






import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:							int				=logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Tuple:
     # initialize config
     if "resnet-50" in model_name:
          UpperCamelCase__			:   Optional[int]       				=  ResNetConfig.from_pretrained("microsoft/resnet-50"			)
     elif "resnet-101" in model_name:
          UpperCamelCase__			:   Optional[Any]       				=  ResNetConfig.from_pretrained("microsoft/resnet-101"			)
     else:
          raise ValueError("Model name should include either resnet50 or resnet101"			)

     UpperCamelCase__			:   Tuple       				=  DetrConfig(use_timm_backbone=__lowerCAmelCase					,    backbone_config=__lowerCAmelCase			)

     # set label attributes
     UpperCamelCase__			:   Tuple       				=  "panoptic" in model_name
     if is_panoptic:
          UpperCamelCase__			:   Any       				=  250
     else:
          UpperCamelCase__			:   Dict       				=  91
          UpperCamelCase__			:   Optional[int]       				=  "huggingface/label-files"
          UpperCamelCase__			:   Any       				=  "coco-detection-id2label.json"
          UpperCamelCase__			:   int       				=  json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			)			)
          UpperCamelCase__			:   int       				=  {int(__lowerCAmelCase			): v for k, v in idalabel.items()}
          UpperCamelCase__			:   Any       				=  idalabel
          UpperCamelCase__			:   Optional[Any]       				=  {v: k for k, v in idalabel.items()}

     return config, is_panoptic







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Any:
     # here we list all keys to be renamed (original name on the left, our name on the right)
     UpperCamelCase__			:   str       				=  []

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")			)
     rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")			)
     rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")			)
     rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")			)
     rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")			)
     # stages
     for stage_idx in range(len(config.backbone_config.depths			)			):
          for layer_idx in range(config.backbone_config.depths[stage_idx]			):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                        )			)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                        )			)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                        )			)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                        )			)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                        )			)
               # 3 convs
               for i in range(3			):
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                        )			)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                        )			)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                        )			)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                        )			)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                        )			)
    # fmt: on

     for i in range(config.encoder_layers			):
          # 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}.multihead_attn.out_proj.weight',
                  f'decoder.layers.{i}.encoder_attn.out_proj.weight',
              )			)
          rename_keys.append(
              (
                  f'transformer.decoder.layers.{i}.multihead_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')			)

     # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
     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"),
         ]			)

     return rename_keys







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   List[str]       				=  state_dict.pop(__lowerCAmelCase			)
     UpperCamelCase__			:   List[str]       				=  val







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase=False			) ->	str:
     UpperCamelCase__			:   str       				=  ""
     if is_panoptic:
          UpperCamelCase__			:   Dict       				=  "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__			:   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__			:   Union[str, Any]       				=  in_proj_weight[:256, :]
          UpperCamelCase__			:   Any       				=  in_proj_bias[:256]
          UpperCamelCase__			:   Tuple       				=  in_proj_weight[256:512, :]
          UpperCamelCase__			:   Union[str, Any]       				=  in_proj_bias[256:512]
          UpperCamelCase__			:   Optional[int]       				=  in_proj_weight[-256:, :]
          UpperCamelCase__			:   Union[str, Any]       				=  in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6			):
          # read in weights + bias of input projection layer of self-attention
          UpperCamelCase__			:   str       				=  state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
          UpperCamelCase__			:   int       				=  state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'			)
          # next, add query, keys and values (in that order) to the state dict
          UpperCamelCase__			:   List[str]       				=  in_proj_weight[:256, :]
          UpperCamelCase__			:   Dict       				=  in_proj_bias[:256]
          UpperCamelCase__			:   Optional[Any]       				=  in_proj_weight[256:512, :]
          UpperCamelCase__			:   List[str]       				=  in_proj_bias[256:512]
          UpperCamelCase__			:   Dict       				=  in_proj_weight[-256:, :]
          UpperCamelCase__			:   Any       				=  in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          UpperCamelCase__			:   int       				=  state_dict.pop(
              f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'			)
          UpperCamelCase__			:   int       				=  state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'			)
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          UpperCamelCase__			:   List[str]       				=  in_proj_weight_cross_attn[:256, :]
          UpperCamelCase__			:   str       				=  in_proj_bias_cross_attn[:256]
          UpperCamelCase__			:   Tuple       				=  in_proj_weight_cross_attn[256:512, :]
          UpperCamelCase__			:   int       				=  in_proj_bias_cross_attn[256:512]
          UpperCamelCase__			:   Optional[Any]       				=  in_proj_weight_cross_attn[-256:, :]
          UpperCamelCase__			:   Tuple       				=  in_proj_bias_cross_attn[-256:]







def 		SCREAMING_SNAKE_CASE							(					) ->	str:
     UpperCamelCase__			:   Tuple       				=  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCamelCase__			:   List[Any]       				=  Image.open(requests.get(__lowerCAmelCase					,    stream=__lowerCAmelCase			).raw			)

     return im







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase=None					,    __lowerCAmelCase=False			) ->	List[str]:
     UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  get_detr_config(__lowerCAmelCase			)

