


'''simple docstring'''



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

import cva
import numpy as np

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



def      UpperCAmelCase__   (      )	->						None:
     __lowerCamelCase							,			__lowerCamelCase  :      Any														=						get_dataset(UpperCAmelCase_ ,       UpperCAmelCase_				)
     for index in range(UpperCAmelCase_				):
          __lowerCamelCase  :      Dict														=						random.sample(range(len(UpperCAmelCase_				)				) ,       4				)
          __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      int														=						update_image_and_anno(
              UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       filter_scale=UpperCAmelCase_ ,       )

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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							str				)	->						tuple[list, list]:
     __lowerCamelCase  :      int														=						[]
     __lowerCamelCase  :      Union[str, Any]														=						[]
     for label_file in glob.glob(os.path.join(UpperCAmelCase_ ,       '*.txt'				)				):
          __lowerCamelCase  :      Tuple														=						label_file.split(os.sep				)[-1].rsplit('.' ,       1				)[0]
          with open(UpperCAmelCase_				) as in_file:
               __lowerCamelCase  :      Tuple														=						in_file.readlines()
          __lowerCamelCase  :      Tuple														=						os.path.join(UpperCAmelCase_ ,       F'{label_name}.jpg'				)

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

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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							list ,       UpperCAmelCase_       :							list ,       UpperCAmelCase_       :							list[int] ,       UpperCAmelCase_       :							tuple[int, int] ,       UpperCAmelCase_       :							tuple[float, float] ,       UpperCAmelCase_       :							float = 0.0 ,       )	->						tuple[list, list, str]:
     __lowerCamelCase  :      Optional[Any]														=						np.zeros([output_size[0], output_size[1], 3] ,       dtype=np.uinta				)
     __lowerCamelCase  :      Any														=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
     __lowerCamelCase  :      Union[str, Any]														=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
     __lowerCamelCase  :      Any														=						int(scale_x * output_size[1]				)
     __lowerCamelCase  :      Optional[Any]														=						int(scale_y * output_size[0]				)

     __lowerCamelCase  :      List[str]														=						[]
     __lowerCamelCase  :      int														=						[]
     for i, index in enumerate(UpperCAmelCase_				):
          __lowerCamelCase  :      int														=						all_img_list[index]
          path_list.append(UpperCAmelCase_				)
          __lowerCamelCase  :      str														=						all_annos[index]
          __lowerCamelCase  :      List[Any]														=						cva.imread(UpperCAmelCase_				)
          if i == 0:  # top-left
               __lowerCamelCase  :      Union[str, Any]														=						cva.resize(UpperCAmelCase_ ,       (divid_point_x, divid_point_y)				)
               __lowerCamelCase  :      str														=						img
               for bbox in img_annos:
                    __lowerCamelCase  :      Any														=						bbox[1] * scale_x
                    __lowerCamelCase  :      Tuple														=						bbox[2] * scale_y
                    __lowerCamelCase  :      Tuple														=						bbox[3] * scale_x
                    __lowerCamelCase  :      List[Any]														=						bbox[4] * scale_y
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
          elif i == 1:  # top-right
               __lowerCamelCase  :      Dict														=						cva.resize(UpperCAmelCase_ ,       (output_size[1] - divid_point_x, divid_point_y)				)
               __lowerCamelCase  :      str														=						img
               for bbox in img_annos:
                    __lowerCamelCase  :      List[str]														=						scale_x + bbox[1] * (1 - scale_x)
                    __lowerCamelCase  :      Any														=						bbox[2] * scale_y
                    __lowerCamelCase  :      str														=						scale_x + bbox[3] * (1 - scale_x)
                    __lowerCamelCase  :      Optional[Any]														=						bbox[4] * scale_y
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
          elif i == 2:  # bottom-left
               __lowerCamelCase  :      Any														=						cva.resize(UpperCAmelCase_ ,       (divid_point_x, output_size[0] - divid_point_y)				)
               __lowerCamelCase  :      Dict														=						img
               for bbox in img_annos:
                    __lowerCamelCase  :      str														=						bbox[1] * scale_x
                    __lowerCamelCase  :      Optional[int]														=						scale_y + bbox[2] * (1 - scale_y)
                    __lowerCamelCase  :      Dict														=						bbox[3] * scale_x
                    __lowerCamelCase  :      List[Any]														=						scale_y + bbox[4] * (1 - scale_y)
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
          else:  # bottom-right
               __lowerCamelCase  :      List[Any]														=						cva.resize(
                   UpperCAmelCase_ ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
               __lowerCamelCase  :      List[Any]														=						img
               for bbox in img_annos:
                    __lowerCamelCase  :      Any														=						scale_x + bbox[1] * (1 - scale_x)
                    __lowerCamelCase  :      int														=						scale_y + bbox[2] * (1 - scale_y)
                    __lowerCamelCase  :      str														=						scale_x + bbox[3] * (1 - scale_x)
                    __lowerCamelCase  :      str														=						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  :      Union[str, 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      UpperCAmelCase__   (      UpperCAmelCase_       :							int				)	->						str:
     assert number_char > 1, "The number of character should greater than 1"
     __lowerCamelCase  :      Any														=						ascii_lowercase + digits
     return "".join(random.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)


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









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







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




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

											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:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

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




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




