



"""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()
_UpperCAmelCase     =   logging.get_logger(__name__)





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=DPTConfig()

   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: int     			=1024
      SCREAMING_SNAKE_CASE_: Dict     			=4096
      SCREAMING_SNAKE_CASE_: Optional[int]     			=24
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=16
      SCREAMING_SNAKE_CASE_: Dict     			=[5, 11, 17, 23]
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[256, 512, 1024, 1024]
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=(1, 384, 384)

   if "ade" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Tuple     			=True

      SCREAMING_SNAKE_CASE_: int     			=150
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""huggingface/label-files"""
      SCREAMING_SNAKE_CASE_: List[str]     			="""ade20k-id2label.json"""
      SCREAMING_SNAKE_CASE_: Tuple     			=json.load(open(cached_download(hf_hub_url(lowercase							,			lowercase							,			repo_type="""dataset""" ) )							,			"""r""" ) )
      SCREAMING_SNAKE_CASE_: int     			={int(lowercase ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=idalabel
      SCREAMING_SNAKE_CASE_: Dict     			={v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_: Dict     			=[1, 150, 480, 480]

   return config, expected_shape





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
   for k in ignore_keys:
      state_dict.pop(lowercase							,			lowercase )





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

   return name





def      __magic_name__				(							lowercase							,			lowercase ):
   for i in range(config.num_hidden_layers ):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      SCREAMING_SNAKE_CASE_: Any     			=state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
      SCREAMING_SNAKE_CASE_: int     			=state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
      # next, add query, keys and values (in that order) to the state dict
      SCREAMING_SNAKE_CASE_: Optional[int]     			=in_proj_weight[: config.hidden_size, :]
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=in_proj_bias[: config.hidden_size]
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      SCREAMING_SNAKE_CASE_: List[str]     			=in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      SCREAMING_SNAKE_CASE_: Any     			=in_proj_weight[
          -config.hidden_size :, :
      ]
      SCREAMING_SNAKE_CASE_: Tuple     			=in_proj_bias[-config.hidden_size :]





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: str     			="""http://images.cocodataset.org/val2017/000000039769.jpg"""
   SCREAMING_SNAKE_CASE_: Optional[int]     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   return im





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=get_dpt_config(lowercase )
   # load original state_dict from URL
   SCREAMING_SNAKE_CASE_: Dict     			=torch.hub.load_state_dict_from_url(lowercase							,			map_location="""cpu""" )
   # remove certain keys
   remove_ignore_keys_(lowercase )
   # rename keys
   for key in state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: Optional[int]     			=state_dict.pop(lowercase )
      SCREAMING_SNAKE_CASE_: Any     			=val
   # read in qkv matrices
   read_in_q_k_v(lowercase							,			lowercase )

   # load HuggingFace model
   SCREAMING_SNAKE_CASE_: List[Any]     			=DPTForSemanticSegmentation(lowercase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowercase )
   model.load_state_dict(lowercase )
   model.eval()

   # Check outputs on an image
   SCREAMING_SNAKE_CASE_: Dict     			=480 if """ade""" in checkpoint_url else 384
   SCREAMING_SNAKE_CASE_: List[str]     			=DPTImageProcessor(size=lowercase )

   SCREAMING_SNAKE_CASE_: int     			=prepare_img()
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image_processor(lowercase							,			return_tensors="""pt""" )

   # forward pass
   SCREAMING_SNAKE_CASE_: List[str]     			=model(**lowercase ).logits if """ade""" in checkpoint_url else model(**lowercase ).predicted_depth

   # Assert logits
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] )
   if "ade" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: int     			=torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] )
   assert outputs.shape == torch.Size(lowercase )
   assert (
       torch.allclose(outputs[0, 0, :3, :3]							,			lowercase							,			atol=1e-4 )
       if "ade" in checkpoint_url
       else torch.allclose(outputs[0, :3, :3]							,			lowercase )
   )

   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:
      print("""Pushing model to hub...""" )
      model.push_to_hub(
          repo_path_or_name=Path(lowercase							,			lowercase )							,			organization="""nielsr"""							,			commit_message="""Add model"""							,			use_temp_dir=lowercase							,			)
      image_processor.push_to_hub(
          repo_path_or_name=Path(lowercase							,			lowercase )							,			organization="""nielsr"""							,			commit_message="""Add image processor"""							,			use_temp_dir=lowercase							,			)


if __name__ == "__main__":
   _UpperCAmelCase     =   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.""",
   )

   _UpperCAmelCase     =   parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase     =			{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =			["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

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



