
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()
lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Tuple:
       lowercase							:					Union[str, Any]	     =				DPTConfig()

       if "large" in checkpoint_url:
              lowercase							:					int	     =				1_024
              lowercase							:					Tuple	     =				4_096
              lowercase							:					Tuple	     =				24
              lowercase							:					Dict	     =				16
              lowercase							:					Tuple	     =				[5, 11, 17, 23]
              lowercase							:					Dict	     =				[256, 512, 1_024, 1_024]
              lowercase							:					Tuple	     =				(1, 384, 384)

       if "ade" in checkpoint_url:
              lowercase							:					Optional[int]	     =				True

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

       return config, expected_shape







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







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

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       for i in range(config.num_hidden_layers    ):
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowercase							:					int	     =				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"    )
              lowercase							:					Optional[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
              lowercase							:					Dict	     =				in_proj_weight[: config.hidden_size, :]
              lowercase							:					Dict	     =				in_proj_bias[: config.hidden_size]
              lowercase							:					str	     =				in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase							:					Dict	     =				in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase							:					Dict	     =				in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowercase							:					str	     =				in_proj_bias[-config.hidden_size :]







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







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

       # load HuggingFace model
       lowercase							:					Optional[int]	     =				DPTForSemanticSegmentation(lowerCamelCase_    ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase_    )
       model.load_state_dict(lowerCamelCase_    )
       model.eval()

       # Check outputs on an image
       lowercase							:					int	     =				480 if """ade""" in checkpoint_url else 384
       lowercase							:					Union[str, Any]	     =				DPTImageProcessor(size=lowerCamelCase_    )

       lowercase							:					List[str]	     =				prepare_img()
       lowercase							:					Tuple	     =				image_processor(lowerCamelCase_ ,     return_tensors="""pt"""    )

       # forward pass
       lowercase							:					Optional[int]	     =				model(**lowerCamelCase_    ).logits if """ade""" in checkpoint_url else model(**lowerCamelCase_    ).predicted_depth

       # Assert logits
       lowercase							:					Any	     =				torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]    )
       if "ade" in checkpoint_url:
              lowercase							:					List[Any]	     =				torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]    )
       assert outputs.shape == torch.Size(lowerCamelCase_    )
       assert (
           torch.allclose(outputs[0, 0, :3, :3] ,     lowerCamelCase_ ,     atol=1e-4    )
           if "ade" in checkpoint_url
           else torch.allclose(outputs[0, :3, :3] ,     lowerCamelCase_    )
       )

       Path(lowerCamelCase_    ).mkdir(exist_ok=lowerCamelCase_    )
       print(f"Saving model to {pytorch_dump_folder_path}"    )
       model.save_pretrained(lowerCamelCase_    )
       print(f"Saving image processor to {pytorch_dump_folder_path}"    )
       image_processor.save_pretrained(lowerCamelCase_    )

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


if __name__ == "__main__":
  lowercase  : str						   =							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.""",
  )

  lowercase  : Optional[int]						   =							parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


class __snake_case		:
    def __init__(  self		,snake_case		,snake_case=None		,snake_case=None				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				data
           lowercase							:					List[Any]	     =				previous
           lowercase							:					List[str]	     =				next_node
    def __str__(  self				):



           '''simple docstring'''







           return f"{self.data}"
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.data
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.next
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.previous






class __snake_case		:
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				head
    def __iter__(  self				):



           '''simple docstring'''







           return self
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if not self.current:
                  raise StopIteration
           else:
                  lowercase							:					Union[str, Any]	     =				self.current.get_data()
                  lowercase							:					Optional[Any]	     =				self.current.get_next()
                  return value






class __snake_case		:
    def __init__(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				None  # First node in list
           lowercase							:					str	     =				None  # Last node in list
    def __str__(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				self.head
           lowercase							:					str	     =				[]
           while current is not None:
                  nodes.append(current.get_data()				)
                  lowercase							:					Dict	     =				current.get_next()
           return " ".join(str(snake_case				) for node in nodes				)
    def __contains__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.head
           while current:
                  if current.get_data() == value:
                         return True
                  lowercase							:					Any	     =				current.get_next()
           return False
    def __iter__(  self				):



           '''simple docstring'''







           return LinkedListIterator(self.head				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.head:
                  return self.head.get_data()
           return None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.tail:
                  return self.tail.get_data()
           return None
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.head is None:
                  lowercase							:					Any	     =				node
                  lowercase							:					Dict	     =				node
           else:
                  self.insert_before_node(self.head		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.head is None:
                  self.set_head(snake_case				)
           else:
                  self.insert_after_node(self.tail		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				Node(snake_case				)
           if self.head is None:
                  self.set_head(snake_case				)
           else:
                  self.set_tail(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				node
           lowercase							:					Optional[int]	     =				node.previous

           if node.get_previous() is None:
                  lowercase							:					Optional[int]	     =				node_to_insert
           else:
                  lowercase							:					Optional[int]	     =				node_to_insert

           lowercase							:					List[Any]	     =				node_to_insert
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				node
           lowercase							:					List[str]	     =				node.next

           if node.get_next() is None:
                  lowercase							:					Union[str, Any]	     =				node_to_insert
           else:
                  lowercase							:					List[str]	     =				node_to_insert

           lowercase							:					Dict	     =				node_to_insert
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				1
           lowercase							:					List[str]	     =				Node(snake_case				)
           lowercase							:					Any	     =				self.head
           while node:
                  if current_position == position:
                         self.insert_before_node(snake_case		,snake_case				)
                         return
                  current_position += 1
                  lowercase							:					List[Any]	     =				node.next
           self.insert_after_node(self.tail		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.head
           while node:
                  if node.get_data() == item:
                         return node
                  lowercase							:					Any	     =				node.get_next()
           raise Exception("""Node not found"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if (node := self.get_node(snake_case				)) is not None:
                  if node == self.head:
                         lowercase							:					Optional[Any]	     =				self.head.get_next()

                  if node == self.tail:
                         lowercase							:					List[str]	     =				self.tail.get_previous()

                  self.remove_node_pointers(snake_case				)
    @staticmethod
    def 				_SCREAMING_SNAKE_CASE	(  snake_case				):



           '''simple docstring'''







           if node.get_next():
                  lowercase							:					Optional[int]	     =				node.previous

           if node.get_previous():
                  lowercase							:					Union[str, Any]	     =				node.next

           lowercase							:					Union[str, Any]	     =				None
           lowercase							:					Optional[Any]	     =				None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.head is None







def 						_snake_case(  )			->   None:
       pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
