
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case				:	List[Any]							   =						logging.get_logger(__name__)






def 						lowerCAmelCase_		(      )       ->	List[str]:







 '''simple docstring'''


 __magic_name__	:	Optional[Any]			     =  DetaConfig(
     num_queries=900   ,     encoder_ffn_dim=2048   ,     decoder_ffn_dim=2048   ,     num_feature_levels=5   ,     assign_first_stage=snake_case__   ,     with_box_refine=snake_case__   ,     two_stage=snake_case__   ,     )

 # set labels
 __magic_name__	:	int			     =  91
 __magic_name__	:	Tuple			     =  'huggingface/label-files'
 __magic_name__	:	List[str]			     =  'coco-detection-id2label.json'
 __magic_name__	:	int			     =  json.load(open(cached_download(hf_hub_url(snake_case__   ,     snake_case__   ,     repo_type="dataset"     )     )   ,     "r"     )     )
 __magic_name__	:	List[Any]			     =  {int(snake_case__     ): v for k, v in idalabel.items()}
 __magic_name__	:	Tuple			     =  idalabel
 __magic_name__	:	str			     =  {v: k for k, v in idalabel.items()}

 return config






def 						lowerCAmelCase_		(      _snake_case   :						Dict     )       ->	Dict:







 '''simple docstring'''


 __magic_name__	:	str			     =  []

 # stem
 # fmt: off
 rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")     )
 rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")     )
 rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")     )
 rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")     )
 rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")     )
 # stages
 for stage_idx in range(len(config.backbone_config.depths     )     ):
  for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
   # shortcut
   if layer_idx == 0:
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
        )     )
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
        )     )
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
        )     )
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
        )     )
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
        )     )
   # 3 convs
   for i in range(3     ):
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
        )     )
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
        )     )
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
        )     )
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
        )     )
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
        )     )
    # transformer encoder
 for i in range(config.encoder_layers     ):
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')     )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')     )

 # transformer decoder
 for i in range(config.decoder_layers     ):
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')     )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')     )

 # fmt: on

 return rename_keys






def 						lowerCAmelCase_		(      _snake_case   :						Any   ,     _snake_case   :						List[Any]   ,     _snake_case   :						Optional[int]     )       ->	List[Any]:







 '''simple docstring'''


 __magic_name__	:	int			     =  dct.pop(snake_case__     )
 __magic_name__	:	List[Any]			     =  val






def 						lowerCAmelCase_		(      _snake_case   :						Dict   ,     _snake_case   :						Tuple     )       ->	Any:







 '''simple docstring'''


 __magic_name__	:	Union[str, Any]			     =  config.d_model
 for i in range(config.decoder_layers     ):
  # read in weights + bias of input projection layer of self-attention
  __magic_name__	:	List[str]			     =  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
  __magic_name__	:	Tuple			     =  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''     )
  # next, add query, keys and values (in that order) to the state dict
  __magic_name__	:	Optional[Any]			     =  in_proj_weight[:hidden_size, :]
  __magic_name__	:	int			     =  in_proj_bias[:hidden_size]
  __magic_name__	:	Optional[Any]			     =  in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  __magic_name__	:	str			     =  in_proj_bias[hidden_size : hidden_size * 2]
  __magic_name__	:	Optional[int]			     =  in_proj_weight[-hidden_size:, :]
  __magic_name__	:	Union[str, Any]			     =  in_proj_bias[-hidden_size:]






def 						lowerCAmelCase_		(      )       ->	List[Any]:







 '''simple docstring'''


 __magic_name__	:	Optional[int]			     =  'http://images.cocodataset.org/val2017/000000039769.jpg'
 __magic_name__	:	Union[str, Any]			     =  Image.open(requests.get(snake_case__   ,     stream=snake_case__     ).raw     )

 return im






@torch.no_grad()
def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						Union[str, Any]   ,     _snake_case   :						List[Any]     )       ->	Tuple:







 '''simple docstring'''


 __magic_name__	:	Union[str, Any]			     =  get_deta_config()

 # load original state dict
 if model_name == "deta-resnet-50":
  __magic_name__	:	Optional[int]			     =  'adet_checkpoint0011.pth'
 elif model_name == "deta-resnet-50-24-epochs":
  __magic_name__	:	Optional[Any]			     =  'adet_2x_checkpoint0023.pth'
 else:
  raise ValueError(F'''Model name {model_name} not supported'''     )
 __magic_name__	:	Optional[int]			     =  hf_hub_download(repo_id="nielsr/deta-checkpoints"   ,     filename=snake_case__     )
 __magic_name__	:	Any			     =  torch.load(snake_case__   ,     map_location="cpu"     )['model']

