"""simple docstring"""






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

import cva
import numpy as np

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



def SCREAMING_SNAKE_CASE__			(   )  ->  None:
   lowercase__, lowercase__:   int					=      get_dataset(__UpperCAmelCase       ,    __UpperCAmelCase )
   for index in range(__UpperCAmelCase ):
      lowercase__:   Optional[Any]					=      random.sample(range(len(__UpperCAmelCase ) )       ,    4 )
      lowercase__, lowercase__, lowercase__:   Optional[int]					=      update_image_and_anno(
          __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    filter_scale=__UpperCAmelCase       ,    )

      # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
      lowercase__:   Optional[int]					=      random_chars(3_2 )
      lowercase__:   Optional[int]					=      path.split(os.sep )[-1].rsplit('''.'''       ,    1 )[0]
      lowercase__:   str					=      F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
      cva.imwrite(F"""{file_root}.jpg"""       ,    __UpperCAmelCase       ,    [cva.IMWRITE_JPEG_QUALITY, 8_5] )
      print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
      lowercase__:   Any					=      []
      for anno in new_annos:
         lowercase__:   str					=      anno[3] - anno[1]
         lowercase__:   Union[str, Any]					=      anno[4] - anno[2]
         lowercase__:   Dict					=      anno[1] + width / 2
         lowercase__:   List[Any]					=      anno[2] + height / 2
         lowercase__:   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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  tuple[list, list]:
   lowercase__:   Dict					=      []
   lowercase__:   Union[str, Any]					=      []
   for label_file in glob.glob(os.path.join(__UpperCAmelCase       ,    '''*.txt''' ) ):
      lowercase__:   str					=      label_file.split(os.sep )[-1].rsplit('''.'''       ,    1 )[0]
      with open(__UpperCAmelCase ) as in_file:
         lowercase__:   Tuple					=      in_file.readlines()
      lowercase__:   Tuple					=      os.path.join(__UpperCAmelCase       ,    F"""{label_name}.jpg""" )

      lowercase__:   List[Any]					=      []
      for obj_list in obj_lists:
         lowercase__:   Any					=      obj_list.rstrip('''\n''' ).split(''' ''' )
         lowercase__:   Tuple					=      float(obj[1] ) - float(obj[3] ) / 2
         lowercase__:   Dict					=      float(obj[2] ) - float(obj[4] ) / 2
         lowercase__:   Any					=      float(obj[1] ) + float(obj[3] ) / 2
         lowercase__:   Optional[int]					=      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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = 0.0       ,    )  ->  tuple[list, list, str]:
   lowercase__:   Optional[Any]					=      np.zeros([output_size[0], output_size[1], 3]       ,    dtype=np.uinta )
   lowercase__:   Union[str, Any]					=      scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   lowercase__:   List[str]					=      scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   lowercase__:   Optional[Any]					=      int(scale_x * output_size[1] )
   lowercase__:   Optional[int]					=      int(scale_y * output_size[0] )

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


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

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			   = {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LiltForQuestionAnswering",
           "LiltForSequenceClassification",
           "LiltForTokenClassification",
           "LiltModel",
           "LiltPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
