"""simple docstring"""


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

import cva
import numpy as np

# Parrameters
lowerCAmelCase_:					Tuple     =					(7_2_0, 1_2_8_0)  # Height, Width
lowerCAmelCase_:					List[Any]     =					(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase_:					List[Any]     =					1 / 1_0_0
lowerCAmelCase_:					List[str]     =					""
lowerCAmelCase_:					List[str]     =					""
lowerCAmelCase_:					Optional[int]     =					""
lowerCAmelCase_:					List[str]     =					2_5_0







def 					__a   (		):






     '''simple docstring'''



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

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






     '''simple docstring'''



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

          lowercase__				 =							[]
          for obj_list in obj_lists:
               lowercase__				 =							obj_list.rstrip("\n"      ).split(" "      )
               lowercase__				 =							float(obj[1]      ) - float(obj[3]      ) / 2
               lowercase__				 =							float(obj[2]      ) - float(obj[4]      ) / 2
               lowercase__				 =							float(obj[1]      ) + float(obj[3]      ) / 2
               lowercase__				 =							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 					__a   (		A    ,				A    ,				A    ,				A    ,				A    ,				A = 0.0    ,				):






     '''simple docstring'''



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

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






     '''simple docstring'''



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


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





"""simple docstring"""


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            _UpperCAmelCase,				split=_UpperCAmelCase,				features=_UpperCAmelCase,				cache_dir=_UpperCAmelCase,				keep_in_memory=_UpperCAmelCase,				streaming=_UpperCAmelCase,				num_proc=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							path_or_paths if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) else {self.split: path_or_paths}
        lowercase__				 =							Text(
            cache_dir=_UpperCAmelCase,				data_files=_UpperCAmelCase,				features=_UpperCAmelCase,				**_UpperCAmelCase,				)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if self.streaming:
             lowercase__				 =							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							None

             self.builder.download_and_prepare(
                 download_config=_UpperCAmelCase,				download_mode=_UpperCAmelCase,				verification_mode=_UpperCAmelCase,				base_path=_UpperCAmelCase,				num_proc=self.num_proc,				)
             lowercase__				 =							self.builder.as_dataset(
                 split=self.split,				verification_mode=_UpperCAmelCase,				in_memory=self.keep_in_memory  )
        return dataset