 # rename keys
 __magic_name__	:	Optional[int]			     =  create_rename_keys(snake_case__     )
 for src, dest in rename_keys:
  rename_key(snake_case__   ,     snake_case__   ,     snake_case__     )
 read_in_decoder_q_k_v(snake_case__   ,     snake_case__     )

 # fix some prefixes
 for key in state_dict.copy().keys():
  if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
   __magic_name__	:	List[str]			     =  state_dict.pop(snake_case__     )
   __magic_name__	:	Optional[Any]			     =  val
  if "input_proj" in key:
   __magic_name__	:	str			     =  state_dict.pop(snake_case__     )
   __magic_name__	:	Tuple			     =  val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   __magic_name__	:	List[Any]			     =  state_dict.pop(snake_case__     )
   __magic_name__	:	Dict			     =  val

    # finally, create HuggingFace model and load state dict
 __magic_name__	:	List[Any]			     =  DetaForObjectDetection(snake_case__     )
 model.load_state_dict(snake_case__     )
 model.eval()

 __magic_name__	:	int			     =  'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(snake_case__     )

 # load image processor
 __magic_name__	:	Optional[Any]			     =  DetaImageProcessor(format="coco_detection"     )

 # verify our conversion on image
 __magic_name__	:	Dict			     =  prepare_img()
 __magic_name__	:	List[Any]			     =  processor(images=snake_case__   ,     return_tensors="pt"     )
 __magic_name__	:	List[Any]			     =  encoding['pixel_values']
 __magic_name__	:	Dict			     =  model(pixel_values.to(snake_case__     )     )

 # verify logits
 if model_name == "deta-resnet-50":
  __magic_name__	:	Dict			     =  torch.tensor(
      [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]     )
  __magic_name__	:	Tuple			     =  torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]     )
 elif model_name == "deta-resnet-50-24-epochs":
  __magic_name__	:	Any			     =  torch.tensor(
      [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]     )
  __magic_name__	:	List[Any]			     =  torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]     )

 assert torch.allclose(outputs.logits[0, :3, :3]   ,     expected_logits.to(snake_case__     )   ,     atol=1E-4     )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,     expected_boxes.to(snake_case__     )   ,     atol=1E-4     )
 print("Everything ok!"     )

 if pytorch_dump_folder_path:
  # Save model and processor
  logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''     )
  Path(snake_case__     ).mkdir(exist_ok=snake_case__     )
  model.save_pretrained(snake_case__     )
  processor.save_pretrained(snake_case__     )

 # Push to hub
 if push_to_hub:
  print("Pushing model and processor to hub..."     )
  model.push_to_hub(F'''jozhang97/{model_name}'''     )
  processor.push_to_hub(F'''jozhang97/{model_name}'''     )


if __name__ == "__main__":
    snake_case				:	str							   =						argparse.ArgumentParser()

    parser.add_argument(
        "--model_name",
        type=str,
        default="deta-resnet-50",
        choices=["deta-resnet-50", "deta-resnet-50-24-epochs"],
        help="Name of the 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.",
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )
    snake_case				:	Dict							   =						parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						list[tuple[int, int, int]]     )       ->	list[tuple[int, int, int]]:







	'''simple docstring'''


	__magic_name__	:	Union[str, Any]			     =  sorted(_snake_case   ,     key=lambda _snake_case     : edge[2]     )

	__magic_name__	:	Union[str, Any]			     =  list(range(_snake_case     )     )

	def find_parent(_snake_case   :						Dict     ):
		if i != parent[i]:
			__magic_name__	:	Any			     =  find_parent(parent[i]     )
		return parent[i]

	__magic_name__	:	List[str]			     =  0
	__magic_name__	:	int			     =  []

	for edge in edges:
		__magic_name__	:	Any			     =  find_parent(edge[0]     )
		__magic_name__	:	int			     =  find_parent(edge[1]     )
		if parent_a != parent_b:
			minimum_spanning_tree_cost += edge[2]
			minimum_spanning_tree.append(_snake_case     )
			__magic_name__	:	Optional[int]			     =  parent_b

	return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
				snake_case   ,snake_case				:	int							   =						list(map(int, input().strip().split()))
				snake_case				:	int							   =						[]

				for _ in range(num_edges):
								snake_case   ,snake_case   ,snake_case				:	Dict							   =						(int(x) for x in input().strip().split())
								edges.append((nodea, nodea, cost))

				kruskal(num_nodes, edges)
