



'''simple docstring'''


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

import cva
import numpy as np

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







def 				__lowerCamelCase				(   )       ->	None:






   """simple docstring"""
   UpperCamelCase				,      UpperCamelCase					=  get_dataset(A__    ,						A__    )
   for index in range(A__    ):
      UpperCamelCase					=  random.sample(range(len(A__    )    )    ,						4    )
      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  update_image_and_anno(
          A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						filter_scale=A__    ,						)

      # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
      UpperCamelCase					=  random_chars(32    )
      UpperCamelCase					=  path.split(os.sep    )[-1].rsplit('.'    ,						1    )[0]
      UpperCamelCase					=  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}"""    )
      UpperCamelCase					=  []
      for anno in new_annos:
         UpperCamelCase					=  anno[3] - anno[1]
         UpperCamelCase					=  anno[4] - anno[2]
         UpperCamelCase					=  anno[1] + width / 2
         UpperCamelCase					=  anno[2] + height / 2
         UpperCamelCase					=  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				(   A__    ,						A__    )       ->	tuple[list, list]:






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

      UpperCamelCase					=  []
      for obj_list in obj_lists:
         UpperCamelCase					=  obj_list.rstrip('\n'    ).split(' '    )
         UpperCamelCase					=  float(obj[1]    ) - float(obj[3]    ) / 2
         UpperCamelCase					=  float(obj[2]    ) - float(obj[4]    ) / 2
         UpperCamelCase					=  float(obj[1]    ) + float(obj[3]    ) / 2
         UpperCamelCase					=  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				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__ = 0.0    ,						)       ->	tuple[list, list, str]:






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

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






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


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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase					: Union[str, Any]      =       {
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: List[Any]      =       [
					    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "InstructBlipQFormerModel",
					    "InstructBlipPreTrainedModel",
					    "InstructBlipForConditionalGeneration",
					    "InstructBlipVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_instructblip import (
					    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    InstructBlipConfig,
					    InstructBlipQFormerConfig,
					    InstructBlipVisionConfig,
					)
					from .processing_instructblip import InstructBlipProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_instructblip import (
										    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    InstructBlipForConditionalGeneration,
										    InstructBlipPreTrainedModel,
										    InstructBlipQFormerModel,
										    InstructBlipVisionModel,
										)

else:
					import sys

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