





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()
snake_case								=							logging.get_logger(__name__)

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any  )   ->    str:
  _lowercase						    =			DPTConfig(embedding_type='hybrid'  )

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

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

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

  return config, expected_shape

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str  )   ->    str:
  _lowercase						    =			['pretrained.model.head.weight', 'pretrained.model.head.bias']
  for k in ignore_keys:
    state_dict.pop(snake_case__      ,				snake_case__  )

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

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

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:int  )   ->    Dict:
  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						    =			state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""  )
    _lowercase						    =			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						    =			in_proj_weight[: config.hidden_size, :]
    _lowercase						    =			in_proj_bias[: config.hidden_size]
    _lowercase						    =			in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _lowercase						    =			in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _lowercase						    =			in_proj_weight[
        -config.hidden_size :, :
    ]
    _lowercase						    =			in_proj_bias[-config.hidden_size :]

def 				SCREAMING_SNAKE_CASE__							(	)   ->    Tuple:
  _lowercase						    =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase						    =			Image.open(requests.get(snake_case__      ,				stream=snake_case__  ).raw  )
  return im

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

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

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

  _lowercase						    =			prepare_img()
  _lowercase						    =			image_processor(snake_case__      ,				return_tensors='pt'  )

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

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

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

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

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


if __name__ == "__main__":
      snake_case								=							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""",
      )

      snake_case								=							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'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """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

      _UpperCamelCase 							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

