



"""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()
_UpperCamelCase:					Optional[Any]     				=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCamelCase:					List[str]     				=	[]
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__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					)  ->      List[Any]:






      '''simple docstring'''


      lowercase	:   Optional[int]											= state_dict.pop(_UpperCAmelCase					)
      lowercase	:   List[str]											= val


def 	lowercase__  (      _UpperCAmelCase					)  ->      List[str]:






      '''simple docstring'''


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

      return new_state_dict


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase=False					)  ->      int:






      '''simple docstring'''


      lowercase	:   Optional[int]											= ''
      if is_panoptic:
            lowercase	:   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)
            lowercase	:   Tuple											= state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
            lowercase	:   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
            lowercase	:   int											= in_proj_weight[:2_56, :]
            lowercase	:   List[Any]											= in_proj_bias[:2_56]
            lowercase	:   str											= in_proj_weight[2_56:5_12, :]
            lowercase	:   Any											= in_proj_bias[2_56:5_12]
            lowercase	:   Dict											= in_proj_weight[-2_56:, :]
            lowercase	:   Tuple											= 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
            lowercase	:   Dict											= state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
            lowercase	:   List[str]											= 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
            lowercase	:   Dict											= in_proj_weight[:2_56, :]
            lowercase	:   Any											= in_proj_bias[:2_56]
            lowercase	:   str											= in_proj_weight[2_56:5_12, :]
            lowercase	:   Union[str, Any]											= in_proj_bias[2_56:5_12]
            lowercase	:   Optional[Any]											= in_proj_weight[-2_56:, :]
            lowercase	:   List[Any]											= in_proj_bias[-2_56:]
            # read in weights + bias of input projection layer of cross-attention
            lowercase	:   List[str]											= state_dict.pop(
                f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
            lowercase	:   Union[str, Any]											= 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
            lowercase	:   Optional[int]											= in_proj_weight_cross_attn[:2_56, :]
            lowercase	:   Dict											= in_proj_bias_cross_attn[:2_56]
            lowercase	:   Tuple											= in_proj_weight_cross_attn[2_56:5_12, :]
            lowercase	:   Union[str, Any]											= in_proj_bias_cross_attn[2_56:5_12]
            lowercase	:   Tuple											= in_proj_weight_cross_attn[-2_56:, :]
            lowercase	:   List[str]											= in_proj_bias_cross_attn[-2_56:]


def 	lowercase__  (      )  ->      Tuple:






      '''simple docstring'''


      lowercase	:   List[Any]											= 'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowercase	:   str											= Image.open(requests.get(_UpperCAmelCase						,   stream=_UpperCAmelCase					).raw					)

      return im


@torch.no_grad()
def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase					)  ->      Tuple:






      '''simple docstring'''


      lowercase	:   str											= DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            lowercase	:   Union[str, Any]											= 'resnet101'
      if "dc5" in model_name:
            lowercase	:   Union[str, Any]											= True
      lowercase	:   Any											= 'panoptic' in model_name
      if is_panoptic:
            lowercase	:   Union[str, Any]											= 2_50
      else:
            lowercase	:   Tuple											= 91
            lowercase	:   Optional[Any]											= 'huggingface/label-files'
            lowercase	:   Optional[Any]											= 'coco-detection-id2label.json'
            lowercase	:   Optional[int]											= json.load(open(hf_hub_download(_UpperCAmelCase						,   _UpperCAmelCase						,   repo_type='dataset'					)						,   'r'					)					)
            lowercase	:   str											= {int(_UpperCAmelCase					): v for k, v in idalabel.items()}
            lowercase	:   str											= idalabel
            lowercase	:   Dict											= {v: k for k, v in idalabel.items()}

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

      # prepare image
      lowercase	:   List[Any]											= prepare_img()
      lowercase	:   Optional[int]											= image_processor(images=_UpperCAmelCase						,   return_tensors='pt'					)
      lowercase	:   Tuple											= encoding['pixel_values']

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

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





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

_UpperCamelCase:					List[str]     				=	{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'regnet'
   _lowerCamelCase       				=					['x', 'y']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, Any]=3,  lowerCAmelCase   :				Tuple=32,  lowerCAmelCase   :				Optional[int]=[128, 192, 512, 1088],  lowerCAmelCase   :				Optional[int]=[2, 6, 12, 2],  lowerCAmelCase   :				str=64,  lowerCAmelCase   :				List[str]="y",  lowerCAmelCase   :				Optional[int]="relu",  **lowerCAmelCase   :				List[Any],  )     ->							Union[str, Any]:
         super().__init__(**lowerCAmelCase      )
         if layer_type not in self.layer_types:
               raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
         lowercase	:   str											= num_channels
         lowercase	:   str											= embedding_size
         lowercase	:   int											= hidden_sizes
         lowercase	:   Tuple											= depths
         lowercase	:   str											= groups_width
         lowercase	:   List[str]											= layer_type
         lowercase	:   Optional[int]											= hidden_act
         # always downsample in the first stage
         lowercase	:   Optional[Any]											= True
