




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


logging.set_verbosity_info()
snake_case_					:					Any       						=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case_					:					Union[str, Any]       						=			[]
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}.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"),
    ]
)


def   A		(__A  :       Union[str, Any]  ,							__A  :       Union[str, Any]  ,							__A  :       List[str]			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				state_dict.pop(__A			)
     UpperCAmelCase_		=				val


def   A		(__A  :       Optional[Any]			)  -> int:







     """simple docstring"""


     UpperCAmelCase_		=				OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCAmelCase_		=				key.replace('''backbone.0.body'''  ,							'''backbone.conv_encoder.model'''			)
               UpperCAmelCase_		=				value
          else:
               UpperCAmelCase_		=				value

     return new_state_dict


def   A		(__A  :       Dict  ,							__A  :       List[Any]=False			)  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_		=				''''''
     if is_panoptic:
          UpperCAmelCase_		=				'''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_		=				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
          UpperCAmelCase_		=				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_		=				in_proj_weight[:256, :]
          UpperCAmelCase_		=				in_proj_bias[:256]
          UpperCAmelCase_		=				in_proj_weight[256:512, :]
          UpperCAmelCase_		=				in_proj_bias[256:512]
          UpperCAmelCase_		=				in_proj_weight[-256:, :]
          UpperCAmelCase_		=				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_		=				state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
          UpperCAmelCase_		=				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_		=				in_proj_weight[:256, :]
          UpperCAmelCase_		=				in_proj_bias[:256]
          UpperCAmelCase_		=				in_proj_weight[256:512, :]
          UpperCAmelCase_		=				in_proj_bias[256:512]
          UpperCAmelCase_		=				in_proj_weight[-256:, :]
          UpperCAmelCase_		=				in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          UpperCAmelCase_		=				state_dict.pop(
              F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""			)
          UpperCAmelCase_		=				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_		=				in_proj_weight_cross_attn[:256, :]
          UpperCAmelCase_		=				in_proj_bias_cross_attn[:256]
          UpperCAmelCase_		=				in_proj_weight_cross_attn[256:512, :]
          UpperCAmelCase_		=				in_proj_bias_cross_attn[256:512]
          UpperCAmelCase_		=				in_proj_weight_cross_attn[-256:, :]
          UpperCAmelCase_		=				in_proj_bias_cross_attn[-256:]


def   A		()  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_		=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCAmelCase_		=				Image.open(requests.get(__A  ,							stream=__A			).raw			)

     return im


@torch.no_grad()
def   A		(__A  :       List[Any]  ,							__A  :       Any			)  -> Any:







     """simple docstring"""


     UpperCAmelCase_		=				DetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          UpperCAmelCase_		=				'''resnet101'''
     if "dc5" in model_name:
          UpperCAmelCase_		=				True
     UpperCAmelCase_		=				'''panoptic''' in model_name
     if is_panoptic:
          UpperCAmelCase_		=				250
     else:
          UpperCAmelCase_		=				91
          UpperCAmelCase_		=				'''huggingface/label-files'''
          UpperCAmelCase_		=				'''coco-detection-id2label.json'''
          UpperCAmelCase_		=				json.load(open(hf_hub_download(__A  ,							__A  ,							repo_type='''dataset'''			)  ,							'''r'''			)			)
          UpperCAmelCase_		=				{int(__A			): v for k, v in idalabel.items()}
          UpperCAmelCase_		=				idalabel
          UpperCAmelCase_		=				{v: k for k, v in idalabel.items()}

     # load image processor
     UpperCAmelCase_		=				'''coco_panoptic''' if is_panoptic else '''coco_detection'''
     UpperCAmelCase_		=				DetrImageProcessor(format=__A			)

     # prepare image
     UpperCAmelCase_		=				prepare_img()
     UpperCAmelCase_		=				image_processor(images=__A  ,							return_tensors='''pt'''			)
     UpperCAmelCase_		=				encoding['''pixel_values''']

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

     # load original model from torch hub
     UpperCAmelCase_		=				torch.hub.load('''facebookresearch/detr'''  ,							__A  ,							pretrained=__A			).eval()
     UpperCAmelCase_		=				detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               UpperCAmelCase_		=				'''detr.''' + src
          rename_key(__A  ,							__A  ,							__A			)
     UpperCAmelCase_		=				rename_backbone_keys(__A			)
     # query, key and value matrices need special treatment
     read_in_q_k_v(__A  ,							is_panoptic=__A			)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCAmelCase_		=				'''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_		=				state_dict.pop(__A			)
                    UpperCAmelCase_		=				val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCAmelCase_		=				state_dict.pop(__A			)
                    UpperCAmelCase_		=				val
               elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
                    continue
               else:
                    UpperCAmelCase_		=				state_dict.pop(__A			)
                    UpperCAmelCase_		=				val
          else:
               if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
                    UpperCAmelCase_		=				state_dict.pop(__A			)
                    UpperCAmelCase_		=				val
    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_		=				DetrForSegmentation(__A			) if is_panoptic else DetrForObjectDetection(__A			)
     model.load_state_dict(__A			)
     model.eval()
     # verify our conversion
     UpperCAmelCase_		=				detr(__A			)
     UpperCAmelCase_		=				model(__A			)
     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(__A			).mkdir(exist_ok=__A			)
     model.save_pretrained(__A			)
     image_processor.save_pretrained(__A			)


if __name__ == "__main__":
    snake_case_					:					Union[str, Any]       						=			argparse.ArgumentParser()

    parser.add_argument(
        "--model_name", default="detr_resnet50", type=str, 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."
    )
    snake_case_					:					Dict       						=			parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 491.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 32) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 32) * 5 / 9) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 273.15) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 491.67) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 5 / 9)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25 + 273.15)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32 + 459.67)  ,							__A			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






