





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  :   Optional[int]						      =logging.get_logger(__name__)




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=DPTConfig(embedding_type="hybrid"  )

   if "large" in checkpoint_url:
      UpperCAmelCase_											=1_0_2_4
      UpperCAmelCase_											=4_0_9_6
      UpperCAmelCase_											=2_4
      UpperCAmelCase_											=1_6
      UpperCAmelCase_											=[5, 1_1, 1_7, 2_3]
      UpperCAmelCase_											=[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
      UpperCAmelCase_											=(1, 3_8_4, 3_8_4)

   if "nyu" or "midas" in checkpoint_url:
      UpperCAmelCase_											=7_6_8
      UpperCAmelCase_											=[1, 1, 1, 0.5]
      UpperCAmelCase_											=[2_5_6, 5_1_2, 7_6_8, 7_6_8]
      UpperCAmelCase_											=1_5_0
      UpperCAmelCase_											=1_6
      UpperCAmelCase_											=(1, 3_8_4, 3_8_4)
      UpperCAmelCase_											=False
      UpperCAmelCase_											="project"

   if "ade" in checkpoint_url:
      UpperCAmelCase_											=True
      UpperCAmelCase_											=7_6_8
      UpperCAmelCase_											=[1, 1, 1, 0.5]
      UpperCAmelCase_											=1_5_0
      UpperCAmelCase_											=1_6
      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, 1_5_0, 4_8_0, 4_8_0]

   return config, expected_shape




def     a__ (					lowercase__  ):






   '''simple docstring'''






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




def     a__ (					lowercase__  ):






   '''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__  ,    lowercase__  ):






   '''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__ (					):






   '''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__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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_											=4_8_0 if "ade" in checkpoint_url else 3_8_4
   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()) * 2_5_5  ).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  :   Tuple						      =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  :   str						      =parser.parse_args()
     convert_dpt_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
     )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase  :   Any						      ={
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =[
         """UperNetForSemanticSegmentation""",
         """UperNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_upernet import UperNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
     import sys

     __lowercase  :   Any						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




