





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

import cva
lowerCAmelCase_     =     """"""
lowerCAmelCase_     =     """"""
lowerCAmelCase_     =     """"""
lowerCAmelCase_     =     1  # (0 is vertical, 1 is horizontal)


def      lowerCamelCase_						(   )->		None:
  _snake_case				,       _snake_case :					List[str]								=						get_dataset(SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_					)
  print('Processing...'					)
  _snake_case				,       _snake_case				,       _snake_case :					Union[str, Any]								=						update_image_and_anno(SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_					)

  for index, image in enumerate(SCREAMING_SNAKE_CASE_					):
    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    _snake_case :					List[Any]								=						random_chars(32					)
    _snake_case :					int								=						paths[index].split(os.sep					)[-1].rsplit('.'     ,				1					)[0]
    _snake_case :					Dict								=						F"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
    cva.imwrite(F"""/{file_root}.jpg"""     ,				SCREAMING_SNAKE_CASE_     ,				[cva.IMWRITE_JPEG_QUALITY, 85]					)
    print(F"""Success {index+1}/{len(SCREAMING_SNAKE_CASE_					)} with {file_name}"""					)
    _snake_case :					List[Any]								=						[]
    for anno in new_annos[index]:
      _snake_case :					int								=						F"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
      annos_list.append(SCREAMING_SNAKE_CASE_					)
    with open(F"""/{file_root}.txt"""     ,				'w'					) as outfile:
      outfile.write('\n'.join(line for line in annos_list					)					)


def      lowerCamelCase_						(   lowerCAmelCase:						Optional[Any]     ,				lowerCAmelCase:						int					)->		tuple[list, list]:
  _snake_case :					List[str]								=						[]
  _snake_case :					List[Any]								=						[]
  for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE_     ,				'*.txt'					)					):
    _snake_case :					Optional[Any]								=						label_file.split(os.sep					)[-1].rsplit('.'     ,				1					)[0]
    with open(SCREAMING_SNAKE_CASE_					) as in_file:
      _snake_case :					List[Any]								=						in_file.readlines()
    _snake_case :					str								=						os.path.join(SCREAMING_SNAKE_CASE_     ,				F"""{label_name}.jpg"""					)

    _snake_case :					Any								=						[]
    for obj_list in obj_lists:
      _snake_case :					Tuple								=						obj_list.rstrip('\n'					).split(' '					)
      boxes.append(
          [
              int(obj[0]					),
              float(obj[1]					),
              float(obj[2]					),
              float(obj[3]					),
              float(obj[4]					),
          ]					)
    if not boxes:
      continue
    img_paths.append(SCREAMING_SNAKE_CASE_					)
    labels.append(SCREAMING_SNAKE_CASE_					)
  return img_paths, labels


def      lowerCamelCase_						(   lowerCAmelCase:						Optional[Any]     ,				lowerCAmelCase:						Optional[Any]     ,				lowerCAmelCase:						List[Any] = 1					)->		tuple[list, list, list]:
  _snake_case :					int								=						[]
  _snake_case :					Dict								=						[]
  _snake_case :					List[Any]								=						[]
  for idx in range(len(SCREAMING_SNAKE_CASE_					)					):
    _snake_case :					List[str]								=						[]
    _snake_case :					Optional[int]								=						img_list[idx]
    path_list.append(SCREAMING_SNAKE_CASE_					)
    _snake_case :					Optional[int]								=						anno_list[idx]
    _snake_case :					str								=						cva.imread(SCREAMING_SNAKE_CASE_					)
    if flip_type == 1:
      _snake_case :					Any								=						cva.flip(SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_					)
      for bbox in img_annos:
        _snake_case :					List[Any]								=						1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]					)
    elif flip_type == 0:
      _snake_case :					int								=						cva.flip(SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_					)
      for bbox in img_annos:
        _snake_case :					int								=						1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]					)
    new_annos_lists.append(SCREAMING_SNAKE_CASE_					)
    new_imgs_list.append(SCREAMING_SNAKE_CASE_					)
  return new_imgs_list, new_annos_lists, path_list


def      lowerCamelCase_						(   lowerCAmelCase:						str = 32					)->		str:
  assert number_char > 1, "The number of character should greater than 1"
  _snake_case :					Optional[int]								=						ascii_lowercase + digits
  return "".join(random.choice(SCREAMING_SNAKE_CASE_					) for _ in range(SCREAMING_SNAKE_CASE_					)					)


