


"""simple docstring"""







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

import cva
import numpy as np

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







    """simple docstring"""
    a 		=get_dataset(lowercase       ,    lowercase		)
    for index in range(lowercase		):
        a 		=random.sample(range(len(lowercase		)		)       ,    4		)
        a 		=update_image_and_anno(
            lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    filter_scale=lowercase       ,    )

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







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

        a 		=[]
        for obj_list in obj_lists:
            a 		=obj_list.rstrip('''\n'''		).split(''' '''		)
            a 		=float(obj[1]		) - float(obj[3]		) / 2
            a 		=float(obj[2]		) - float(obj[4]		) / 2
            a 		=float(obj[1]		) + float(obj[3]		) / 2
            a 		=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 _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase = 0.0       ,    ):







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

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







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


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

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


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[str]						=      """deformable_detr"""
    a__	:						Union[str, Any]						=      {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }







    def __init__(	self , __lowercase=True , __lowercase=None , __lowercase=3 , __lowercase=300 , __lowercase=1_024 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=0.0 , __lowercase=True , __lowercase="relu" , __lowercase=256 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1.0 , __lowercase=True , __lowercase=False , __lowercase="sine" , __lowercase="resnet50" , __lowercase=True , __lowercase=False , __lowercase=4 , __lowercase=4 , __lowercase=4 , __lowercase=False , __lowercase=300 , __lowercase=False , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=1 , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=0.1 , __lowercase=0.25 , __lowercase=False , **__lowercase , )       ->	int:
        if backbone_config is not None and use_timm_backbone:
            raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
                __UpperCamelCase     :str			  =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
            elif isinstance(__lowercase , __lowercase):
                __UpperCamelCase     :str			  =		backbone_config.get('''model_type''')
                __UpperCamelCase     :Tuple			  =		CONFIG_MAPPING[backbone_model_type]
                __UpperCamelCase     :Any			  =		config_class.from_dict(__lowercase)
        __UpperCamelCase     :int			  =		use_timm_backbone
        __UpperCamelCase     :Dict			  =		backbone_config
        __UpperCamelCase     :Any			  =		num_channels
        __UpperCamelCase     :Optional[int]			  =		num_queries
        __UpperCamelCase     :Any			  =		max_position_embeddings
        __UpperCamelCase     :str			  =		d_model
        __UpperCamelCase     :Tuple			  =		encoder_ffn_dim
        __UpperCamelCase     :Union[str, Any]			  =		encoder_layers
        __UpperCamelCase     :List[Any]			  =		encoder_attention_heads
        __UpperCamelCase     :Any			  =		decoder_ffn_dim
        __UpperCamelCase     :List[str]			  =		decoder_layers
        __UpperCamelCase     :int			  =		decoder_attention_heads
        __UpperCamelCase     :str			  =		dropout
        __UpperCamelCase     :Any			  =		attention_dropout
        __UpperCamelCase     :int			  =		activation_dropout
        __UpperCamelCase     :List[Any]			  =		activation_function
        __UpperCamelCase     :List[Any]			  =		init_std
        __UpperCamelCase     :List[Any]			  =		init_xavier_std
        __UpperCamelCase     :int			  =		encoder_layerdrop
        __UpperCamelCase     :str			  =		auxiliary_loss
        __UpperCamelCase     :Optional[Any]			  =		position_embedding_type
        __UpperCamelCase     :Union[str, Any]			  =		backbone
        __UpperCamelCase     :Any			  =		use_pretrained_backbone
        __UpperCamelCase     :str			  =		dilation
        # deformable attributes
        __UpperCamelCase     :Optional[Any]			  =		num_feature_levels
        __UpperCamelCase     :str			  =		encoder_n_points
        __UpperCamelCase     :int			  =		decoder_n_points
        __UpperCamelCase     :Union[str, Any]			  =		two_stage
        __UpperCamelCase     :Optional[Any]			  =		two_stage_num_proposals
        __UpperCamelCase     :Dict			  =		with_box_refine
        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
        __UpperCamelCase     :Optional[int]			  =		class_cost
        __UpperCamelCase     :List[Any]			  =		bbox_cost
        __UpperCamelCase     :str			  =		giou_cost
        # Loss coefficients
        __UpperCamelCase     :Tuple			  =		mask_loss_coefficient
        __UpperCamelCase     :Tuple			  =		dice_loss_coefficient
        __UpperCamelCase     :int			  =		bbox_loss_coefficient
        __UpperCamelCase     :Any			  =		giou_loss_coefficient
        __UpperCamelCase     :Dict			  =		eos_coefficient
        __UpperCamelCase     :Optional[Any]			  =		focal_alpha
        __UpperCamelCase     :Optional[Any]			  =		disable_custom_kernels
        super().__init__(is_encoder_decoder=__lowercase , **__lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.encoder_attention_heads







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.d_model







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Dict			  =		copy.deepcopy(self.__dict__)
        if self.backbone_config is not None:
            __UpperCamelCase     :Tuple			  =		self.backbone_config.to_dict()
        __UpperCamelCase     :List[Any]			  =		self.__class__.model_type
        return output





