

'''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_       						=							logging.get_logger(__name__)





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					DPTConfig(embedding_type="hybrid" )

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

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

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

  return config, expected_shape





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					["pretrained.model.head.weight", "pretrained.model.head.bias"]
  for k in ignore_keys:
    state_dict.pop(snake_case      ,       snake_case )





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

  if ".." in name:
    SCREAMING_SNAKE_CASE:Tuple						      =					name.replace(".."      ,       "." )

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





def    A_  ( snake_case      ,       snake_case ):
  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:Optional[int]						      =					state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
    SCREAMING_SNAKE_CASE: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
    SCREAMING_SNAKE_CASE:Tuple						      =					in_proj_weight[: config.hidden_size, :]
    SCREAMING_SNAKE_CASE:str						      =					in_proj_bias[: config.hidden_size]
    SCREAMING_SNAKE_CASE:List[str]						      =					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    SCREAMING_SNAKE_CASE:int						      =					in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					in_proj_weight[
        -config.hidden_size :, :
    ]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					in_proj_bias[-config.hidden_size :]





def    A_  ( ):
  SCREAMING_SNAKE_CASE:str						      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:Tuple						      =					Image.open(requests.get(snake_case      ,       stream=snake_case ).raw )
  return im





@torch.no_grad()
def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					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")
  SCREAMING_SNAKE_CASE:Dict						      =					torch.load(snake_case      ,       map_location="cpu" )
  # remove certain keys
  remove_ignore_keys_(snake_case )
  # rename keys
  for key in state_dict.copy().keys():
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					state_dict.pop(snake_case )
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					val
  # read in qkv matrices
  read_in_q_k_v(snake_case      ,       snake_case )

  # load HuggingFace model
  SCREAMING_SNAKE_CASE: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
  SCREAMING_SNAKE_CASE:Any						      =					480 if "ade" in checkpoint_url else 384
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					DPTImageProcessor(size=snake_case )

  SCREAMING_SNAKE_CASE:Optional[int]						      =					prepare_img()
  SCREAMING_SNAKE_CASE:int						      =					image_processor(snake_case      ,       return_tensors="pt" )

  # forward pass
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model(**snake_case ).logits if "ade" in checkpoint_url else model(**snake_case ).predicted_depth

  if show_prediction:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					(
        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__":
    A_       						=							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_       						=							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'''

def    A_  ( snake_case = 100 ):
  SCREAMING_SNAKE_CASE:Dict						      =					0
  SCREAMING_SNAKE_CASE:Optional[int]						      =					0
  for i in range(1      ,       n + 1 ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
    print(f'''{solution() = }''')