     # load original model from torch hub
     UpperCamelCase__			:   int       				=  {
         "detr-resnet-50": "detr_resnet50",
         "detr-resnet-101": "detr_resnet101",
     }
     logger.info(f'Converting model {model_name}...'			)
     UpperCamelCase__			:   Optional[int]       				=  torch.hub.load("facebookresearch/detr"					,    model_name_to_original_name[model_name]					,    pretrained=__lowerCAmelCase			).eval()
     UpperCamelCase__			:   Union[str, Any]       				=  detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(__lowerCAmelCase			):
          if is_panoptic:
               UpperCamelCase__			:   List[Any]       				=  "detr." + src
          rename_key(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     # query, key and value matrices need special treatment
     read_in_q_k_v(__lowerCAmelCase					,    is_panoptic=__lowerCAmelCase			)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCamelCase__			:   Dict       				=  "detr.model." if is_panoptic else "model."
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("detr"			)
                   and not key.startswith("class_labels_classifier"			)
                   and not key.startswith("bbox_predictor"			)
               ):
                    UpperCamelCase__			:   Optional[Any]       				=  state_dict.pop(__lowerCAmelCase			)
                    UpperCamelCase__			:   int       				=  val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCamelCase__			:   Tuple       				=  state_dict.pop(__lowerCAmelCase			)
                    UpperCamelCase__			:   List[Any]       				=  val
               elif key.startswith("bbox_attention"			) or key.startswith("mask_head"			):
                    continue
               else:
                    UpperCamelCase__			:   Optional[Any]       				=  state_dict.pop(__lowerCAmelCase			)
                    UpperCamelCase__			:   Any       				=  val
          else:
               if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
                    UpperCamelCase__			:   int       				=  state_dict.pop(__lowerCAmelCase			)
                    UpperCamelCase__			:   str       				=  val

    # finally, create HuggingFace model and load state dict
     UpperCamelCase__			:   Optional[int]       				=  DetrForSegmentation(__lowerCAmelCase			) if is_panoptic else DetrForObjectDetection(__lowerCAmelCase			)
     model.load_state_dict(__lowerCAmelCase			)
     model.eval()

     # verify our conversion on an image
     UpperCamelCase__			:   Optional[int]       				=  "coco_panoptic" if is_panoptic else "coco_detection"
     UpperCamelCase__			:   Dict       				=  DetrImageProcessor(format=__lowerCAmelCase			)

     UpperCamelCase__			:   Optional[Any]       				=  processor(images=prepare_img()					,    return_tensors="pt"			)
     UpperCamelCase__			:   Union[str, Any]       				=  encoding["pixel_values"]

     UpperCamelCase__			:   str       				=  detr(__lowerCAmelCase			)
     UpperCamelCase__			:   Tuple       				=  model(__lowerCAmelCase			)

     assert torch.allclose(outputs.logits					,    original_outputs["pred_logits"]					,    atol=1E-3			)
     assert torch.allclose(outputs.pred_boxes					,    original_outputs["pred_boxes"]					,    atol=1E-3			)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks					,    original_outputs["pred_masks"]					,    atol=1E-4			)
     print("Looks ok!"			)

     if pytorch_dump_folder_path is not None:
          # Save model and image processor
          logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'			)
          Path(__lowerCAmelCase			).mkdir(exist_ok=__lowerCAmelCase			)
          model.save_pretrained(__lowerCAmelCase			)
          processor.save_pretrained(__lowerCAmelCase			)

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info("Uploading PyTorch model and image processor to the hub..."			)
          model.push_to_hub(f'nielsr/{model_name}'			)
          processor.push_to_hub(f'nielsr/{model_name}'			)


if __name__ == "__main__":
 lowerCamelCase	:							str				=argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''detr-resnet-50''',
     type=str,
     choices=['''detr-resnet-50''', '''detr-resnet-101'''],
     help='''Name of the 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.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
 lowerCamelCase	:							int				=parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCamelCase	:							Tuple				=logging.get_logger(__name__)

lowerCamelCase	:							Optional[Any]				={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Optional[Any]        = '''conditional_detr'''
 _lowerCAmelCase       :       List[Any]        = ['''past_key_values''']
 _lowerCAmelCase       :       List[str]        = {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }







