

"""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__				:  str													=  logging.get_logger(__name__)

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

 return new_state_dict
def       UpperCamelCase					(			_lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						Dict=False )				->			List[str]:
 _UpperCAmelCase    :							int									=					""""""
 if is_panoptic:
  _UpperCAmelCase    :							Tuple									=					"""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    :							Union[str, Any]									=					state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
  _UpperCAmelCase    :							List[str]									=					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    :							Union[str, Any]									=					in_proj_weight[:256, :]
  _UpperCAmelCase    :							List[Any]									=					in_proj_bias[:256]
  _UpperCAmelCase    :							Any									=					in_proj_weight[256:512, :]
  _UpperCAmelCase    :							List[Any]									=					in_proj_bias[256:512]
  _UpperCAmelCase    :							List[Any]									=					in_proj_weight[-256:, :]
  _UpperCAmelCase    :							Tuple									=					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    :							List[Any]									=					state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
  _UpperCAmelCase    :							Optional[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
  _UpperCAmelCase    :							Optional[int]									=					in_proj_weight[:256, :]
  _UpperCAmelCase    :							Any									=					in_proj_bias[:256]
  _UpperCAmelCase    :							Any									=					in_proj_weight[256:512, :]
  _UpperCAmelCase    :							Tuple									=					in_proj_bias[256:512]
  _UpperCAmelCase    :							Any									=					in_proj_weight[-256:, :]
  _UpperCAmelCase    :							Dict									=					in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  _UpperCAmelCase    :							str									=					state_dict.pop(
      f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
  _UpperCAmelCase    :							Optional[int]									=					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    :							str									=					in_proj_weight_cross_attn[:256, :]
  _UpperCAmelCase    :							Optional[int]									=					in_proj_bias_cross_attn[:256]
  _UpperCAmelCase    :							List[str]									=					in_proj_weight_cross_attn[256:512, :]
  _UpperCAmelCase    :							str									=					in_proj_bias_cross_attn[256:512]
  _UpperCAmelCase    :							Dict									=					in_proj_weight_cross_attn[-256:, :]
  _UpperCAmelCase    :							List[Any]									=					in_proj_bias_cross_attn[-256:]
def       UpperCamelCase					(			)				->			Optional[Any]:
 _UpperCAmelCase    :							str									=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _UpperCAmelCase    :							Optional[int]									=					Image.open(requests.get(_lowerCAmelCase,  stream=_lowerCAmelCase ).raw )

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

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

 # prepare image
 _UpperCAmelCase    :							List[Any]									=					prepare_img()
 _UpperCAmelCase    :							List[str]									=					image_processor(images=_lowerCAmelCase,  return_tensors="""pt""" )
 _UpperCAmelCase    :							str									=					encoding["""pixel_values"""]

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

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








"""simple docstring"""






import safs






class     _UpperCAmelCase			(		safs.SaFileSystem):
			pass





