





'''simple docstring'''

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__						:      Tuple	=    logging.get_logger(__name__)







def  a__    (    lowercase :					Tuple				)		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	DPTConfig(embedding_type='''hybrid'''				)

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

     if "nyu" or "midas" in checkpoint_url:
          _UpperCamelCase       =	768
          _UpperCamelCase       =	[1, 1, 1, 0.5]
          _UpperCamelCase       =	[256, 512, 768, 768]
          _UpperCamelCase       =	150
          _UpperCamelCase       =	16
          _UpperCamelCase       =	(1, 384, 384)
          _UpperCamelCase       =	False
          _UpperCamelCase       =	'''project'''

     if "ade" in checkpoint_url:
          _UpperCamelCase       =	True
          _UpperCamelCase       =	768
          _UpperCamelCase       =	[1, 1, 1, 0.5]
          _UpperCamelCase       =	150
          _UpperCamelCase       =	16
          _UpperCamelCase       =	'''huggingface/label-files'''
          _UpperCamelCase       =	'''ade20k-id2label.json'''
          _UpperCamelCase       =	json.load(open(cached_download(hf_hub_url(lowercase, lowercase, repo_type='''dataset'''				)				), '''r'''				)				)
          _UpperCamelCase       =	{int(lowercase				): v for k, v in idalabel.items()}
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}
          _UpperCamelCase       =	[1, 150, 480, 480]

     return config, expected_shape







def  a__    (    lowercase :					Dict				)		->		Any:





     """simple docstring"""

     _UpperCamelCase       =	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
     for k in ignore_keys:
          state_dict.pop(lowercase, lowercase				)







def  a__    (    lowercase :					str				)		->		Dict:





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

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          _UpperCamelCase       =	name.replace('''pretrained.act_postprocess1.3''', '''neck.reassemble_stage.layers.0.projection'''				)
     if "pretrained.act_postprocess1.4" in name:
          _UpperCamelCase       =	name.replace('''pretrained.act_postprocess1.4''', '''neck.reassemble_stage.layers.0.resize'''				)
     if "pretrained.act_postprocess2.3" in name:
          _UpperCamelCase       =	name.replace('''pretrained.act_postprocess2.3''', '''neck.reassemble_stage.layers.1.projection'''				)
     if "pretrained.act_postprocess2.4" in name:
          _UpperCamelCase       =	name.replace('''pretrained.act_postprocess2.4''', '''neck.reassemble_stage.layers.1.resize'''				)
     if "pretrained.act_postprocess3.3" in name:
          _UpperCamelCase       =	name.replace('''pretrained.act_postprocess3.3''', '''neck.reassemble_stage.layers.2.projection'''				)
     if "pretrained.act_postprocess4.3" in name:
          _UpperCamelCase       =	name.replace('''pretrained.act_postprocess4.3''', '''neck.reassemble_stage.layers.3.projection'''				)
     if "pretrained.act_postprocess4.4" in name:
          _UpperCamelCase       =	name.replace('''pretrained.act_postprocess4.4''', '''neck.reassemble_stage.layers.3.resize'''				)
     if "pretrained" in name:
          _UpperCamelCase       =	name.replace('''pretrained''', '''dpt'''				)
     if "bn" in name:
          _UpperCamelCase       =	name.replace('''bn''', '''batch_norm'''				)
     if "head" in name:
          _UpperCamelCase       =	name.replace('''head''', '''head.head'''				)
     if "encoder.norm" in name:
          _UpperCamelCase       =	name.replace('''encoder.norm''', '''layernorm'''				)
     if "auxlayer" in name:
          _UpperCamelCase       =	name.replace('''auxlayer''', '''auxiliary_head.head'''				)
     if "backbone" in name:
          _UpperCamelCase       =	name.replace('''backbone''', '''backbone.bit.encoder'''				)

     if ".." in name:
          _UpperCamelCase       =	name.replace('''..''', '''.'''				)

