




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						:   List[str]		=   logging.get_logger(__name__)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any	):




 """simple docstring"""







 UpperCAmelCase__							 =						DPTConfig()

 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 "ade" in checkpoint_url:
  UpperCAmelCase__							 =						True

  UpperCAmelCase__							 =						150
  UpperCAmelCase__							 =						"huggingface/label-files"
  UpperCAmelCase__							 =						"ade20k-id2label.json"
  UpperCAmelCase__							 =						json.load(open(cached_download(hf_hub_url(_lowerCAmelCase						,							_lowerCAmelCase						,							repo_type="dataset"	)	)						,							"r"	)	)
  UpperCAmelCase__							 =						{int(_lowerCAmelCase	): 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 						lowerCAmelCase		(		_lowerCAmelCase							:   str	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   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"						,							"patch_embeddings"	)
 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:
  UpperCAmelCase__							 =						name.replace("norm1"						,							"layernorm_before"	)
 if "norm2" 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"	)

 return name



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Optional[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 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						"http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCAmelCase__							 =						Image.open(requests.get(_lowerCAmelCase						,							stream=_lowerCAmelCase	).raw	)
 return im



@torch.no_grad()
def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







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

 # load HuggingFace model
 UpperCAmelCase__							 =						DPTForSemanticSegmentation(_lowerCAmelCase	) if "ade" in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase	)
 model.load_state_dict(_lowerCAmelCase	)
 model.eval()

 # Check outputs on an image
 UpperCAmelCase__							 =						480 if "ade" in checkpoint_url else 384
 UpperCAmelCase__							 =						DPTImageProcessor(size=_lowerCAmelCase	)

 UpperCAmelCase__							 =						prepare_img()
 UpperCAmelCase__							 =						image_processor(_lowerCAmelCase						,							return_tensors="pt"	)

 # forward pass
 UpperCAmelCase__							 =						model(**_lowerCAmelCase	).logits if "ade" in checkpoint_url else model(**_lowerCAmelCase	).predicted_depth

 # Assert logits
 UpperCAmelCase__							 =						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:
  UpperCAmelCase__							 =						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(_lowerCAmelCase	)
 assert (
     torch.allclose(outputs[0, 0, :3, :3]						,							_lowerCAmelCase						,							atol=1E-4	)
     if "ade" in checkpoint_url
     else torch.allclose(outputs[0, :3, :3]						,							_lowerCAmelCase	)
 )

 Path(_lowerCAmelCase	).mkdir(exist_ok=_lowerCAmelCase	)
 print(F'''Saving model to {pytorch_dump_folder_path}'''	)
 model.save_pretrained(_lowerCAmelCase	)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
 image_processor.save_pretrained(_lowerCAmelCase	)

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


if __name__ == "__main__":
  _lowerCAmelCase						:   Optional[int]		=   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.",
  )

  _lowerCAmelCase						:   Dict		=   parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)











from typing import TYPE_CHECKING

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


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   ["MaskFormerFeatureExtractor"]
  _lowerCAmelCase						:   Dict		=   ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  _lowerCAmelCase						:   List[str]		=   [
      "MaskFormerSwinBackbone",
      "MaskFormerSwinModel",
      "MaskFormerSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase						:   str		=   _LazyModule(__name__, globals()["__file__"], _import_structure)