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







import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     logging.get_logger(__name__)






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Optional[int]										=      """mask2former"""
     UpperCamelCase_	:Dict										=      ["""swin"""]
     UpperCamelCase_	:List[Any]										=      {"""hidden_size""": """hidden_dim"""}






     def __init__(				self		,						_lowercase = None		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 1_024		,						_lowercase = "relu"		,						_lowercase = 6		,						_lowercase = 10		,						_lowercase = 8		,						_lowercase = 0.0		,						_lowercase = 2_048		,						_lowercase = False		,						_lowercase = False		,						_lowercase = 4		,						_lowercase = 255		,						_lowercase = 100		,						_lowercase = 0.1		,						_lowercase = 2.0		,						_lowercase = 5.0		,						_lowercase = 5.0		,						_lowercase = 12_544		,						_lowercase = 3.0		,						_lowercase = 0.75		,						_lowercase = 0.02		,						_lowercase = 1.0		,						_lowercase = True		,						_lowercase = [4, 8, 16, 32]		,						_lowercase = None		,						**_lowercase		,						)->     Union[str, Any]:
         if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
             UpperCamelCase_			  =						CONFIG_MAPPING["swin"](
                 image_size=224		,						in_channels=3		,						patch_size=4		,						embed_dim=96		,						depths=[2, 2, 18, 2]		,						num_heads=[3, 6, 12, 24]		,						window_size=7		,						drop_path_rate=0.3		,						use_absolute_embeddings=_lowercase		,						out_features=["stage1", "stage2", "stage3", "stage4"]		,						)

         if isinstance(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						backbone_config.pop("model_type"	)
             UpperCamelCase_			  =						CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_			  =						config_class.from_dict(_lowercase	)

         # verify that the backbone is supported
         if backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(
                 F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
                 F"Supported model types: {','.join(self.backbones_supported	)}"	)

         UpperCamelCase_			  =						backbone_config
         UpperCamelCase_			  =						feature_size
         UpperCamelCase_			  =						mask_feature_size
         UpperCamelCase_			  =						hidden_dim
         UpperCamelCase_			  =						encoder_feedforward_dim
         UpperCamelCase_			  =						activation_function
         UpperCamelCase_			  =						encoder_layers
         UpperCamelCase_			  =						decoder_layers
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						dropout
         UpperCamelCase_			  =						dim_feedforward
         UpperCamelCase_			  =						pre_norm
         UpperCamelCase_			  =						enforce_input_projection
         UpperCamelCase_			  =						common_stride
         UpperCamelCase_			  =						ignore_value
         UpperCamelCase_			  =						num_queries
         UpperCamelCase_			  =						no_object_weight
         UpperCamelCase_			  =						class_weight
         UpperCamelCase_			  =						mask_weight
         UpperCamelCase_			  =						dice_weight
         UpperCamelCase_			  =						train_num_points
         UpperCamelCase_			  =						oversample_ratio
         UpperCamelCase_			  =						importance_sample_ratio
         UpperCamelCase_			  =						init_std
         UpperCamelCase_			  =						init_xavier_std
         UpperCamelCase_			  =						use_auxiliary_loss
         UpperCamelCase_			  =						feature_strides
         UpperCamelCase_			  =						output_auxiliary_logits
         UpperCamelCase_			  =						decoder_layers

         super().__init__(**_lowercase	)






     @classmethod
     def 	UpperCAmelCase_				(				cls		,						_lowercase		,						**_lowercase	)->     Optional[int]:
         return cls(
             backbone_config=_lowercase		,						**_lowercase		,						)






     def 	UpperCAmelCase_				(				self	)->     Dict[str, any]:
         UpperCamelCase_			  =						copy.deepcopy(self.__dict__	)
         UpperCamelCase_			  =						self.backbone_config.to_dict()
         UpperCamelCase_			  =						self.__class__.model_type
         return output
