





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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_ 							=	state_dict.pop(UpperCamelCase_	)
       UpperCamelCase_ 							=	val




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		List[str]:
       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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_=False	)  ->		Optional[Any]:
       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 			lowerCAmelCase_			(				)  ->		Optional[Any]:
       UpperCamelCase_ 							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCamelCase_ 							=	Image.open(requests.get(UpperCamelCase_       ,		stream=UpperCamelCase_	).raw	)

       return im




@torch.no_grad()
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       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(UpperCamelCase_       ,		UpperCamelCase_       ,		repo_type="dataset"	)       ,		"r"	)	)
              UpperCamelCase_ 							=	{int(UpperCamelCase_	): 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=UpperCamelCase_	)

       # prepare image
       UpperCamelCase_ 							=	prepare_img()
       UpperCamelCase_ 							=	image_processor(images=UpperCamelCase_       ,		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"       ,		UpperCamelCase_       ,		pretrained=UpperCamelCase_	).eval()
       UpperCamelCase_ 							=	detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     UpperCamelCase_ 							=	"detr." + src
              rename_key(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	rename_backbone_keys(UpperCamelCase_	)
       # query, key and value matrices need special treatment
       read_in_q_k_v(UpperCamelCase_       ,		is_panoptic=UpperCamelCase_	)
       # 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(UpperCamelCase_	)
                            UpperCamelCase_ 							=	val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            UpperCamelCase_ 							=	state_dict.pop(UpperCamelCase_	)
                            UpperCamelCase_ 							=	val
                     elif key.startswith("bbox_attention"	) or key.startswith("mask_head"	):
                            continue
                     else:
                            UpperCamelCase_ 							=	state_dict.pop(UpperCamelCase_	)
                            UpperCamelCase_ 							=	val
              else:
                     if not key.startswith("class_labels_classifier"	) and not key.startswith("bbox_predictor"	):
                            UpperCamelCase_ 							=	state_dict.pop(UpperCamelCase_	)
                            UpperCamelCase_ 							=	val
    # finally, create HuggingFace model and load state dict
       UpperCamelCase_ 							=	DetrForSegmentation(UpperCamelCase_	) if is_panoptic else DetrForObjectDetection(UpperCamelCase_	)
       model.load_state_dict(UpperCamelCase_	)
       model.eval()
       # verify our conversion
       UpperCamelCase_ 							=	detr(UpperCamelCase_	)
       UpperCamelCase_ 							=	model(UpperCamelCase_	)
       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(UpperCamelCase_	).mkdir(exist_ok=UpperCamelCase_	)
       model.save_pretrained(UpperCamelCase_	)
       image_processor.save_pretrained(UpperCamelCase_	)


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)


'''simple docstring'''
def 				__UpperCamelCase (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float			)							->	list[int]:
  if hue < 0 or hue > 360:
    raise Exception('''hue should be between 0 and 360'''			)

  if saturation < 0 or saturation > 1:
    raise Exception('''saturation should be between 0 and 1'''			)

  if value < 0 or value > 1:
    raise Exception('''value should be between 0 and 1'''			)

  lowercase				:							List[str]									=   value * saturation
  lowercase				:							Optional[Any]									=   hue / 60
  lowercase				:							Dict									=   chroma * (1 - abs(hue_section % 2 - 1			))
  lowercase				:							List[Any]									=   value - chroma

  if hue_section >= 0 and hue_section <= 1:
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							int									=   round(255 * (match_value)			)
  elif hue_section > 1 and hue_section <= 2:
    lowercase				:							Optional[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Any									=   round(255 * (chroma + match_value)			)
    lowercase				:							Any									=   round(255 * (match_value)			)
  elif hue_section > 2 and hue_section <= 3:
    lowercase				:							Dict									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
  elif hue_section > 3 and hue_section <= 4:
    lowercase				:							List[str]									=   round(255 * (match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (chroma + match_value)			)
  elif hue_section > 4 and hue_section <= 5:
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
  else:
    lowercase				:							int									=   round(255 * (chroma + match_value)			)
    lowercase				:							Optional[Any]									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)

  return [red, green, blue]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[float]:
  if red < 0 or red > 255:
    raise Exception('''red should be between 0 and 255'''			)

  if green < 0 or green > 255:
    raise Exception('''green should be between 0 and 255'''			)

  if blue < 0 or blue > 255:
    raise Exception('''blue should be between 0 and 255'''			)

  lowercase				:							str									=   red / 255
  lowercase				:							Union[str, Any]									=   green / 255
  lowercase				:							Union[str, Any]									=   blue / 255
  lowercase				:							str									=   max(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							int									=   value - min(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							Optional[int]									=   0 if value == 0 else chroma / value

  if chroma == 0:
    lowercase				:							Dict									=   0.0
  elif value == float_red:
    lowercase				:							Tuple									=   60 * (0 + (float_green - float_blue) / chroma)
  elif value == float_green:
    lowercase				:							Optional[int]									=   60 * (2 + (float_blue - float_red) / chroma)
  else:
    lowercase				:							Dict									=   60 * (4 + (float_red - float_green) / chroma)

  lowercase				:							List[str]									=   (hue + 360) % 360

  return [hue, saturation, value]







def 				__UpperCamelCase (					__UpperCAmelCase  :					list[float] ,   __UpperCAmelCase  :					list[float]			)							->	bool:
  lowercase				:							int									=   abs(hsv_a[0] - hsv_a[0]			) < 0.2
  lowercase				:							Optional[int]									=   abs(hsv_a[1] - hsv_a[1]			) < 0.002
  lowercase				:							Dict									=   abs(hsv_a[2] - hsv_a[2]			) < 0.002

  return check_hue and check_saturation and check_value