




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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   DPTConfig(embedding_type='hybrid'					)

      if "large" in checkpoint_url:
            lowerCamelCase_							=   10_24
            lowerCamelCase_							=   40_96
            lowerCamelCase_							=   24
            lowerCamelCase_							=   16
            lowerCamelCase_							=   [5, 11, 17, 23]
            lowerCamelCase_							=   [2_56, 5_12, 10_24, 10_24]
            lowerCamelCase_							=   (1, 3_84, 3_84)

      if "nyu" or "midas" in checkpoint_url:
            lowerCamelCase_							=   7_68
            lowerCamelCase_							=   [1, 1, 1, 0.5]
            lowerCamelCase_							=   [2_56, 5_12, 7_68, 7_68]
            lowerCamelCase_							=   1_50
            lowerCamelCase_							=   16
            lowerCamelCase_							=   (1, 3_84, 3_84)
            lowerCamelCase_							=   False
            lowerCamelCase_							=   'project'

      if "ade" in checkpoint_url:
            lowerCamelCase_							=   True
            lowerCamelCase_							=   7_68
            lowerCamelCase_							=   [1, 1, 1, 0.5]
            lowerCamelCase_							=   1_50
            lowerCamelCase_							=   16
            lowerCamelCase_							=   'huggingface/label-files'
            lowerCamelCase_							=   'ade20k-id2label.json'
            lowerCamelCase_							=   json.load(open(cached_download(hf_hub_url(lowercase     ,							lowercase     ,							repo_type='dataset'					)					)     ,							'r'					)					)
            lowerCamelCase_							=   {int(lowercase					): v for k, v in idalabel.items()}
            lowerCamelCase_							=   idalabel
            lowerCamelCase_							=   {v: k for k, v in idalabel.items()}
            lowerCamelCase_							=   [1, 1_50, 4_80, 4_80]

      return config, expected_shape

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Union[str, Any]					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]					):


      '''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'     ,							''					)
      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 and "backbone" not in name:
            lowerCamelCase_							=   name.replace('norm1'     ,							'layernorm_before'					)
      if "norm2" in name and "backbone" not 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'					)
      if "backbone" in name:
            lowerCamelCase_							=   name.replace('backbone'     ,							'backbone.bit.encoder'					)

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

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	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)
            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       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



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

@torch.no_grad()
def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	Optional[Any]     ,							lowercase     :	List[str]     ,							lowercase     :	List[Any]     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



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

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

      # Check outputs on an image
      lowerCamelCase_							=   4_80 if 'ade' in checkpoint_url else 3_84
      lowerCamelCase_							=   DPTImageProcessor(size=lowercase					)

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

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

      if show_prediction:
            lowerCamelCase_							=   (
                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_55					).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__":
   lowerCamelCase    :		Dict			   =       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",
   )

   lowerCamelCase    :		List[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 ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''transformers''', '''torch''', '''note_seq''']

 def __init__(    self :  Any						,							*A_ :  Any						,							**A_ :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       requires_backends(self						,							['transformers', 'torch', 'note_seq']  )

 @classmethod
 def     a__			(    cls :  Tuple						,							*A_ :  Dict						,							**A_ :  Optional[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       requires_backends(cls						,							['transformers', 'torch', 'note_seq']  )





 @classmethod
 def     a__			(    cls :  int						,							*A_ :  Dict						,							**A_ :  List[str]  )       ->     Tuple:

       """simple docstring"""






       requires_backends(cls						,							['transformers', 'torch', 'note_seq']  )



