


"""simple docstring"""



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

import cva
import numpy as np

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







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case   ,snake_case       : int    =       get_dataset(lowercase		,				lowercase					)
    for index in range(lowercase					):
        snake_case       : int    =       random.sample(range(len(lowercase					)					)		,				4					)
        snake_case   ,snake_case   ,snake_case       : str    =       update_image_and_anno(
            lowercase		,				lowercase		,				lowercase		,				lowercase		,				lowercase		,				filter_scale=lowercase		,				)

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        snake_case       : Dict    =       random_chars(32					)
        snake_case       : Optional[int]    =       path.split(os.sep					)[-1].rsplit("."		,				1					)[0]
        snake_case       : Dict    =       F'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
        cva.imwrite(F'{file_root}.jpg'		,				lowercase		,				[cva.IMWRITE_JPEG_QUALITY, 85]					)
        print(F'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'					)
        snake_case       : Tuple    =       []
        for anno in new_annos:
            snake_case       : Any    =       anno[3] - anno[1]
            snake_case       : Tuple    =       anno[4] - anno[2]
            snake_case       : Tuple    =       anno[1] + width / 2
            snake_case       : List[Any]    =       anno[2] + height / 2
            snake_case       : Dict    =       F'{anno[0]} {x_center} {y_center} {width} {height}'
            annos_list.append(lowercase					)
        with open(F'{file_root}.txt'		,				"w"					) as outfile:
            outfile.write("\n".join(line for line in annos_list					)					)







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str					)      ->		tuple[list, list]:


    """simple docstring"""





    snake_case       : Union[str, Any]    =       []
    snake_case       : int    =       []
    for label_file in glob.glob(os.path.join(lowercase		,				"*.txt"					)					):
        snake_case       : List[Any]    =       label_file.split(os.sep					)[-1].rsplit("."		,				1					)[0]
        with open(lowercase					) as in_file:
            snake_case       : Any    =       in_file.readlines()
        snake_case       : Tuple    =       os.path.join(lowercase		,				F'{label_name}.jpg'					)

        snake_case       : str    =       []
        for obj_list in obj_lists:
            snake_case       : Any    =       obj_list.rstrip("\n"					).split(" "					)
            snake_case       : List[Any]    =       float(obj[1]					) - float(obj[3]					) / 2
            snake_case       : Optional[int]    =       float(obj[2]					) - float(obj[4]					) / 2
            snake_case       : List[Any]    =       float(obj[1]					) + float(obj[3]					) / 2
            snake_case       : Tuple    =       float(obj[2]					) + float(obj[4]					) / 2

            boxes.append([int(obj[0]					), xmin, ymin, xmax, ymax]					)
        if not boxes:
            continue
        img_paths.append(lowercase					)
        labels.append(lowercase					)
    return img_paths, labels







def     __lowerCAmelCase		(		lowercase    :    list		,				lowercase    :    list		,				lowercase    :    list[int]		,				lowercase    :    tuple[int, int]		,				lowercase    :    tuple[float, float]		,				lowercase    :    float = 0.0		,				)      ->		tuple[list, list, str]:


    """simple docstring"""





    snake_case       : str    =       np.zeros([output_size[0], output_size[1], 3]		,				dtype=np.uinta					)
    snake_case       : Any    =       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    snake_case       : List[str]    =       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    snake_case       : Optional[Any]    =       int(scale_x * output_size[1]					)
    snake_case       : List[Any]    =       int(scale_y * output_size[0]					)

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


    """simple docstring"""





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


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









"""simple docstring"""



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


__snake_case       =       {
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Any				    =    '''maskformer'''
   __UpperCAmelCase					:			List[str]				    =    {'''hidden_size''': '''mask_feature_size'''}
   __UpperCAmelCase					:			Optional[Any]				    =    ['''resnet''', '''swin''']
   __UpperCAmelCase					:			str				    =    ['''detr''']







   def __init__(      self		,      UpperCamelCase__ = 256		,      UpperCamelCase__ = 256		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 0.02		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 20.0		,      UpperCamelCase__ = None		,      **UpperCamelCase__		,      )      ->						Optional[int]:

       '''simple docstring'''




       if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           snake_case       : Tuple    =       SwinConfig(
               image_size=384		,      in_channels=3		,      patch_size=4		,      embed_dim=128		,      depths=[2, 2, 18, 2]		,      num_heads=[4, 8, 16, 32]		,      window_size=12		,      drop_path_rate=0.3		,      out_features=["stage1", "stage2", "stage3", "stage4"]		,      )

       if isinstance(UpperCamelCase__		,      UpperCamelCase__		):
           snake_case       : Optional[Any]    =       backbone_config.pop("model_type"		)
           snake_case       : List[str]    =       CONFIG_MAPPING[backbone_model_type]
           snake_case       : str    =       config_class.from_dict(UpperCamelCase__		)

       # 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 MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported		)}'		)

       if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           snake_case       : Optional[int]    =       DetrConfig()
       else:
           # verify that the decoder is supported
           snake_case       : Optional[Any]    =       (
               decoder_config.pop("model_type"		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
               raise ValueError(
                   F'Transformer Decoder {decoder_type} not supported, please use one of'
                   F' {",".join(self.decoders_supported		)}'		)
           if isinstance(UpperCamelCase__		,      UpperCamelCase__		):
               snake_case       : Dict    =       CONFIG_MAPPING[decoder_type]
               snake_case       : List[Any]    =       config_class.from_dict(UpperCamelCase__		)

       snake_case       : Optional[int]    =       backbone_config
       snake_case       : Optional[Any]    =       decoder_config
       # main feature dimension for the model
       snake_case       : List[str]    =       fpn_feature_size
       snake_case       : Tuple    =       mask_feature_size
       # initializer
       snake_case       : str    =       init_std
       snake_case       : str    =       init_xavier_std
       # Hungarian matcher && loss
       snake_case       : List[Any]    =       cross_entropy_weight
       snake_case       : int    =       dice_weight
       snake_case       : str    =       mask_weight
       snake_case       : Any    =       use_auxiliary_loss
       snake_case       : Any    =       no_object_weight
       snake_case       : List[Any]    =       output_auxiliary_logits

       snake_case       : Optional[int]    =       self.decoder_config.encoder_attention_heads
       snake_case       : Tuple    =       self.decoder_config.num_hidden_layers
       super().__init__(**UpperCamelCase__		)







   @classmethod
   def 					lowerCamelCase						(      cls		,      UpperCamelCase__		,      UpperCamelCase__		,      **UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       return cls(
           backbone_config=UpperCamelCase__		,      decoder_config=UpperCamelCase__		,      **UpperCamelCase__		,      )







   def 					lowerCamelCase						(      self		)      ->						Dict[str, any]:

       '''simple docstring'''




       snake_case       : int    =       copy.deepcopy(self.__dict__		)
       snake_case       : List[str]    =       self.backbone_config.to_dict()
       snake_case       : Union[str, Any]    =       self.decoder_config.to_dict()
       snake_case       : Optional[int]    =       self.__class__.model_type
       return output





