





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

import cva
import numpy as np

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






def 					lowerCamelCase_						(    ):






				"""simple docstring"""


				snake_case_						:   Any           =  get_dataset(_A			,    _A			)
				for index in range(_A			):
								snake_case_						:   List[str]           =  random.sample(range(len(_A			)			)			,    4			)
								snake_case_						:   List[Any]           =  update_image_and_anno(
								    _A			,    _A			,    _A			,    _A			,    _A			,    filter_scale=_A			,    )

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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			):






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  []
				snake_case_						:   Dict           =  []
				for label_file in glob.glob(os.path.join(_A			,    '''*.txt'''			)			):
								snake_case_						:   int           =  label_file.split(os.sep			)[-1].rsplit('''.'''			,    1			)[0]
								with open(_A			) as in_file:
												snake_case_						:   Tuple           =  in_file.readlines()
								snake_case_						:   List[str]           =  os.path.join(_A			,    f'''{label_name}.jpg'''			)

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

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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = 0.0			,    ):






				"""simple docstring"""


				snake_case_						:   Any           =  np.zeros([output_size[0], output_size[1], 3]			,    dtype=np.uinta			)
				snake_case_						:   Union[str, Any]           =  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				snake_case_						:   Union[str, Any]           =  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				snake_case_						:   Any           =  int(scale_x * output_size[1]			)
				snake_case_						:   List[Any]           =  int(scale_y * output_size[0]			)

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






				"""simple docstring"""


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


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







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[Any]	      =				['''image_processor''', '''tokenizer''']
			lowerCamelCase_					:					List[Any]	      =				'''BlipImageProcessor'''
			lowerCamelCase_					:					Union[str, Any]	      =				'''AutoTokenizer'''






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )

							# add QFormer tokenizer
							snake_case_						:   Optional[Any]           =  qformer_tokenizer






			def __call__(self ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = 0 ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = True ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''simple docstring'''



							if images is None and text is None:
											raise ValueError('''You have to specify at least images or text.'''   )

							snake_case_						:   Tuple           =  BatchFeature()

							if text is not None:
											snake_case_						:   Tuple           =  self.tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											encoding.update(__magic_name__   )
											snake_case_						:   Optional[Any]           =  self.qformer_tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											snake_case_						:   Optional[int]           =  qformer_text_encoding.pop('''input_ids'''   )
											snake_case_						:   Tuple           =  qformer_text_encoding.pop('''attention_mask'''   )

							if images is not None:
											snake_case_						:   Any           =  self.image_processor(__magic_name__ ,							return_tensors=__magic_name__   )
											encoding.update(__magic_name__   )

							return encoding






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							return self.tokenizer.batch_decode(*__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							return self.tokenizer.decode(*__magic_name__ ,							**__magic_name__   )






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.tokenizer.model_input_names
							snake_case_						:   List[Any]           =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							if os.path.isfile(__magic_name__   ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(__magic_name__ ,							exist_ok=__magic_name__   )
							snake_case_						:   Any           =  os.path.join(__magic_name__ ,							'''qformer_tokenizer'''   )
							self.qformer_tokenizer.save_pretrained(__magic_name__   )
							return super().save_pretrained(__magic_name__ ,							**__magic_name__   )






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  AutoTokenizer.from_pretrained(__magic_name__ ,							subfolder='''qformer_tokenizer'''   )
							snake_case_						:   str           =  cls._get_arguments_from_pretrained(__magic_name__ ,							**__magic_name__   )
							args.append(__magic_name__   )
							return cls(*__magic_name__   )
