"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_:       Optional[int]			     =       logging.get_logger(__name__)







def        __a (		):






     '''simple docstring'''



     lowercase__								=				DetaConfig(
         num_queries=9_00     ,					encoder_ffn_dim=20_48     ,					decoder_ffn_dim=20_48     ,					num_feature_levels=5     ,					assign_first_stage=A     ,					with_box_refine=A     ,					two_stage=A     ,					)

     # set labels
     lowercase__								=				91
     lowercase__								=				"huggingface/label-files"
     lowercase__								=				"coco-detection-id2label.json"
     lowercase__								=				json.load(open(cached_download(hf_hub_url(A     ,					A     ,					repo_type="dataset"   )   )     ,					"r"   )   )
     lowercase__								=				{int(A   ): v for k, v in idalabel.items()}
     lowercase__								=				idalabel
     lowercase__								=				{v: k for k, v in idalabel.items()}

     return config







def        __a (		A   ):






     '''simple docstring'''



     lowercase__								=				[]

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")   )
     rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")   )
     rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")   )
     rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")   )
     rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                        )   )
    # transformer encoder
     for i in range(config.encoder_layers   ):
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')   )
          rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')   )

     # transformer decoder
     for i in range(config.decoder_layers   ):
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')   )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')   )

     # fmt: on

     return rename_keys







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				dct.pop(A   )
     lowercase__								=				val







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				config.d_model
     for i in range(config.decoder_layers   ):
          # read in weights + bias of input projection layer of self-attention
          lowercase__								=				state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''   )
          lowercase__								=				state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''   )
          # next, add query, keys and values (in that order) to the state dict
          lowercase__								=				in_proj_weight[:hidden_size, :]
          lowercase__								=				in_proj_bias[:hidden_size]
          lowercase__								=				in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          lowercase__								=				in_proj_bias[hidden_size : hidden_size * 2]
          lowercase__								=				in_proj_weight[-hidden_size:, :]
          lowercase__								=				in_proj_bias[-hidden_size:]







def        __a (		):






     '''simple docstring'''



     lowercase__								=				"http://images.cocodataset.org/val2017/000000039769.jpg"
     lowercase__								=				Image.open(requests.get(A     ,					stream=A   ).raw   )

     return im







@torch.no_grad()
def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				get_deta_config()

     # load original state dict
     if model_name == "deta-resnet-50":
          lowercase__								=				"adet_checkpoint0011.pth"
     elif model_name == "deta-resnet-50-24-epochs":
          lowercase__								=				"adet_2x_checkpoint0023.pth"
     else:
          raise ValueError(f'''Model name {model_name} not supported'''   )
     lowercase__								=				hf_hub_download(repo_id="nielsr/deta-checkpoints"     ,					filename=A   )
     lowercase__								=				torch.load(A     ,					map_location="cpu"   )["model"]

     # rename keys
     lowercase__								=				create_rename_keys(A   )
     for src, dest in rename_keys:
          rename_key(A     ,					A     ,					A   )
     read_in_decoder_q_k_v(A     ,					A   )

     # fix some prefixes
     for key in state_dict.copy().keys():
          if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
               lowercase__								=				state_dict.pop(A   )
               lowercase__								=				val
          if "input_proj" in key:
               lowercase__								=				state_dict.pop(A   )
               lowercase__								=				val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               lowercase__								=				state_dict.pop(A   )
               lowercase__								=				val

    # finally, create HuggingFace model and load state dict
     lowercase__								=				DetaForObjectDetection(A   )
     model.load_state_dict(A   )
     model.eval()

     lowercase__								=				"cuda" if torch.cuda.is_available() else "cpu"
     model.to(A   )

     # load image processor
     lowercase__								=				DetaImageProcessor(format="coco_detection"   )

     # verify our conversion on image
     lowercase__								=				prepare_img()
     lowercase__								=				processor(images=A     ,					return_tensors="pt"   )
     lowercase__								=				encoding["pixel_values"]
     lowercase__								=				model(pixel_values.to(A   )   )

     # verify logits
     if model_name == "deta-resnet-50":
          lowercase__								=				torch.tensor(
              [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]   )
          lowercase__								=				torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]   )
     elif model_name == "deta-resnet-50-24-epochs":
          lowercase__								=				torch.tensor(
              [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]   )
          lowercase__								=				torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]   )

     assert torch.allclose(outputs.logits[0, :3, :3]     ,					expected_logits.to(A   )     ,					atol=1e-4   )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,					expected_boxes.to(A   )     ,					atol=1e-4   )
     print("Everything ok!"   )

     if pytorch_dump_folder_path:
          # Save model and processor
          logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''   )
          Path(A   ).mkdir(exist_ok=A   )
          model.save_pretrained(A   )
          processor.save_pretrained(A   )

     # Push to hub
     if push_to_hub:
          print("Pushing model and processor to hub..."   )
          model.push_to_hub(f'''jozhang97/{model_name}'''   )
          processor.push_to_hub(f'''jozhang97/{model_name}'''   )


if __name__ == "__main__":
 lowerCAmelCase_:       Any			     =       argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     type=str,
     default="deta-resnet-50",
     choices=["deta-resnet-50", "deta-resnet-50-24-epochs"],
     help="Name of the 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 or not to push the converted model to the 🤗 hub."
 )
 lowerCAmelCase_:       Union[str, Any]			     =       parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCAmelCase_:       List[str]			     =       logging.get_logger(__name__)






class  a__     ( _a						):
   snake_case_				=   "speech-encoder-decoder"
   snake_case_				=   True


   def __init__(  self,					**_UpperCAmelCase	):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)
        if "encoder" not in kwargs or "decoder" not in kwargs:
             raise ValueError(
                 F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                 F''' `decoder` sub-configurations are passed, but only {kwargs}'''	)

        lowercase__								=				kwargs.pop("encoder"	)
        lowercase__								=				encoder_config.pop("model_type"	)
        lowercase__								=				kwargs.pop("decoder"	)
        lowercase__								=				decoder_config.pop("model_type"	)

        lowercase__								=				AutoConfig.for_model(_UpperCAmelCase,					**_UpperCAmelCase	)
        lowercase__								=				AutoConfig.for_model(_UpperCAmelCase,					**_UpperCAmelCase	)
        lowercase__								=				True


   @classmethod
   def 						snake_case__						(  cls,					_UpperCAmelCase,					_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



        logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"	)
        lowercase__								=				True
        lowercase__								=				True

        return cls(encoder=encoder_config.to_dict(),					decoder=decoder_config.to_dict(),					**_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.encoder.to_dict()
        lowercase__								=				self.decoder.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




