



import argparse
import json
from pathlib import Path

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

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__    =	logging.get_logger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     int:
 _snake_case    :  List[str]					=					DPTConfig()

 if "large" in checkpoint_url:
  _snake_case    :  Any					=					1_024
  _snake_case    :  List[Any]					=					4_096
  _snake_case    :  Optional[int]					=					24
  _snake_case    :  Union[str, Any]					=					16
  _snake_case    :  Any					=					[5, 11, 17, 23]
  _snake_case    :  Optional[int]					=					[256, 512, 1_024, 1_024]
  _snake_case    :  str					=					(1, 384, 384)

 if "ade" in checkpoint_url:
  _snake_case    :  Any					=					True

  _snake_case    :  List[str]					=					150
  _snake_case    :  Tuple					=					"""huggingface/label-files"""
  _snake_case    :  Optional[Any]					=					"""ade20k-id2label.json"""
  _snake_case    :  Any					=					json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       repo_type="""dataset"""				)				)				,       """r"""				)				)
  _snake_case    :  Any					=					{int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
  _snake_case    :  List[Any]					=					idalabel
  _snake_case    :  Optional[Any]					=					{v: k for k, v in idalabel.items()}
  _snake_case    :  List[Any]					=					[1, 150, 480, 480]

 return config, expected_shape



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     List[Any]:
 _snake_case    :  Optional[int]					=					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
 for k in ignore_keys:
  state_dict.pop(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				)  ->     Any:
 if (
     "pretrained.model" in name
     and "cls_token" not in name
     and "pos_embed" not in name
     and "patch_embed" not in name
 ):
  _snake_case    :  int					=					name.replace("""pretrained.model"""				,       """dpt.encoder"""				)
 if "pretrained.model" in name:
  _snake_case    :  List[Any]					=					name.replace("""pretrained.model"""				,       """dpt.embeddings"""				)
 if "patch_embed" in name:
  _snake_case    :  str					=					name.replace("""patch_embed"""				,       """patch_embeddings"""				)
 if "pos_embed" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""pos_embed"""				,       """position_embeddings"""				)
 if "attn.proj" in name:
  _snake_case    :  List[str]					=					name.replace("""attn.proj"""				,       """attention.output.dense"""				)
 if "proj" in name and "project" not in name:
  _snake_case    :  int					=					name.replace("""proj"""				,       """projection"""				)
 if "blocks" in name:
  _snake_case    :  int					=					name.replace("""blocks"""				,       """layer"""				)
 if "mlp.fc1" in name:
  _snake_case    :  Optional[Any]					=					name.replace("""mlp.fc1"""				,       """intermediate.dense"""				)
 if "mlp.fc2" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""mlp.fc2"""				,       """output.dense"""				)
 if "norm1" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""norm1"""				,       """layernorm_before"""				)
 if "norm2" in name:
  _snake_case    :  List[Any]					=					name.replace("""norm2"""				,       """layernorm_after"""				)
 if "scratch.output_conv" in name:
  _snake_case    :  Optional[Any]					=					name.replace("""scratch.output_conv"""				,       """head"""				)
 if "scratch" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""scratch"""				,       """neck"""				)
 if "layer1_rn" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""layer1_rn"""				,       """convs.0"""				)
 if "layer2_rn" in name:
  _snake_case    :  int					=					name.replace("""layer2_rn"""				,       """convs.1"""				)
 if "layer3_rn" in name:
  _snake_case    :  str					=					name.replace("""layer3_rn"""				,       """convs.2"""				)
 if "layer4_rn" in name:
  _snake_case    :  List[str]					=					name.replace("""layer4_rn"""				,       """convs.3"""				)
 if "refinenet" in name:
  _snake_case    :  List[str]					=					int(name[len("""neck.refinenet"""				) : len("""neck.refinenet"""				) + 1]				)
  # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
  _snake_case    :  str					=					name.replace(F'''refinenet{layer_idx}'''				,       F'''fusion_stage.layers.{abs(layer_idx-4				)}'''				)
 if "out_conv" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""out_conv"""				,       """projection"""				)
 if "resConfUnit1" in name:
  _snake_case    :  Optional[int]					=					name.replace("""resConfUnit1"""				,       """residual_layer1"""				)
 if "resConfUnit2" in name:
  _snake_case    :  List[Any]					=					name.replace("""resConfUnit2"""				,       """residual_layer2"""				)
 if "conv1" in name:
  _snake_case    :  Dict					=					name.replace("""conv1"""				,       """convolution1"""				)
 if "conv2" in name:
  _snake_case    :  Optional[int]					=					name.replace("""conv2"""				,       """convolution2"""				)
 # readout blocks
 if "pretrained.act_postprocess1.0.project.0" in name:
  _snake_case    :  Any					=					name.replace("""pretrained.act_postprocess1.0.project.0"""				,       """neck.reassemble_stage.readout_projects.0.0"""				)
 if "pretrained.act_postprocess2.0.project.0" in name:
  _snake_case    :  Tuple					=					name.replace("""pretrained.act_postprocess2.0.project.0"""				,       """neck.reassemble_stage.readout_projects.1.0"""				)
 if "pretrained.act_postprocess3.0.project.0" in name:
  _snake_case    :  str					=					name.replace("""pretrained.act_postprocess3.0.project.0"""				,       """neck.reassemble_stage.readout_projects.2.0"""				)
 if "pretrained.act_postprocess4.0.project.0" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""pretrained.act_postprocess4.0.project.0"""				,       """neck.reassemble_stage.readout_projects.3.0"""				)
 # resize blocks
 if "pretrained.act_postprocess1.3" in name:
  _snake_case    :  Optional[int]					=					name.replace("""pretrained.act_postprocess1.3"""				,       """neck.reassemble_stage.layers.0.projection"""				)
 if "pretrained.act_postprocess1.4" in name:
  _snake_case    :  Optional[Any]					=					name.replace("""pretrained.act_postprocess1.4"""				,       """neck.reassemble_stage.layers.0.resize"""				)
 if "pretrained.act_postprocess2.3" in name:
  _snake_case    :  Any					=					name.replace("""pretrained.act_postprocess2.3"""				,       """neck.reassemble_stage.layers.1.projection"""				)
 if "pretrained.act_postprocess2.4" in name:
  _snake_case    :  List[Any]					=					name.replace("""pretrained.act_postprocess2.4"""				,       """neck.reassemble_stage.layers.1.resize"""				)
 if "pretrained.act_postprocess3.3" in name:
  _snake_case    :  Optional[Any]					=					name.replace("""pretrained.act_postprocess3.3"""				,       """neck.reassemble_stage.layers.2.projection"""				)
 if "pretrained.act_postprocess4.3" in name:
  _snake_case    :  Any					=					name.replace("""pretrained.act_postprocess4.3"""				,       """neck.reassemble_stage.layers.3.projection"""				)
 if "pretrained.act_postprocess4.4" in name:
  _snake_case    :  int					=					name.replace("""pretrained.act_postprocess4.4"""				,       """neck.reassemble_stage.layers.3.resize"""				)
 if "pretrained" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""pretrained"""				,       """dpt"""				)
 if "bn" in name:
  _snake_case    :  Optional[int]					=					name.replace("""bn"""				,       """batch_norm"""				)
 if "head" in name:
  _snake_case    :  List[Any]					=					name.replace("""head"""				,       """head.head"""				)
 if "encoder.norm" in name:
  _snake_case    :  int					=					name.replace("""encoder.norm"""				,       """layernorm"""				)
 if "auxlayer" in name:
  _snake_case    :  Tuple					=					name.replace("""auxlayer"""				,       """auxiliary_head.head"""				)

