


'''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()
lowerCamelCase							:   List[Any]  						=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase							:   Union[str, 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 _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Optional[int]:






       """simple docstring"""







       lowercase__      			= state_dict.pop(A       )
       lowercase__      			= val

def _SCREAMING_SNAKE_CASE      (A       )       ->       str:






       """simple docstring"""







       lowercase__      			= OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase__      			= key.replace('''backbone.0.body'''  , '''backbone.conv_encoder.model'''       )
                     lowercase__      			= value
              else:
                     lowercase__      			= value

       return new_state_dict

def _SCREAMING_SNAKE_CASE      (A  , A=False       )       ->       Any:






       """simple docstring"""







       lowercase__      			= ''''''
       if is_panoptic:
              lowercase__      			= '''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)
              lowercase__      			= state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"       )
              lowercase__      			= 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__      			= in_proj_weight[:256, :]
              lowercase__      			= in_proj_bias[:256]
              lowercase__      			= in_proj_weight[256:512, :]
              lowercase__      			= in_proj_bias[256:512]
              lowercase__      			= in_proj_weight[-256:, :]
              lowercase__      			= in_proj_bias[-256:]

def _SCREAMING_SNAKE_CASE      ()       ->       Any:






       """simple docstring"""







       lowercase__      			= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowercase__      			= Image.open(requests.get(A  , stream=A       ).raw       )

       return im

@torch.no_grad()
def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Tuple:






       """simple docstring"""







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

       # load image processor
       lowercase__      			= '''coco_panoptic''' if is_panoptic else '''coco_detection'''
       lowercase__      			= ConditionalDetrImageProcessor(format=A       )

       # prepare image
       lowercase__      			= prepare_img()
       lowercase__      			= image_processor(images=A  , return_tensors='''pt'''       )
       lowercase__      			= encoding['''pixel_values''']

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

       # load original model from torch hub
       lowercase__      			= torch.hub.load('''DeppMeng/ConditionalDETR'''  , A  , pretrained=A       ).eval()
       lowercase__      			= conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase__      			= '''conditional_detr.''' + src
              rename_key(A  , A  , A       )
       lowercase__      			= rename_backbone_keys(A       )
       # query, key and value matrices need special treatment
       read_in_q_k_v(A  , is_panoptic=A       )
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       lowercase__      			= '''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'''       )
                     ):
                            lowercase__      			= state_dict.pop(A       )
                            lowercase__      			= val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase__      			= state_dict.pop(A       )
                            lowercase__      			= val
                     elif key.startswith('''bbox_attention'''       ) or key.startswith('''mask_head'''       ):
                            continue
                     else:
                            lowercase__      			= state_dict.pop(A       )
                            lowercase__      			= val
              else:
                     if not key.startswith('''class_labels_classifier'''       ) and not key.startswith('''bbox_predictor'''       ):
                            lowercase__      			= state_dict.pop(A       )
                            lowercase__      			= val
    # finally, create HuggingFace model and load state dict
       lowercase__      			= ConditionalDetrForSegmentation(A       ) if is_panoptic else ConditionalDetrForObjectDetection(A       )
       model.load_state_dict(A       )
       model.eval()
       model.push_to_hub(repo_id=A  , organization='''DepuMeng'''  , commit_message='''Add model'''       )
       # verify our conversion
       lowercase__      			= conditional_detr(A       )
       lowercase__      			= model(A       )
       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(A       ).mkdir(exist_ok=A       )
       model.save_pretrained(A       )
       image_processor.save_pretrained(A       )


if __name__ == "__main__":
 lowerCamelCase							:   List[str]  						=	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.'
 )
 lowerCamelCase							:   List[str]  						=	parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








'''simple docstring'''

from __future__ import annotations

def _SCREAMING_SNAKE_CASE      (A       )       ->       bool:






       """simple docstring"""







       return len(set(A       )       ) == len(A       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




