






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_						:     List[str]	     =		logging.get_logger(__name__)

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		DPTConfig()

      if "large" in checkpoint_url:
            lowerCamelCase								=		1024
            lowerCamelCase								=		4096
            lowerCamelCase								=		24
            lowerCamelCase								=		16
            lowerCamelCase								=		[5, 11, 17, 23]
            lowerCamelCase								=		[256, 512, 1024, 1024]
            lowerCamelCase								=		(1, 384, 384)

      if "ade" in checkpoint_url:
            lowerCamelCase								=		True

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

      return config, expected_shape

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(_lowerCamelCase   ,   _lowerCamelCase )

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



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

      return name

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for i in range(config.num_hidden_layers ):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            lowerCamelCase								=		state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
            lowerCamelCase								=		state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
            # next, add query, keys and values (in that order) to the state dict
            lowerCamelCase								=		in_proj_weight[: config.hidden_size, :]
            lowerCamelCase								=		in_proj_bias[: config.hidden_size]
            lowerCamelCase								=		in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowerCamelCase								=		in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowerCamelCase								=		in_proj_weight[
                -config.hidden_size :, :
            ]
            lowerCamelCase								=		in_proj_bias[-config.hidden_size :]

def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCamelCase								=		Image.open(requests.get(_lowerCamelCase   ,   stream=_lowerCamelCase ).raw )
      return im

@torch.no_grad()
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		get_dpt_config(_lowerCamelCase )
      # load original state_dict from URL
      lowerCamelCase								=		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():
            lowerCamelCase								=		state_dict.pop(_lowerCamelCase )
            lowerCamelCase								=		val
      # read in qkv matrices
      read_in_q_k_v(_lowerCamelCase   ,   _lowerCamelCase )

      # load HuggingFace model
      lowerCamelCase								=		DPTForSemanticSegmentation(_lowerCamelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase )
      model.load_state_dict(_lowerCamelCase )
      model.eval()

      # Check outputs on an image
      lowerCamelCase								=		480 if """ade""" in checkpoint_url else 384
      lowerCamelCase								=		DPTImageProcessor(size=_lowerCamelCase )

      lowerCamelCase								=		prepare_img()
      lowerCamelCase								=		image_processor(_lowerCamelCase   ,   return_tensors="pt" )

      # forward pass
      lowerCamelCase								=		model(**_lowerCamelCase ).logits if """ade""" in checkpoint_url else model(**_lowerCamelCase ).predicted_depth

      # Assert logits
      lowerCamelCase								=		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:
            lowerCamelCase								=		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(_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__":
     a_						:     Union[str, Any]	     =		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_						:     Optional[int]	     =		parser.parse_args()
     convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[[] for _ in range(__a    )]
           lowerCamelCase								=		size







     def __getitem__(self				,			__a    ):






           '''simple docstring'''



           return iter(self._graph[vertex]    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self._size







     def 						_a      (self				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           if weight not in (0, 1):
                 raise ValueError("Edge weight must be either 0 or 1."    )

           if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("Vertex indexes must be in [0; size)."    )

           self._graph[from_vertex].append(Edge(__a				,			__a    )    )







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		deque([start_vertex]    )
           lowerCamelCase								=		[None] * self.size
           lowerCamelCase								=		0

           while queue:
                 lowerCamelCase								=		queue.popleft()
                 lowerCamelCase								=		distances[current_vertex]
                 if current_distance is None:
                       continue

                 for edge in self[current_vertex]:
                       lowerCamelCase								=		current_distance + edge.weight
                       lowerCamelCase								=		distances[edge.destination_vertex]
                       if (
                           isinstance(__a				,			__a    )
                           and new_distance >= dest_vertex_distance
                       ):
                             continue
                       lowerCamelCase								=		new_distance
                       if edge.weight == 0:
                             queue.appendleft(edge.destination_vertex    )
                       else:
                             queue.append(edge.destination_vertex    )

           if distances[finish_vertex] is None:
                 raise ValueError("No path from start_vertex to finish_vertex."    )

           return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()