
'''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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							: str =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_							: Any =							[]
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 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Tuple				):



      """simple docstring"""




      _lowerCamelCase					: List[str]		    = state_dict.pop(_lowerCAmelCase				)
      _lowerCamelCase					: List[Any]		    = val







def 		A_							(						_lowerCAmelCase :     Any				):



      """simple docstring"""




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

      return new_state_dict







def 		A_							(						_lowerCAmelCase :     int					,						_lowerCAmelCase :     List[str]=False				):



      """simple docstring"""




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







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = "http://images.cocodataset.org/val2017/000000039769.jpg"
      _lowerCamelCase					: Dict		    = Image.open(requests.get(_lowerCAmelCase					,						stream=_lowerCAmelCase				).raw				)

      return im







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     str				):



      """simple docstring"""




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

      # load image processor
      _lowerCamelCase					: Optional[Any]		    = "coco_panoptic" if is_panoptic else "coco_detection"
      _lowerCamelCase					: Dict		    = ConditionalDetrImageProcessor(format=_lowerCAmelCase				)

      # prepare image
      _lowerCamelCase					: Tuple		    = prepare_img()
      _lowerCamelCase					: Dict		    = image_processor(images=_lowerCAmelCase					,						return_tensors="pt"				)
      _lowerCamelCase					: Optional[int]		    = encoding["pixel_values"]

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

      # load original model from torch hub
      _lowerCamelCase					: Dict		    = torch.hub.load("DeppMeng/ConditionalDETR"					,						_lowerCAmelCase					,						pretrained=_lowerCAmelCase				).eval()
      _lowerCamelCase					: str		    = conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  _lowerCamelCase					: List[str]		    = "conditional_detr." + src
            rename_key(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase				)
      _lowerCamelCase					: 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
      _lowerCamelCase					: str		    = "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"				)
                  ):
                        _lowerCamelCase					: Dict		    = state_dict.pop(_lowerCAmelCase				)
                        _lowerCamelCase					: List[Any]		    = val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        _lowerCamelCase					: Optional[int]		    = state_dict.pop(_lowerCAmelCase				)
                        _lowerCamelCase					: Optional[Any]		    = val
                  elif key.startswith("bbox_attention"				) or key.startswith("mask_head"				):
                        continue
                  else:
                        _lowerCamelCase					: str		    = state_dict.pop(_lowerCAmelCase				)
                        _lowerCamelCase					: List[Any]		    = val
            else:
                  if not key.startswith("class_labels_classifier"				) and not key.startswith("bbox_predictor"				):
                        _lowerCamelCase					: Optional[Any]		    = state_dict.pop(_lowerCAmelCase				)
                        _lowerCamelCase					: Tuple		    = val
    # finally, create HuggingFace model and load state dict
      _lowerCamelCase					: str		    = 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
      _lowerCamelCase					: int		    = conditional_detr(_lowerCAmelCase				)
      _lowerCamelCase					: List[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__":
 UpperCAmelCase_							: Optional[Any] =							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_							: List[str] =							parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""simple docstring"""

from PIL import Image


def lowerCAmelCase_   (					UpperCamelCase__						:			Image					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						(259 * (level + 255)) / (255 * (259 - level))

 def contrast(UpperCamelCase__						:			int			) -> int:
  return int(128 + factor * (c - 128)			)

 return img.point(UpperCamelCase__			)


if __name__ == "__main__":
       # Load image
       with Image.open("image_data/lena.jpg") as img:
              # Change contrast to 170
              UpperCAmelCase__					=change_contrast(img, 170)
              cont_img.save("image_data/lena_high_contrast.png", format="png")