 return name



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     Dict:
 for i in range(config.num_hidden_layers				):
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  _snake_case    :  int					=					state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''				)
  _snake_case    :  int					=					state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''				)
  # next, add query, keys and values (in that order) to the state dict
  _snake_case    :  Optional[int]					=					in_proj_weight[: config.hidden_size, :]
  _snake_case    :  Dict					=					in_proj_bias[: config.hidden_size]
  _snake_case    :  Any					=					in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  _snake_case    :  Optional[int]					=					in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  _snake_case    :  List[str]					=					in_proj_weight[
      -config.hidden_size :, :
  ]
  _snake_case    :  Tuple					=					in_proj_bias[-config.hidden_size :]



def       lowercase       (   )  ->     Tuple:
 _snake_case    :  str					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Optional[int]					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)
 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     Optional[int]:
 _snake_case	,    _snake_case    :  str					=					get_dpt_config(SCREAMING_SNAKE_CASE__				)
 # load original state_dict from URL
 _snake_case    :  List[str]					=					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__				,       map_location="""cpu"""				)
 # remove certain keys
 remove_ignore_keys_(SCREAMING_SNAKE_CASE__				)
 # rename keys
 for key in state_dict.copy().keys():
  _snake_case    :  Dict					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Optional[Any]					=					val
 # read in qkv matrices
 read_in_q_k_v(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 # load HuggingFace model
 _snake_case    :  Union[str, Any]					=					DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE__				) if """ade""" in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE__				)
 model.load_state_dict(SCREAMING_SNAKE_CASE__				)
 model.eval()

 # Check outputs on an image
 _snake_case    :  List[str]					=					480 if """ade""" in checkpoint_url else 384
 _snake_case    :  Optional[int]					=					DPTImageProcessor(size=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[Any]					=					prepare_img()
 _snake_case    :  Dict					=					image_processor(SCREAMING_SNAKE_CASE__				,       return_tensors="""pt"""				)

 # forward pass
 _snake_case    :  List[str]					=					model(**SCREAMING_SNAKE_CASE__				).logits if """ade""" in checkpoint_url else model(**SCREAMING_SNAKE_CASE__				).predicted_depth

 # Assert logits
 _snake_case    :  Tuple					=					torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]				)
 if "ade" in checkpoint_url:
  _snake_case    :  Dict					=					torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]				)
 assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE__				)
 assert (
     torch.allclose(outputs[0, 0, :3, :3]				,       SCREAMING_SNAKE_CASE__				,       atol=1e-4				)
     if "ade" in checkpoint_url
     else torch.allclose(outputs[0, :3, :3]				,       SCREAMING_SNAKE_CASE__				)
 )

 Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
 print(F'''Saving model to {pytorch_dump_folder_path}'''				)
 model.save_pretrained(SCREAMING_SNAKE_CASE__				)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

 if push_to_hub:
  print("""Pushing model to hub..."""				)
  model.push_to_hub(
      repo_path_or_name=Path(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)				,       organization="""nielsr"""				,       commit_message="""Add model"""				,       use_temp_dir=SCREAMING_SNAKE_CASE__				,       )
  image_processor.push_to_hub(
      repo_path_or_name=Path(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)				,       organization="""nielsr"""				,       commit_message="""Add image processor"""				,       use_temp_dir=SCREAMING_SNAKE_CASE__				,       )


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
      type=str,
      help="""URL of the original DPT checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
  )
  parser.add_argument(
      """--model_name""",
      default="""dpt-large""",
      type=str,
      help="""Name of the model, in case you're pushing to the hub.""",
  )

  a__    =	parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
