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()
a_    					=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_    					=       []
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    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						List[Any] =					state_dict.pop(lowerCamelCase	)
 UpperCamelCase_					:						int =					val





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

 return new_state_dict





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     Union[str, Any]=False	):
 UpperCamelCase_					:						List[str] =					''
 if is_panoptic:
  UpperCamelCase_					:						Optional[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_					:						str =					state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"	)
  UpperCamelCase_					:						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_					:						List[str] =					in_proj_weight[:256, :]
  UpperCamelCase_					:						Dict =					in_proj_bias[:256]
  UpperCamelCase_					:						Dict =					in_proj_weight[256:512, :]
  UpperCamelCase_					:						str =					in_proj_bias[256:512]
  UpperCamelCase_					:						List[Any] =					in_proj_weight[-256:, :]
  UpperCamelCase_					:						Union[str, Any] =					in_proj_bias[-256:]





def    __lowercase     (						):
 UpperCamelCase_					:						Any =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCamelCase_					:						Dict =					Image.open(requests.get(lowerCamelCase  ,  stream=lowerCamelCase	).raw	)

 return im





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

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

 # prepare image
 UpperCamelCase_					:						Any =					prepare_img()
 UpperCamelCase_					:						Tuple =					image_processor(images=lowerCamelCase  ,  return_tensors='pt'	)
 UpperCamelCase_					:						Optional[Any] =					encoding['pixel_values']

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

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




def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



