

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()
_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}.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 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Optional[Any] = state_dict.pop(lowercase__						)
      __lowerCAmelCase :						str = val







def 		_lowercase			(					lowercase__						):
      __lowerCAmelCase :						Any = OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  __lowerCAmelCase :						str = key.replace('''backbone.0.body'''       ,     '''backbone.conv_encoder.model'''						)
                  __lowerCAmelCase :						str = value
            else:
                  __lowerCAmelCase :						int = value

      return new_state_dict







def 		_lowercase			(					lowercase__       ,     lowercase__=False						):
      __lowerCAmelCase :						Dict = ''''''
      if is_panoptic:
            __lowerCAmelCase :						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)
            __lowerCAmelCase :						int = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
            __lowerCAmelCase :						Any = 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
            __lowerCAmelCase :						Union[str, Any] = in_proj_weight[:2_5_6, :]
            __lowerCAmelCase :						List[Any] = in_proj_bias[:2_5_6]
            __lowerCAmelCase :						List[Any] = in_proj_weight[2_5_6:5_1_2, :]
            __lowerCAmelCase :						int = in_proj_bias[2_5_6:5_1_2]
            __lowerCAmelCase :						Optional[Any] = in_proj_weight[-2_5_6:, :]
            __lowerCAmelCase :						Union[str, Any] = in_proj_bias[-2_5_6:]
      # 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
            __lowerCAmelCase :						int = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
            __lowerCAmelCase :						List[Any] = 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
            __lowerCAmelCase :						Dict = in_proj_weight[:2_5_6, :]
            __lowerCAmelCase :						Dict = in_proj_bias[:2_5_6]
            __lowerCAmelCase :						List[Any] = in_proj_weight[2_5_6:5_1_2, :]
            __lowerCAmelCase :						Tuple = in_proj_bias[2_5_6:5_1_2]
            __lowerCAmelCase :						int = in_proj_weight[-2_5_6:, :]
            __lowerCAmelCase :						List[str] = in_proj_bias[-2_5_6:]
            # read in weights + bias of input projection layer of cross-attention
            __lowerCAmelCase :						Union[str, Any] = state_dict.pop(
                f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""						)
            __lowerCAmelCase :						Tuple = 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
            __lowerCAmelCase :						Dict = in_proj_weight_cross_attn[:2_5_6, :]
            __lowerCAmelCase :						Tuple = in_proj_bias_cross_attn[:2_5_6]
            __lowerCAmelCase :						Optional[Any] = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
            __lowerCAmelCase :						Tuple = in_proj_bias_cross_attn[2_5_6:5_1_2]
            __lowerCAmelCase :						List[str] = in_proj_weight_cross_attn[-2_5_6:, :]
            __lowerCAmelCase :						Tuple = in_proj_bias_cross_attn[-2_5_6:]







def 		_lowercase			(					):
      __lowerCAmelCase :						Optional[int] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __lowerCAmelCase :						Union[str, Any] = Image.open(requests.get(lowercase__       ,     stream=lowercase__						).raw						)

      return im







@torch.no_grad()
def 		_lowercase			(					lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Any = DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            __lowerCAmelCase :						Union[str, Any] = '''resnet101'''
      if "dc5" in model_name:
            __lowerCAmelCase :						Optional[int] = True
      __lowerCAmelCase :						List[Any] = '''panoptic''' in model_name
      if is_panoptic:
            __lowerCAmelCase :						Dict = 2_5_0
      else:
            __lowerCAmelCase :						str = 9_1
            __lowerCAmelCase :						Optional[int] = '''huggingface/label-files'''
            __lowerCAmelCase :						Union[str, Any] = '''coco-detection-id2label.json'''
            __lowerCAmelCase :						Optional[Any] = json.load(open(hf_hub_download(lowercase__       ,     lowercase__       ,     repo_type='''dataset'''						)       ,     '''r'''						)						)
            __lowerCAmelCase :						Optional[Any] = {int(lowercase__						): v for k, v in idalabel.items()}
            __lowerCAmelCase :						Any = idalabel
            __lowerCAmelCase :						str = {v: k for k, v in idalabel.items()}

      # load image processor
      __lowerCAmelCase :						Tuple = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
      __lowerCAmelCase :						int = DetrImageProcessor(format=lowercase__						)

      # prepare image
      __lowerCAmelCase :						Dict = prepare_img()
      __lowerCAmelCase :						List[str] = image_processor(images=lowercase__       ,     return_tensors='''pt'''						)
      __lowerCAmelCase :						Dict = encoding['''pixel_values''']

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

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


if __name__ == "__main__":
 _UpperCamelCase			      =		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."
 )
 _UpperCamelCase			      =		parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





def 		_lowercase			(					lowercase__       ,     lowercase__						):
      if len(str(lowercase__						)						) == 1 or len(str(lowercase__						)						) == 1:
            return a * b

      __lowerCAmelCase :						Any = max(len(str(lowercase__						)						)       ,     len(str(lowercase__						)						)						)
      __lowerCAmelCase :						Optional[int] = ma // 2

      __lowerCAmelCase,							__lowerCAmelCase :						Tuple = divmod(lowercase__       ,     1_0**ma						)
      __lowerCAmelCase,							__lowerCAmelCase :						Any = divmod(lowercase__       ,     1_0**ma						)

      __lowerCAmelCase :						str = karatsuba(lowercase__       ,     lowercase__						)
      __lowerCAmelCase :						Optional[int] = karatsuba((aa + aa)       ,     (ba + ba)						)
      __lowerCAmelCase :						List[Any] = karatsuba(lowercase__       ,     lowercase__						)

      return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)







def 		_lowercase			(					):
      print(karatsuba(1_5_4_6_3       ,     2_3_4_8_9						)						)


if __name__ == "__main__":
 main()


