"""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()
_a   :	str 					=     logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[str]			) ->   List[str]:
  _lowerCAmelCase		: List[Any]						    =  DPTConfig(embedding_type="""hybrid"""			)

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

  if "nyu" or "midas" in checkpoint_url:
    _lowerCAmelCase		: Tuple						    =  768
    _lowerCAmelCase		: List[str]						    =  [1, 1, 1, 0.5]
    _lowerCAmelCase		: int						    =  [256, 512, 768, 768]
    _lowerCAmelCase		: Optional[Any]						    =  150
    _lowerCAmelCase		: Optional[Any]						    =  16
    _lowerCAmelCase		: Dict						    =  (1, 384, 384)
    _lowerCAmelCase		: str						    =  False
    _lowerCAmelCase		: int						    =  """project"""

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

  return config, expected_shape





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[str]			) ->   Union[str, Any]:
  _lowerCAmelCase		: Union[str, Any]						    =  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
  for k in ignore_keys:
    state_dict.pop(_lowerCamelCase    ,_lowerCamelCase			)





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

  if ".." in name:
    _lowerCAmelCase		: Dict						    =  name.replace(""".."""    ,"""."""			)

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: Optional[Any]			) ->   List[Any]:
  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		: str						    =  state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"			)
    _lowerCAmelCase		: Optional[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
    _lowerCAmelCase		: Any						    =  in_proj_weight[: config.hidden_size, :]
    _lowerCAmelCase		: int						    =  in_proj_bias[: config.hidden_size]
    _lowerCAmelCase		: Any						    =  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _lowerCAmelCase		: Tuple						    =  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _lowerCAmelCase		: List[Any]						    =  in_proj_weight[
        -config.hidden_size :, :
    ]
    _lowerCAmelCase		: Tuple						    =  in_proj_bias[-config.hidden_size :]





def 							SCREAMING_SNAKE_CASE  (    ) ->   str:
  _lowerCAmelCase		: List[Any]						    =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _lowerCAmelCase		: int						    =  Image.open(requests.get(_lowerCamelCase    ,stream=_lowerCamelCase			).raw			)
  return im





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

  # load HuggingFace model
  _lowerCAmelCase		: List[str]						    =  DPTForSemanticSegmentation(_lowerCamelCase			) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase			)
  model.load_state_dict(_lowerCamelCase			)
  model.eval()

  # Check outputs on an image
  _lowerCAmelCase		: Union[str, Any]						    =  480 if """ade""" in checkpoint_url else 384
  _lowerCAmelCase		: Optional[Any]						    =  DPTImageProcessor(size=_lowerCamelCase			)

  _lowerCAmelCase		: List[Any]						    =  prepare_img()
  _lowerCAmelCase		: Optional[Any]						    =  image_processor(_lowerCamelCase    ,return_tensors="""pt"""			)

  # forward pass
  _lowerCAmelCase		: Optional[Any]						    =  model(**_lowerCamelCase			).logits if """ade""" in checkpoint_url else model(**_lowerCamelCase			).predicted_depth

  if show_prediction:
    _lowerCAmelCase		: Union[str, Any]						    =  (
        torch.nn.functional.interpolate(
            outputs.unsqueeze(1			)    ,size=(image.size[1], image.size[0])    ,mode="""bicubic"""    ,align_corners=_lowerCamelCase    ,)
        .squeeze()
        .cpu()
        .numpy()
    )

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

  if pytorch_dump_folder_path is not None:
    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:
    model.push_to_hub("""ybelkada/dpt-hybrid-midas"""			)
    image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""			)


if __name__ == "__main__":
     _a   :	Union[str, Any] 					=     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',
     )

     _a   :	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
     )





"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_a   :	Tuple 					=     logging.get_logger(__name__)
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  *a__       ,  **a__						):
       warnings.warn(
           """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
           """ Please use SegformerImageProcessor instead."""       ,  a__       ,  )
       super().__init__(*a__       ,  **a__						)




