






'''simple docstring'''

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()
UpperCamelCase      					=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase      					=[]
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 				snake_case       (  a_				:			str     , a_				:			Union[str, Any]     , a_				:			Tuple						)							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				state_dict.pop(a_						)
      UpperCamelCase_					: str            =				val



def 				snake_case       (  a_				:			Optional[Any]						)							->					List[str]:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  UpperCamelCase_					: int            =				key.replace("""backbone.0.body"""     , """backbone.conv_encoder.model"""						)
                  UpperCamelCase_					: Optional[int]            =				value
            else:
                  UpperCamelCase_					: Union[str, Any]            =				value

      return new_state_dict



def 				snake_case       (  a_				:			List[Any]     , a_				:			Tuple=False						)							->					str:





      """simple docstring"""

      UpperCamelCase_					: 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_					: Dict            =				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_					: Optional[int]            =				in_proj_weight[:256, :]
            UpperCamelCase_					: int            =				in_proj_bias[:256]
            UpperCamelCase_					: Dict            =				in_proj_weight[256:512, :]
            UpperCamelCase_					: Tuple            =				in_proj_bias[256:512]
            UpperCamelCase_					: Tuple            =				in_proj_weight[-256:, :]
            UpperCamelCase_					: Union[str, Any]            =				in_proj_bias[-256:]



def 				snake_case       (  )							->					Optional[int]:





      """simple docstring"""

      UpperCamelCase_					: Any            =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase_					: Dict            =				Image.open(requests.get(a_     , stream=a_						).raw						)

      return im



@torch.no_grad()
def 				snake_case       (  a_				:			Union[str, Any]     , a_				:			Any						)							->					Tuple:





      """simple docstring"""

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

      # load image processor
      UpperCamelCase_					: List[str]            =				"""coco_panoptic""" if is_panoptic else """coco_detection"""
      UpperCamelCase_					: Optional[int]            =				ConditionalDetrImageProcessor(format=a_						)

      # prepare image
      UpperCamelCase_					: Optional[int]            =				prepare_img()
      UpperCamelCase_					: Optional[Any]            =				image_processor(images=a_     , return_tensors="""pt"""						)
      UpperCamelCase_					: List[str]            =				encoding["""pixel_values"""]

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

      # load original model from torch hub
      UpperCamelCase_					: int            =				torch.hub.load("""DeppMeng/ConditionalDETR"""     , a_     , pretrained=a_						).eval()
      UpperCamelCase_					: Optional[Any]            =				conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  UpperCamelCase_					: Any            =				"""conditional_detr.""" + src
            rename_key(a_     , a_     , a_						)
      UpperCamelCase_					: Union[str, Any]            =				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_					: int            =				"""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_					: List[str]            =				state_dict.pop(a_						)
                        UpperCamelCase_					: List[Any]            =				val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        UpperCamelCase_					: Optional[Any]            =				state_dict.pop(a_						)
                        UpperCamelCase_					: Optional[int]            =				val
                  elif key.startswith("""bbox_attention"""						) or key.startswith("""mask_head"""						):
                        continue
                  else:
                        UpperCamelCase_					: Optional[int]            =				state_dict.pop(a_						)
                        UpperCamelCase_					: str            =				val
            else:
                  if not key.startswith("""class_labels_classifier"""						) and not key.startswith("""bbox_predictor"""						):
                        UpperCamelCase_					: int            =				state_dict.pop(a_						)
                        UpperCamelCase_					: Any            =				val
    # finally, create HuggingFace model and load state dict
      UpperCamelCase_					: List[str]            =				ConditionalDetrForSegmentation(a_						) if is_panoptic else ConditionalDetrForObjectDetection(a_						)
      model.load_state_dict(a_						)
      model.eval()
      model.push_to_hub(repo_id=a_     , organization="""DepuMeng"""     , commit_message="""Add model"""						)
      # verify our conversion
      UpperCamelCase_					: Optional[Any]            =				conditional_detr(a_						)
      UpperCamelCase_					: str            =				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__":
      UpperCamelCase      					=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."
      )
      UpperCamelCase      					=parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)














'''simple docstring'''

from abc import ABC, abstractmethod
from argparse import ArgumentParser




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 @staticmethod
 @abstractmethod
 def 	_UpperCAmelCase							(				__lowerCAmelCase			):
       raise NotImplementedError()





 @abstractmethod
 def 	_UpperCAmelCase							(				self			):
       raise NotImplementedError()






