





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()
lowercase    : str    	=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase    : Dict    	=  []
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			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						state_dict.pop(lowerCAmelCase					)
       _UpperCamelCase              =						val


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

       return new_state_dict


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase=False					):
       _UpperCamelCase              =						''''''
       if is_panoptic:
              _UpperCamelCase              =						'''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              =						state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
              _UpperCamelCase              =						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              =						in_proj_weight[:256, :]
              _UpperCamelCase              =						in_proj_bias[:256]
              _UpperCamelCase              =						in_proj_weight[256:512, :]
              _UpperCamelCase              =						in_proj_bias[256:512]
              _UpperCamelCase              =						in_proj_weight[-256:, :]
              _UpperCamelCase              =						in_proj_bias[-256:]


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _UpperCamelCase              =						Image.open(requests.get(lowerCAmelCase    ,       stream=lowerCAmelCase					).raw					)

       return im


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

       # load image processor
       _UpperCamelCase              =						'''coco_panoptic''' if is_panoptic else '''coco_detection'''
       _UpperCamelCase              =						ConditionalDetrImageProcessor(format=lowerCAmelCase					)

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

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

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













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						0
       for ch in input_str:
              _UpperCamelCase              =						ord(lowerCAmelCase					)
              _UpperCamelCase              =						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()






