





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


logging.set_verbosity_info()
_lowerCamelCase  					=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase  					=     []
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_     (  lowercase_       :							Any			,				lowercase_       :							Optional[int]			,				lowercase_       :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      str 							=	state_dict.pop(lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Any 							=	val




def 		lowerCAmelCase_     (  lowercase_       :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	key.replace('''backbone.0.body'''			,				'''backbone.conv_encoder.model'''		)
   __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	value
  else:
   __SCREAMING_SNAKE_CASE			:      int 							=	value

 return new_state_dict




def 		lowerCAmelCase_     (  lowercase_       :							List[str]			,				lowercase_       :							List[str]=False		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	''''''
 if is_panoptic:
  __SCREAMING_SNAKE_CASE			:      Any 							=	'''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)
  __SCREAMING_SNAKE_CASE			:      Dict 							=	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''		)
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	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
  __SCREAMING_SNAKE_CASE			:      str 							=	in_proj_weight[:256, :]
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	in_proj_bias[:256]
  __SCREAMING_SNAKE_CASE			:      int 							=	in_proj_weight[256:512, :]
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	in_proj_bias[256:512]
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	in_proj_weight[-256:, :]
  __SCREAMING_SNAKE_CASE			:      int 							=	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
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
  __SCREAMING_SNAKE_CASE			:      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
  __SCREAMING_SNAKE_CASE			:      int 							=	in_proj_weight[:256, :]
  __SCREAMING_SNAKE_CASE			:      str 							=	in_proj_bias[:256]
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	in_proj_weight[256:512, :]
  __SCREAMING_SNAKE_CASE			:      Any 							=	in_proj_bias[256:512]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	in_proj_weight[-256:, :]
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	state_dict.pop(
      F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''		)
  __SCREAMING_SNAKE_CASE			:      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
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	in_proj_weight_cross_attn[:256, :]
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	in_proj_bias_cross_attn[:256]
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	in_proj_weight_cross_attn[256:512, :]
  __SCREAMING_SNAKE_CASE			:      Any 							=	in_proj_bias_cross_attn[256:512]
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	in_proj_weight_cross_attn[-256:, :]
  __SCREAMING_SNAKE_CASE			:      Any 							=	in_proj_bias_cross_attn[-256:]




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	Image.open(requests.get(lowercase_			,				stream=lowercase_		).raw		)

 return im




@torch.no_grad()
def 		lowerCAmelCase_     (  lowercase_       :							List[Any]			,				lowercase_       :							str		):

 '''simple docstring'''






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

 # load image processor
 __SCREAMING_SNAKE_CASE			:      str 							=	'''coco_panoptic''' if is_panoptic else '''coco_detection'''
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	DetrImageProcessor(format=lowercase_		)

 # prepare image
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	prepare_img()
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	image_processor(images=lowercase_			,				return_tensors='''pt'''		)
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	encoding['''pixel_values''']

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

 # load original model from torch hub
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	torch.hub.load('''facebookresearch/detr'''			,				lowercase_			,				pretrained=lowercase_		).eval()
 __SCREAMING_SNAKE_CASE			:      int 							=	detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   __SCREAMING_SNAKE_CASE			:      Any 							=	'''detr.''' + src
  rename_key(lowercase_			,				lowercase_			,				lowercase_		)
 __SCREAMING_SNAKE_CASE			:      str 							=	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
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	'''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'''		)
   ):
    __SCREAMING_SNAKE_CASE			:      List[str] 							=	state_dict.pop(lowercase_		)
    __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(lowercase_		)
    __SCREAMING_SNAKE_CASE			:      List[str] 							=	val
   elif key.startswith('''bbox_attention'''		) or key.startswith('''mask_head'''		):
    continue
   else:
    __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(lowercase_		)
    __SCREAMING_SNAKE_CASE			:      Dict 							=	val
  else:
   if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
    __SCREAMING_SNAKE_CASE			:      List[str] 							=	state_dict.pop(lowercase_		)
    __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	val
    # finally, create HuggingFace model and load state dict
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	DetrForSegmentation(lowercase_		) if is_panoptic else DetrForObjectDetection(lowercase_		)
 model.load_state_dict(lowercase_		)
 model.eval()
 # verify our conversion
 __SCREAMING_SNAKE_CASE			:      int 							=	detr(lowercase_		)
 __SCREAMING_SNAKE_CASE			:      List[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__":
    _lowerCamelCase  					=     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  					=     parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""





from ..utils import DummyObject, requires_backends





class       snake_case		(       metaclass=__UpperCAmelCase       ):
      lowerCamelCase__      =			['''torch''', '''transformers''', '''invisible_watermark''']

      def __init__(      self       :Optional[int]						,							*_lowerCamelCase       :int						,							**_lowerCamelCase       :Any						):
       requires_backends(self						,							['''torch''', '''transformers''', '''invisible_watermark''']						)

      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :Dict						,							*_lowerCamelCase       :Optional[Any]						,							**_lowerCamelCase       :int						):
       requires_backends(cls						,							['''torch''', '''transformers''', '''invisible_watermark''']						)



      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :Optional[int]						,							*_lowerCamelCase       :Optional[Any]						,							**_lowerCamelCase       :Union[str, Any]						):
       requires_backends(cls						,							['''torch''', '''transformers''', '''invisible_watermark''']						)







class       snake_case		(       metaclass=__UpperCAmelCase       ):
      lowerCamelCase__      =			['''torch''', '''transformers''', '''invisible_watermark''']

      def __init__(      self       :Dict						,							*_lowerCamelCase       :Dict						,							**_lowerCamelCase       :Optional[Any]						):
       requires_backends(self						,							['''torch''', '''transformers''', '''invisible_watermark''']						)

      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :Optional[int]						,							*_lowerCamelCase       :int						,							**_lowerCamelCase       :Optional[int]						):
       requires_backends(cls						,							['''torch''', '''transformers''', '''invisible_watermark''']						)



      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :List[Any]						,							*_lowerCamelCase       :Dict						,							**_lowerCamelCase       :List[str]						):
       requires_backends(cls						,							['''torch''', '''transformers''', '''invisible_watermark''']						)

