import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase							 =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowercase							 =					[]
for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append(
        (
            F'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
            F'decoder.layers.{i}.encoder_attn.out_proj.weight',
        )
    )
    rename_keys.append(
        (
            F'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
            F'decoder.layers.{i}.encoder_attn.out_proj.bias',
        )
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
    )

    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		state_dict.pop(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Union[str, Any]			  =		val



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            __UpperCamelCase     :str			  =		key.replace('''backbone.0.body'''  ,			'''backbone.conv_encoder.model'''	)
            __UpperCamelCase     :Optional[Any]			  =		value
        else:
            __UpperCamelCase     :Any			  =		value

    return new_state_dict



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		''''''
    if is_panoptic:
        __UpperCamelCase     :Dict			  =		'''conditional_detr.'''

    # first: transformer encoder
    for i in range(6	):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        __UpperCamelCase     :Dict			  =		state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
        __UpperCamelCase     :Optional[int]			  =		state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""	)
        # next, add query, keys and values (in that order) to the state dict
        __UpperCamelCase     :int			  =		in_proj_weight[:256, :]
        __UpperCamelCase     :Union[str, Any]			  =		in_proj_bias[:256]
        __UpperCamelCase     :Any			  =		in_proj_weight[256:512, :]
        __UpperCamelCase     :Optional[int]			  =		in_proj_bias[256:512]
        __UpperCamelCase     :str			  =		in_proj_weight[-256:, :]
        __UpperCamelCase     :Optional[Any]			  =		in_proj_bias[-256:]



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase     :int			  =		Image.open(requests.get(SCREAMING_SNAKE_CASE  ,			stream=SCREAMING_SNAKE_CASE	).raw	)

    return im



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        __UpperCamelCase     :List[str]			  =		'''resnet101'''
    if "dc5" in model_name:
        __UpperCamelCase     :Dict			  =		True
    __UpperCamelCase     :Dict			  =		'''panoptic''' in model_name
    if is_panoptic:
        __UpperCamelCase     :Optional[int]			  =		250
    else:
        __UpperCamelCase     :Dict			  =		91
        __UpperCamelCase     :List[str]			  =		'''huggingface/label-files'''
        __UpperCamelCase     :Union[str, Any]			  =		'''coco-detection-id2label.json'''
        __UpperCamelCase     :Union[str, Any]			  =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			repo_type='''dataset'''	)  ,			'''r'''	)	)
        __UpperCamelCase     :List[Any]			  =		{int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
        __UpperCamelCase     :str			  =		idalabel
        __UpperCamelCase     :Tuple			  =		{v: k for k, v in idalabel.items()}

    # load image processor
    __UpperCamelCase     :Optional[Any]			  =		'''coco_panoptic''' if is_panoptic else '''coco_detection'''
    __UpperCamelCase     :Any			  =		ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE	)

    # prepare image
    __UpperCamelCase     :Tuple			  =		prepare_img()
    __UpperCamelCase     :str			  =		image_processor(images=SCREAMING_SNAKE_CASE  ,			return_tensors='''pt'''	)
    __UpperCamelCase     :str			  =		encoding['''pixel_values''']

    logger.info(f"""Converting model {model_name}..."""	)

    # load original model from torch hub
    __UpperCamelCase     :Dict			  =		torch.hub.load('''DeppMeng/ConditionalDETR'''  ,			SCREAMING_SNAKE_CASE  ,			pretrained=SCREAMING_SNAKE_CASE	).eval()
    __UpperCamelCase     :Tuple			  =		conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            __UpperCamelCase     :List[str]			  =		'''conditional_detr.''' + src
        rename_key(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		rename_backbone_keys(SCREAMING_SNAKE_CASE	)
    # query, key and value matrices need special treatment
    read_in_q_k_v(SCREAMING_SNAKE_CASE  ,			is_panoptic=SCREAMING_SNAKE_CASE	)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    __UpperCamelCase     :Union[str, Any]			  =		'''conditional_detr.model.''' if is_panoptic else '''model.'''
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('''conditional_detr'''	)
                and not key.startswith('''class_labels_classifier'''	)
                and not key.startswith('''bbox_predictor'''	)
            ):
                __UpperCamelCase     :Optional[Any]			  =		state_dict.pop(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :str			  =		val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                __UpperCamelCase     :List[Any]			  =		state_dict.pop(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Optional[int]			  =		val
            elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
                continue
            else:
                __UpperCamelCase     :int			  =		state_dict.pop(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :List[str]			  =		val
        else:
            if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
                __UpperCamelCase     :Optional[Any]			  =		state_dict.pop(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :List[str]			  =		val
    # finally, create HuggingFace model and load state dict
    __UpperCamelCase     :Union[str, Any]			  =		ConditionalDetrForSegmentation(SCREAMING_SNAKE_CASE	) if is_panoptic else ConditionalDetrForObjectDetection(SCREAMING_SNAKE_CASE	)
    model.load_state_dict(SCREAMING_SNAKE_CASE	)
    model.eval()
    model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE  ,			organization='''DepuMeng'''  ,			commit_message='''Add model'''	)
    # verify our conversion
    __UpperCamelCase     :Optional[int]			  =		conditional_detr(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		model(SCREAMING_SNAKE_CASE	)
    assert torch.allclose(outputs.logits  ,			original_outputs['''pred_logits''']  ,			atol=1e-4	)
    assert torch.allclose(outputs.pred_boxes  ,			original_outputs['''pred_boxes''']  ,			atol=1e-4	)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks  ,			original_outputs['''pred_masks''']  ,			atol=1e-4	)

    # Save model and image processor
    logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""	)
    Path(SCREAMING_SNAKE_CASE	).mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
    model.save_pretrained(SCREAMING_SNAKE_CASE	)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()

    parser.add_argument(
        '''--model_name''',
        default='''conditional_detr_resnet50''',
        type=str,
        help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
    )
    __lowercase							 =					parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






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





