
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      =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase      =[]
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_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				state_dict.pop(UpperCamelCase__		)
      UpperCamelCase__    :   List[Any]					=				val


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

      return new_state_dict


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=False		):
      UpperCamelCase__    :   Union[str, Any]					=				''''''
      if is_panoptic:
            UpperCamelCase__    :   List[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__    :   Optional[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[:2_5_6, :]
            UpperCamelCase__    :   Optional[int]					=				in_proj_bias[:2_5_6]
            UpperCamelCase__    :   Optional[int]					=				in_proj_weight[2_5_6:5_1_2, :]
            UpperCamelCase__    :   List[Any]					=				in_proj_bias[2_5_6:5_1_2]
            UpperCamelCase__    :   int					=				in_proj_weight[-2_5_6:, :]
            UpperCamelCase__    :   Optional[int]					=				in_proj_bias[-2_5_6:]


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Optional[int]					=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__    :   List[Any]					=				Image.open(requests.get(UpperCamelCase__ ,		stream=UpperCamelCase__		).raw		)

      return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Union[str, Any]					=				ConditionalDetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            UpperCamelCase__    :   List[Any]					=				'''resnet101'''
      if "dc5" in model_name:
            UpperCamelCase__    :   Dict					=				True
      UpperCamelCase__    :   Tuple					=				'''panoptic''' in model_name
      if is_panoptic:
            UpperCamelCase__    :   int					=				2_5_0
      else:
            UpperCamelCase__    :   List[Any]					=				9_1
            UpperCamelCase__    :   Optional[Any]					=				'''huggingface/label-files'''
            UpperCamelCase__    :   List[str]					=				'''coco-detection-id2label.json'''
            UpperCamelCase__    :   Any					=				json.load(open(hf_hub_download(UpperCamelCase__ ,		UpperCamelCase__ ,		repo_type='''dataset'''		) ,		'''r'''		)		)
            UpperCamelCase__    :   List[str]					=				{int(UpperCamelCase__		): v for k, v in idalabel.items()}
            UpperCamelCase__    :   Optional[int]					=				idalabel
            UpperCamelCase__    :   List[Any]					=				{v: k for k, v in idalabel.items()}

      # load image processor
      UpperCamelCase__    :   int					=				'''coco_panoptic''' if is_panoptic else '''coco_detection'''
      UpperCamelCase__    :   Tuple					=				ConditionalDetrImageProcessor(format=UpperCamelCase__		)

      # prepare image
      UpperCamelCase__    :   List[Any]					=				prepare_img()
      UpperCamelCase__    :   Union[str, Any]					=				image_processor(images=UpperCamelCase__ ,		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''' ,		UpperCamelCase__ ,		pretrained=UpperCamelCase__		).eval()
      UpperCamelCase__    :   List[str]					=				conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  UpperCamelCase__    :   Tuple					=				'''conditional_detr.''' + src
            rename_key(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
      UpperCamelCase__    :   Union[str, Any]					=				rename_backbone_keys(UpperCamelCase__		)
      # query, key and value matrices need special treatment
      read_in_q_k_v(UpperCamelCase__ ,		is_panoptic=UpperCamelCase__		)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      UpperCamelCase__    :   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'''		)
                  ):
                        UpperCamelCase__    :   Union[str, Any]					=				state_dict.pop(UpperCamelCase__		)
                        UpperCamelCase__    :   Dict					=				val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        UpperCamelCase__    :   List[Any]					=				state_dict.pop(UpperCamelCase__		)
                        UpperCamelCase__    :   List[str]					=				val
                  elif key.startswith('''bbox_attention'''		) or key.startswith('''mask_head'''		):
                        continue
                  else:
                        UpperCamelCase__    :   Optional[int]					=				state_dict.pop(UpperCamelCase__		)
                        UpperCamelCase__    :   Any					=				val
            else:
                  if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
                        UpperCamelCase__    :   str					=				state_dict.pop(UpperCamelCase__		)
                        UpperCamelCase__    :   List[Any]					=				val
    # finally, create HuggingFace model and load state dict
      UpperCamelCase__    :   int					=				ConditionalDetrForSegmentation(UpperCamelCase__		) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase__		)
      model.load_state_dict(UpperCamelCase__		)
      model.eval()
      model.push_to_hub(repo_id=UpperCamelCase__ ,		organization='''DepuMeng''' ,		commit_message='''Add model'''		)
      # verify our conversion
      UpperCamelCase__    :   Any					=				conditional_detr(UpperCamelCase__		)
      UpperCamelCase__    :   Optional[Any]					=				model(UpperCamelCase__		)
      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(UpperCamelCase__		).mkdir(exist_ok=UpperCamelCase__		)
      model.save_pretrained(UpperCamelCase__		)
      image_processor.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
    lowerCamelCase      =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      =parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




lowerCamelCase      ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowerCamelCase      =["a", "b", "c", "d", "e"]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				start
      # add current to visited
      visited.append(UpperCamelCase__		)
      UpperCamelCase__    :   int					=				edges[current]
      for neighbor in neighbors:
            # if neighbor not in visited, visit
            if neighbor not in visited:
                  UpperCamelCase__    :   int					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # if all neighbors visited add current to sort
      sort.append(UpperCamelCase__		)
      # if all vertices haven't been visited select a new one to visit
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            for vertice in vertices:
                  if vertice not in visited:
                        UpperCamelCase__    :   Optional[int]					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # return sort
      return sort


if __name__ == "__main__":
    lowerCamelCase      =topological_sort("a", [], [])
    print(sort)


