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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
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}.cross_attn.out_proj.weight""",
          f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
      )
  )
  rename_keys.append(
      (
          f"""transformer.decoder.layers.{i}.cross_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"""))

  # q, k, v projections in self/cross-attention in decoder for conditional DETR
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", f"""decoder.layers.{i}.sa_qcontent_proj.weight""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", f"""decoder.layers.{i}.sa_kcontent_proj.weight""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", f"""decoder.layers.{i}.sa_qpos_proj.weight""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", f"""decoder.layers.{i}.sa_kpos_proj.weight""")
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.weight""", f"""decoder.layers.{i}.sa_v_proj.weight"""))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", f"""decoder.layers.{i}.ca_qcontent_proj.weight""")
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", f"""decoder.layers.{i}.ca_kcontent_proj.weight""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", f"""decoder.layers.{i}.ca_kpos_proj.weight""")
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.weight""", f"""decoder.layers.{i}.ca_v_proj.weight"""))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", f"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
  )

  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", f"""decoder.layers.{i}.sa_qcontent_proj.bias""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", f"""decoder.layers.{i}.sa_kcontent_proj.bias""")
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", f"""decoder.layers.{i}.sa_qpos_proj.bias"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", f"""decoder.layers.{i}.sa_kpos_proj.bias"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.bias""", f"""decoder.layers.{i}.sa_v_proj.bias"""))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", f"""decoder.layers.{i}.ca_qcontent_proj.bias""")
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", f"""decoder.layers.{i}.ca_kcontent_proj.bias""")
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", f"""decoder.layers.{i}.ca_kpos_proj.bias"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.bias""", f"""decoder.layers.{i}.ca_v_proj.bias"""))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", f"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
  )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							int  ,					lowercase:							Optional[Any]		)    ->				str:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= state_dict.pop(lowercase		)
 _UpperCamelCase:     Dict							= val




def       lowerCAmelCase_     (		lowercase:							Tuple		)    ->				List[Any]:







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

 return new_state_dict




def       lowerCAmelCase_     (		lowercase:							Union[str, Any]  ,					lowercase:							List[Any]=False		)    ->				str:







 '''simple docstring'''
 _UpperCamelCase:     Optional[Any]							= ''''''
 if is_panoptic:
  _UpperCamelCase:     Any							= '''conditional_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:     List[Any]							= state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
  _UpperCamelCase:     List[Any]							= 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:     Dict							= in_proj_weight[:256, :]
  _UpperCamelCase:     Union[str, Any]							= in_proj_bias[:256]
  _UpperCamelCase:     int							= in_proj_weight[256:512, :]
  _UpperCamelCase:     Optional[Any]							= in_proj_bias[256:512]
  _UpperCamelCase:     Any							= in_proj_weight[-256:, :]
  _UpperCamelCase:     List[str]							= in_proj_bias[-256:]




def       lowerCAmelCase_     (		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[Any]							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _UpperCamelCase:     Any							= Image.open(requests.get(lowercase  ,					stream=lowercase		).raw		)

 return im




@torch.no_grad()
def       lowerCAmelCase_     (		lowercase:							Dict  ,					lowercase:							List[str]		)    ->				List[str]:







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

 # load image processor
 _UpperCamelCase:     Union[str, Any]							= '''coco_panoptic''' if is_panoptic else '''coco_detection'''
 _UpperCamelCase:     Tuple							= ConditionalDetrImageProcessor(format=lowercase		)

 # prepare image
 _UpperCamelCase:     Dict							= prepare_img()
 _UpperCamelCase:     Dict							= image_processor(images=lowercase  ,					return_tensors='''pt'''		)
 _UpperCamelCase:     Any							= encoding['''pixel_values''']

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

 # load original model from torch hub
 _UpperCamelCase:     Tuple							= torch.hub.load('''DeppMeng/ConditionalDETR'''  ,					lowercase  ,					pretrained=lowercase		).eval()
 _UpperCamelCase:     Optional[Any]							= conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   _UpperCamelCase:     Optional[Any]							= '''conditional_detr.''' + src
  rename_key(lowercase  ,					lowercase  ,					lowercase		)
 _UpperCamelCase:     Any							= 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
 _UpperCamelCase:     Optional[Any]							= '''conditional_detr.model.''' if is_panoptic else '''model.'''
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith('''conditional_detr'''		)
       and not key.startswith('''class_labels_classifier'''		)
       and not key.startswith('''bbox_predictor'''		)
   ):
    _UpperCamelCase:     Optional[int]							= state_dict.pop(lowercase		)
    _UpperCamelCase:     List[Any]							= val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    _UpperCamelCase:     int							= state_dict.pop(lowercase		)
    _UpperCamelCase:     List[str]							= val
   elif key.startswith('''bbox_attention'''		) or key.startswith('''mask_head'''		):
    continue
   else:
    _UpperCamelCase:     List[Any]							= state_dict.pop(lowercase		)
    _UpperCamelCase:     Dict							= val
  else:
   if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
    _UpperCamelCase:     str							= state_dict.pop(lowercase		)
    _UpperCamelCase:     Any							= val
    # finally, create HuggingFace model and load state dict
 _UpperCamelCase:     List[str]							= ConditionalDetrForSegmentation(lowercase		) if is_panoptic else ConditionalDetrForObjectDetection(lowercase		)
 model.load_state_dict(lowercase		)
 model.eval()
 model.push_to_hub(repo_id=lowercase  ,					organization='''DepuMeng'''  ,					commit_message='''Add model'''		)
 # verify our conversion
 _UpperCamelCase:     Any							= conditional_detr(lowercase		)
 _UpperCamelCase:     Dict							= 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__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()

  parser.add_argument(
      '''--model_name''',
      default='''conditional_detr_resnet50''',
      type=str,
      help='''Name of the CONDITIONAL_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_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
def       lowerCAmelCase_     (		lowercase:							float		)    ->				float:







 '''simple docstring'''

 if edge <= 0 or not isinstance(lowercase  ,					lowercase		):
  raise ValueError('''Length must be a positive.'''		)
 return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def       lowerCAmelCase_     (		lowercase:							float		)    ->				float:







 '''simple docstring'''

 if edge <= 0 or not isinstance(lowercase  ,					lowercase		):
  raise ValueError('''Length must be a positive.'''		)
 return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
  import doctest

  doctest.testmod()