_list       )       )





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       str       )		->    tuple[list, list]:


				'''simple docstring'''




				lowerCAmelCase__      			=						[]
				lowerCAmelCase__      			=						[]
				for label_file in glob.glob(os.path.join(lowercase__     ,					'''*.txt'''       )       ):
								lowerCAmelCase__      			=						label_file.split(os.sep       )[-1].rsplit('''.'''     ,					1       )[0]
								with open(lowercase__       ) as in_file:
												lowerCAmelCase__      			=						in_file.readlines()
								lowerCAmelCase__      			=						os.path.join(lowercase__     ,					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(lowercase__       )
								labels.append(lowercase__       )
				return img_paths, labels





def 	lowerCAmelCase_		(lowercase__						:       list     ,					lowercase__						:       list     ,					lowercase__						:       list[int]     ,					lowercase__						:       tuple[int, int]     ,					lowercase__						:       tuple[float, float]     ,					lowercase__						:       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(lowercase__       ):
								lowerCAmelCase__      			=						all_img_list[index]
								path_list.append(lowercase__       )
								lowerCAmelCase__      			=						all_annos[index]
								lowerCAmelCase__      			=						cva.imread(lowercase__       )
								if i == 0:  # top-left
												lowerCAmelCase__      			=						cva.resize(lowercase__     ,					(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(lowercase__     ,					(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(lowercase__     ,					(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(
												    lowercase__     ,					(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_		(lowercase__						:       int       )		->    str:


				'''simple docstring'''




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


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













import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Tuple		      =    ['image_processor', 'tokenizer']
							UpperCamelCase_		:Tuple		      =    'ViTImageProcessor'
							UpperCamelCase_		:Dict		      =    ('CLIPTokenizer', 'CLIPTokenizerFast')




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
											lowerCAmelCase__      			=						None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
															lowerCAmelCase__      			=						kwargs.pop('''feature_extractor'''	)

											lowerCAmelCase__      			=						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__(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def __call__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											if text is None and visual_prompt is None and images is None:
															raise ValueError('''You have to specify either text, visual prompt or images.'''	)

											if text is not None and visual_prompt is not None:
															raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''	)

											if text is not None:
															lowerCAmelCase__      			=						self.tokenizer(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if images is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None and images is not None:
															lowerCAmelCase__      			=						{
															    '''pixel_values''': image_features.pixel_values,
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											elif text is not None and images is not None:
															lowerCAmelCase__      			=						image_features.pixel_values
															return encoding
											elif text is not None:
															return encoding
											elif visual_prompt is not None:
															lowerCAmelCase__      			=						{
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											else:
															return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_	) ,							tensor_type=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   str	):
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   Tuple	):
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							@property
							def 						__snake_case					(   self     :   str	):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor_class




							@property
							def 						__snake_case					(   self     :   Tuple	):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor





