
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()
lowerCamelCase_				:							Dict       =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_				:							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        __lowercase(							__snake_case				:		str    ,__snake_case				:		Dict    ,__snake_case				:		Optional[Any]					)       ->   List[Any]:
       __snake_case						       =   state_dict.pop(lowerCAmelCase__					)
       __snake_case						       =   val
def        __lowercase(							__snake_case				:		Union[str, Any]					)       ->   Any:
       __snake_case						       =   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     __snake_case						       =   key.replace('backbone.0.body'    ,'backbone.conv_encoder.model'					)
                     __snake_case						       =   value
              else:
                     __snake_case						       =   value

       return new_state_dict
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[Any]=False					)       ->   Optional[int]:
       __snake_case						       =   ''
       if is_panoptic:
              __snake_case						       =   '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)
              __snake_case						       =   state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
              __snake_case						       =   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
              __snake_case						       =   in_proj_weight[:2_56, :]
              __snake_case						       =   in_proj_bias[:2_56]
              __snake_case						       =   in_proj_weight[2_56:5_12, :]
              __snake_case						       =   in_proj_bias[2_56:5_12]
              __snake_case						       =   in_proj_weight[-2_56:, :]
              __snake_case						       =   in_proj_bias[-2_56:]
       # 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
              __snake_case						       =   state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
              __snake_case						       =   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
              __snake_case						       =   in_proj_weight[:2_56, :]
              __snake_case						       =   in_proj_bias[:2_56]
              __snake_case						       =   in_proj_weight[2_56:5_12, :]
              __snake_case						       =   in_proj_bias[2_56:5_12]
              __snake_case						       =   in_proj_weight[-2_56:, :]
              __snake_case						       =   in_proj_bias[-2_56:]
              # read in weights + bias of input projection layer of cross-attention
              __snake_case						       =   state_dict.pop(
                  f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
              __snake_case						       =   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
              __snake_case						       =   in_proj_weight_cross_attn[:2_56, :]
              __snake_case						       =   in_proj_bias_cross_attn[:2_56]
              __snake_case						       =   in_proj_weight_cross_attn[2_56:5_12, :]
              __snake_case						       =   in_proj_bias_cross_attn[2_56:5_12]
              __snake_case						       =   in_proj_weight_cross_attn[-2_56:, :]
              __snake_case						       =   in_proj_bias_cross_attn[-2_56:]
def        __lowercase(							)       ->   Any:
       __snake_case						       =   'http://images.cocodataset.org/val2017/000000039769.jpg'
       __snake_case						       =   Image.open(requests.get(lowerCAmelCase__    ,stream=lowerCAmelCase__					).raw					)

       return im
@torch.no_grad()
def        __lowercase(							__snake_case				:		Any    ,__snake_case				:		Tuple					)       ->   str:
       __snake_case						       =   DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              __snake_case						       =   'resnet101'
       if "dc5" in model_name:
              __snake_case						       =   True
       __snake_case						       =   'panoptic' in model_name
       if is_panoptic:
              __snake_case						       =   2_50
       else:
              __snake_case						       =   91
              __snake_case						       =   'huggingface/label-files'
              __snake_case						       =   'coco-detection-id2label.json'
              __snake_case						       =   json.load(open(hf_hub_download(lowerCAmelCase__    ,lowerCAmelCase__    ,repo_type='dataset'					)    ,'r'					)					)
              __snake_case						       =   {int(lowerCAmelCase__					): v for k, v in idalabel.items()}
              __snake_case						       =   idalabel
              __snake_case						       =   {v: k for k, v in idalabel.items()}

       # load image processor
       __snake_case						       =   'coco_panoptic' if is_panoptic else 'coco_detection'
       __snake_case						       =   DetrImageProcessor(format=lowerCAmelCase__					)

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

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

       # load original model from torch hub
       __snake_case						       =   torch.hub.load('facebookresearch/detr'    ,lowerCAmelCase__    ,pretrained=lowerCAmelCase__					).eval()
       __snake_case						       =   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     __snake_case						       =   'detr.' + src
              rename_key(lowerCAmelCase__    ,lowerCAmelCase__    ,lowerCAmelCase__					)
       __snake_case						       =   rename_backbone_keys(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
       __snake_case						       =   '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'					)
                     ):
                            __snake_case						       =   state_dict.pop(lowerCAmelCase__					)
                            __snake_case						       =   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            __snake_case						       =   state_dict.pop(lowerCAmelCase__					)
                            __snake_case						       =   val
                     elif key.startswith('bbox_attention'					) or key.startswith('mask_head'					):
                            continue
                     else:
                            __snake_case						       =   state_dict.pop(lowerCAmelCase__					)
                            __snake_case						       =   val
              else:
                     if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
                            __snake_case						       =   state_dict.pop(lowerCAmelCase__					)
                            __snake_case						       =   val
              # finally, create HuggingFace model and load state dict
       __snake_case						       =   DetrForSegmentation(lowerCAmelCase__					) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__					)
       model.load_state_dict(lowerCAmelCase__					)
       model.eval()
       # verify our conversion
       __snake_case						       =   detr(lowerCAmelCase__					)
       __snake_case						       =   model(lowerCAmelCase__					)
       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(lowerCAmelCase__					).mkdir(exist_ok=lowerCAmelCase__					)
       model.save_pretrained(lowerCAmelCase__					)
       image_processor.save_pretrained(lowerCAmelCase__					)


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





"""simple docstring"""







def 							UpperCamelCase_       (       lowerCAmelCase__ :							int = 400_0000		)		->  int:

  """simple docstring"""




  if n <= 1:
    return 0
  lowerCAmelCase_  :							Union[str, Any]								=					0
  lowerCAmelCase_  :							Tuple								=					2
  lowerCAmelCase_  :							List[Any]								=					0
  while 4 * b + a <= n:
    lowerCAmelCase_		,lowerCAmelCase_  :							str								=					b, 4 * b + a
    count += a
  return count + b


if __name__ == "__main__":
     print(f'{solution() = }')