     if "stem.conv" in name:
          _UpperCamelCase       =	name.replace('''stem.conv''', '''bit.embedder.convolution'''				)
     if "blocks" in name:
          _UpperCamelCase       =	name.replace('''blocks''', '''layers'''				)
     if "convolution" in name and "backbone" in name:
          _UpperCamelCase       =	name.replace('''convolution''', '''conv'''				)
     if "layer" in name and "backbone" in name:
          _UpperCamelCase       =	name.replace('''layer''', '''layers'''				)
     if "backbone.bit.encoder.bit" in name:
          _UpperCamelCase       =	name.replace('''backbone.bit.encoder.bit''', '''backbone.bit'''				)
     if "embedder.conv" in name:
          _UpperCamelCase       =	name.replace('''embedder.conv''', '''embedder.convolution'''				)
     if "backbone.bit.encoder.stem.norm" in name:
          _UpperCamelCase       =	name.replace('''backbone.bit.encoder.stem.norm''', '''backbone.bit.embedder.norm'''				)
     return name







def  a__    (    lowercase :					Any, lowercase :					Tuple				)		->		int:





     """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)
          _UpperCamelCase       =	state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""				)
          _UpperCamelCase       =	state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""				)
          # next, add query, keys and values (in that order) to the state dict
          _UpperCamelCase       =	in_proj_weight[: config.hidden_size, :]
          _UpperCamelCase       =	in_proj_bias[: config.hidden_size]
          _UpperCamelCase       =	in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _UpperCamelCase       =	in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _UpperCamelCase       =	in_proj_weight[
              -config.hidden_size :, :
          ]
          _UpperCamelCase       =	in_proj_bias[-config.hidden_size :]







def  a__    (    )		->		List[Any]:





     """simple docstring"""

     _UpperCamelCase       =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _UpperCamelCase       =	Image.open(requests.get(lowercase, stream=lowercase				).raw				)
     return im







@torch.no_grad()
def  a__    (    lowercase :					Optional[Any], lowercase :					Union[str, Any], lowercase :					List[str], lowercase :					str, lowercase :					Optional[Any]				)		->		List[Any]:





     """simple docstring"""

     _UpperCamelCase   ,    _UpperCamelCase       =	get_dpt_config(lowercase				)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     _UpperCamelCase       =	torch.load(lowercase, map_location='''cpu'''				)
     # remove certain keys
     remove_ignore_keys_(lowercase				)
     # rename keys
     for key in state_dict.copy().keys():
          _UpperCamelCase       =	state_dict.pop(lowercase				)
          _UpperCamelCase       =	val
     # read in qkv matrices
     read_in_q_k_v(lowercase, lowercase				)

     # load HuggingFace model
     _UpperCamelCase       =	DPTForSemanticSegmentation(lowercase				) if '''ade''' in checkpoint_url else DPTForDepthEstimation(lowercase				)
     model.load_state_dict(lowercase				)
     model.eval()

     # Check outputs on an image
     _UpperCamelCase       =	480 if '''ade''' in checkpoint_url else 384
     _UpperCamelCase       =	DPTImageProcessor(size=lowercase				)

     _UpperCamelCase       =	prepare_img()
     _UpperCamelCase       =	image_processor(lowercase, return_tensors='''pt'''				)

     # forward pass
     _UpperCamelCase       =	model(**lowercase				).logits if '''ade''' in checkpoint_url else model(**lowercase				).predicted_depth

     if show_prediction:
          _UpperCamelCase       =	(
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1				), size=(image.size[1], image.size[0]), mode='''bicubic''', align_corners=lowercase, )
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255				).show()

     if pytorch_dump_folder_path is not None:
          Path(lowercase				).mkdir(exist_ok=lowercase				)
          print(F"""Saving model to {pytorch_dump_folder_path}"""				)
          model.save_pretrained(lowercase				)
          print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
          image_processor.save_pretrained(lowercase				)

     if push_to_hub:
          model.push_to_hub('''ybelkada/dpt-hybrid-midas'''				)
          image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''				)


if __name__ == "__main__":
   lowercase__						:      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=False,
       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.',
   )
   parser.add_argument(
       '--show_prediction',
       action='store_true',
   )

   lowercase__						:      Any	=    parser.parse_args()
   convert_dpt_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
   )










'''simple docstring'''

def  a__    (    lowercase :					int				)		->		int:





     """simple docstring"""

     if not isinstance(lowercase, lowercase				):
          raise TypeError('''Input value must be an \'int\' type'''				)

     _UpperCamelCase       =	0
     while number:
          position += 1
          number >>= 1

     return position


if __name__ == "__main__":
   import doctest

   doctest.testmod()



