





'''simple docstring'''

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

import cva
import numpy as np

# Parrameters
a        =						(720, 1280)  # Height, Width
a        =						(0.4, 0.6)  # if height or width lower than this scale, drop it.
a        =						1 / 100
a        =						""
a        =						""
a        =						""
a        =						250

def 						__magic_name__       (							)						->					None:

   '''simple docstring'''







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

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

   '''simple docstring'''







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

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

   '''simple docstring'''







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

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

   '''simple docstring'''







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


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












'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a        =						logging.get_logger(__name__)

a        =						{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class __a (   _snake_case			):
   __UpperCamelCase   : Dict	      =	'deta'
   __UpperCamelCase   : List[str]	      =	{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }





   def __init__(					self				:			Tuple				,lowerCamelCase				:			List[Any]=None				,lowerCamelCase				:			Any=900				,lowerCamelCase				:			int=2048				,lowerCamelCase				:			Any=6				,lowerCamelCase				:			Optional[Any]=2048				,lowerCamelCase				:			str=8				,lowerCamelCase				:			Union[str, Any]=6				,lowerCamelCase				:			List[str]=1024				,lowerCamelCase				:			int=8				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Optional[int]="relu"				,lowerCamelCase				:			int=256				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Optional[Any]=0.0				,lowerCamelCase				:			Tuple=0.0				,lowerCamelCase				:			List[str]=0.02				,lowerCamelCase				:			Any=1.0				,lowerCamelCase				:			Optional[int]=True				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Optional[Any]="sine"				,lowerCamelCase				:			Dict=5				,lowerCamelCase				:			List[Any]=4				,lowerCamelCase				:			Optional[Any]=4				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			int=300				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Tuple=True				,lowerCamelCase				:			int=1				,lowerCamelCase				:			Tuple=5				,lowerCamelCase				:			Union[str, Any]=2				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			int=1				,lowerCamelCase				:			str=5				,lowerCamelCase				:			Optional[Any]=2				,lowerCamelCase				:			List[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.25				,**lowerCamelCase				:			int				,):

      '''simple docstring'''

      if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
         __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]    )
      else:
         if isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       backbone_config.pop("""model_type"""    )
            __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING[backbone_model_type]
            __SCREAMING_SNAKE_CASE      							=       config_class.from_dict(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       backbone_config
      __SCREAMING_SNAKE_CASE      							=       num_queries
      __SCREAMING_SNAKE_CASE      							=       max_position_embeddings
      __SCREAMING_SNAKE_CASE      							=       d_model
      __SCREAMING_SNAKE_CASE      							=       encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       encoder_layers
      __SCREAMING_SNAKE_CASE      							=       encoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       decoder_layers
      __SCREAMING_SNAKE_CASE      							=       decoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_function
      __SCREAMING_SNAKE_CASE      							=       init_std
      __SCREAMING_SNAKE_CASE      							=       init_xavier_std
      __SCREAMING_SNAKE_CASE      							=       encoder_layerdrop
      __SCREAMING_SNAKE_CASE      							=       auxiliary_loss
      __SCREAMING_SNAKE_CASE      							=       position_embedding_type
      # deformable attributes
      __SCREAMING_SNAKE_CASE      							=       num_feature_levels
      __SCREAMING_SNAKE_CASE      							=       encoder_n_points
      __SCREAMING_SNAKE_CASE      							=       decoder_n_points
      __SCREAMING_SNAKE_CASE      							=       two_stage
      __SCREAMING_SNAKE_CASE      							=       two_stage_num_proposals
      __SCREAMING_SNAKE_CASE      							=       with_box_refine
      __SCREAMING_SNAKE_CASE      							=       assign_first_stage
      if two_stage is True and with_box_refine is False:
         raise ValueError("""If two_stage is True, with_box_refine must be True."""    )
      # Hungarian matcher
      __SCREAMING_SNAKE_CASE      							=       class_cost
      __SCREAMING_SNAKE_CASE      							=       bbox_cost
      __SCREAMING_SNAKE_CASE      							=       giou_cost
      # Loss coefficients
      __SCREAMING_SNAKE_CASE      							=       mask_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       dice_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       bbox_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       giou_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       eos_coefficient
      __SCREAMING_SNAKE_CASE      							=       focal_alpha
      super().__init__(is_encoder_decoder=lowerCamelCase				,**lowerCamelCase    )





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return self.encoder_attention_heads





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return self.d_model





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       copy.deepcopy(self.__dict__    )
      __SCREAMING_SNAKE_CASE      							=       self.backbone_config.to_dict()
      __SCREAMING_SNAKE_CASE      							=       self.__class__.model_type
      return output





