



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         =			logging.get_logger(__name__)







def    _snake_case (       __snake_case    )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		DPTConfig(embedding_type="hybrid"    )

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

      if "nyu" or "midas" in checkpoint_url:
            UpperCAmelCase_  :	Dict				      =		7_6_8
            UpperCAmelCase_  :	Dict				      =		[1, 1, 1, 0.5]
            UpperCAmelCase_  :	Optional[int]				      =		[2_5_6, 5_1_2, 7_6_8, 7_6_8]
            UpperCAmelCase_  :	Tuple				      =		1_5_0
            UpperCAmelCase_  :	Union[str, Any]				      =		1_6
            UpperCAmelCase_  :	Optional[Any]				      =		(1, 3_8_4, 3_8_4)
            UpperCAmelCase_  :	List[Any]				      =		False
            UpperCAmelCase_  :	Dict				      =		"project"

      if "ade" in checkpoint_url:
            UpperCAmelCase_  :	Dict				      =		True
            UpperCAmelCase_  :	Any				      =		7_6_8
            UpperCAmelCase_  :	List[Any]				      =		[1, 1, 1, 0.5]
            UpperCAmelCase_  :	int				      =		1_5_0
            UpperCAmelCase_  :	int				      =		1_6
            UpperCAmelCase_  :	str				      =		"huggingface/label-files"
            UpperCAmelCase_  :	Dict				      =		"ade20k-id2label.json"
            UpperCAmelCase_  :	Dict				      =		json.load(open(cached_download(hf_hub_url(__snake_case					,  __snake_case					,  repo_type="dataset"    )    )					,  "r"    )    )
            UpperCAmelCase_  :	Dict				      =		{int(__snake_case    ): v for k, v in idalabel.items()}
            UpperCAmelCase_  :	Tuple				      =		idalabel
            UpperCAmelCase_  :	Union[str, Any]				      =		{v: k for k, v in idalabel.items()}
            UpperCAmelCase_  :	Optional[Any]				      =		[1, 1_5_0, 4_8_0, 4_8_0]

      return config, expected_shape







def    _snake_case (       __snake_case    )     ->     List[Any]:






      '''simple docstring'''







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







def    _snake_case (       __snake_case    )     ->     Tuple:






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

      if ".." in name:
            UpperCAmelCase_  :	str				      =		name.replace(".."					,  "."    )

      if "stem.conv" in name:
            UpperCAmelCase_  :	Union[str, Any]				      =		name.replace("stem.conv"					,  "bit.embedder.convolution"    )
      if "blocks" in name:
            UpperCAmelCase_  :	Optional[int]				      =		name.replace("blocks"					,  "layers"    )
      if "convolution" in name and "backbone" in name:
            UpperCAmelCase_  :	str				      =		name.replace("convolution"					,  "conv"    )
      if "layer" in name and "backbone" in name:
            UpperCAmelCase_  :	List[str]				      =		name.replace("layer"					,  "layers"    )
      if "backbone.bit.encoder.bit" in name:
            UpperCAmelCase_  :	str				      =		name.replace("backbone.bit.encoder.bit"					,  "backbone.bit"    )
      if "embedder.conv" in name:
            UpperCAmelCase_  :	int				      =		name.replace("embedder.conv"					,  "embedder.convolution"    )
      if "backbone.bit.encoder.stem.norm" in name:
            UpperCAmelCase_  :	Optional[Any]				      =		name.replace("backbone.bit.encoder.stem.norm"					,  "backbone.bit.embedder.norm"    )
      return name







def    _snake_case (       __snake_case					,  __snake_case    )     ->     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_  :	Dict				      =		state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""    )
            UpperCAmelCase_  :	List[Any]				      =		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_  :	Optional[int]				      =		in_proj_weight[: config.hidden_size, :]
            UpperCAmelCase_  :	List[str]				      =		in_proj_bias[: config.hidden_size]
            UpperCAmelCase_  :	int				      =		in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            UpperCAmelCase_  :	Tuple				      =		in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            UpperCAmelCase_  :	Tuple				      =		in_proj_weight[
                -config.hidden_size :, :
            ]
            UpperCAmelCase_  :	List[Any]				      =		in_proj_bias[-config.hidden_size :]







def    _snake_case (       )     ->     List[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_  :	List[str]				      =		Image.open(requests.get(__snake_case					,  stream=__snake_case    ).raw    )
      return im







@torch.no_grad()
def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     List[str]:






      '''simple docstring'''







      UpperCAmelCase_  :	int				      =		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")
      UpperCAmelCase_  :	str				      =		torch.load(__snake_case					,  map_location="cpu"    )
      # remove certain keys
      remove_ignore_keys_(__snake_case    )
      # rename keys
      for key in state_dict.copy().keys():
            UpperCAmelCase_  :	Union[str, Any]				      =		state_dict.pop(__snake_case    )
            UpperCAmelCase_  :	Dict				      =		val
      # read in qkv matrices
      read_in_q_k_v(__snake_case					,  __snake_case    )

      # load HuggingFace model
      UpperCAmelCase_  :	str				      =		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
      UpperCAmelCase_  :	Any				      =		4_8_0 if "ade" in checkpoint_url else 3_8_4
      UpperCAmelCase_  :	str				      =		DPTImageProcessor(size=__snake_case    )

      UpperCAmelCase_  :	Any				      =		prepare_img()
      UpperCAmelCase_  :	Union[str, Any]				      =		image_processor(__snake_case					,  return_tensors="pt"    )

      # forward pass
      UpperCAmelCase_  :	List[Any]				      =		model(**__snake_case    ).logits if "ade" in checkpoint_url else model(**__snake_case    ).predicted_depth

      if show_prediction:
            UpperCAmelCase_  :	List[str]				      =		(
                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()) * 2_5_5    ).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__":
   __lowerCamelCase         =			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         =			parser.parse_args()
   convert_dpt_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
   )











import logging

from transformers import PretrainedConfig


__lowerCamelCase         =			logging.getLogger(__name__)


__lowerCamelCase         =			{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""bertabs"""



       def __init__(     self     ,lowercase=30522     ,lowercase=512     ,lowercase=6     ,lowercase=512     ,lowercase=8     ,lowercase=512     ,lowercase=0.2     ,lowercase=6     ,lowercase=768     ,lowercase=8     ,lowercase=2048     ,lowercase=0.2     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_  :	Any				      =		vocab_size
             UpperCAmelCase_  :	int				      =		max_pos

             UpperCAmelCase_  :	List[str]				      =		enc_layers
             UpperCAmelCase_  :	List[str]				      =		enc_hidden_size
             UpperCAmelCase_  :	Any				      =		enc_heads
             UpperCAmelCase_  :	Any				      =		enc_ff_size
             UpperCAmelCase_  :	Optional[int]				      =		enc_dropout

             UpperCAmelCase_  :	List[str]				      =		dec_layers
             UpperCAmelCase_  :	List[Any]				      =		dec_hidden_size
             UpperCAmelCase_  :	List[str]				      =		dec_heads
             UpperCAmelCase_  :	List[Any]				      =		dec_ff_size
             UpperCAmelCase_  :	List[str]				      =		dec_dropout