 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Tuple=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=3							,   SCREAMING_SNAKE_CASE					:   Any=3_00							,   SCREAMING_SNAKE_CASE					:   Tuple=6							,   SCREAMING_SNAKE_CASE					:   int=20_48							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=8							,   SCREAMING_SNAKE_CASE					:   int=6							,   SCREAMING_SNAKE_CASE					:   Dict=20_48							,   SCREAMING_SNAKE_CASE					:   Optional[int]=8							,   SCREAMING_SNAKE_CASE					:   Dict=0.0							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   int="relu"							,   SCREAMING_SNAKE_CASE					:   Any=2_56							,   SCREAMING_SNAKE_CASE					:   int=0.1							,   SCREAMING_SNAKE_CASE					:   int=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=0.0							,   SCREAMING_SNAKE_CASE					:   List[str]=0.0_2							,   SCREAMING_SNAKE_CASE					:   Optional[int]=1.0							,   SCREAMING_SNAKE_CASE					:   List[str]=False							,   SCREAMING_SNAKE_CASE					:   int="sine"							,   SCREAMING_SNAKE_CASE					:   str="resnet50"							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   Tuple=False							,   SCREAMING_SNAKE_CASE					:   Tuple=2							,   SCREAMING_SNAKE_CASE					:   Tuple=5							,   SCREAMING_SNAKE_CASE					:   List[str]=2							,   SCREAMING_SNAKE_CASE					:   List[str]=1							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=1							,   SCREAMING_SNAKE_CASE					:   str=2							,   SCREAMING_SNAKE_CASE					:   Any=5							,   SCREAMING_SNAKE_CASE					:   int=2							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.2_5							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      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__			:   Tuple       				=  CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
           elif isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Union[str, Any]       				=  backbone_config.get("model_type"						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING[backbone_model_type]
                UpperCamelCase__			:   Any       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  use_timm_backbone
      UpperCamelCase__			:   List[Any]       				=  backbone_config
      UpperCamelCase__			:   Tuple       				=  num_channels
      UpperCamelCase__			:   List[Any]       				=  num_queries
      UpperCamelCase__			:   Dict       				=  d_model
      UpperCamelCase__			:   Any       				=  encoder_ffn_dim
      UpperCamelCase__			:   List[str]       				=  encoder_layers
      UpperCamelCase__			:   List[str]       				=  encoder_attention_heads
      UpperCamelCase__			:   Optional[int]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  decoder_attention_heads
      UpperCamelCase__			:   int       				=  dropout
      UpperCamelCase__			:   Optional[int]       				=  attention_dropout
      UpperCamelCase__			:   Any       				=  activation_dropout
      UpperCamelCase__			:   int       				=  activation_function
      UpperCamelCase__			:   int       				=  init_std
      UpperCamelCase__			:   List[Any]       				=  init_xavier_std
      UpperCamelCase__			:   List[str]       				=  encoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  decoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  encoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  auxiliary_loss
      UpperCamelCase__			:   List[str]       				=  position_embedding_type
      UpperCamelCase__			:   Optional[Any]       				=  backbone
      UpperCamelCase__			:   Optional[int]       				=  use_pretrained_backbone
      UpperCamelCase__			:   List[Any]       				=  dilation
      # Hungarian matcher
      UpperCamelCase__			:   List[str]       				=  class_cost
      UpperCamelCase__			:   Union[str, Any]       				=  bbox_cost
      UpperCamelCase__			:   int       				=  giou_cost
      # Loss coefficients
      UpperCamelCase__			:   str       				=  mask_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  dice_loss_coefficient
      UpperCamelCase__			:   int       				=  cls_loss_coefficient
      UpperCamelCase__			:   Tuple       				=  bbox_loss_coefficient
      UpperCamelCase__			:   Any       				=  giou_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  focal_alpha
      super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






      return self.encoder_attention_heads







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return self.d_model







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  copy.deepcopy(self.__dict__						)
      if self.backbone_config is not None:
           UpperCamelCase__			:   List[str]       				=  self.backbone_config.to_dict()
      UpperCamelCase__			:   int       				=  self.__class__.model_type
      return output







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = version.parse('''1.11'''       )







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ("pixel_mask", {0: "batch"}),
          ]						)







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return 1e-5







 @property
 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return 12