"""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     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      DPTConfig()

  if "large" in checkpoint_url:
    _UpperCAmelCase						 =      10_24
    _UpperCAmelCase						 =      40_96
    _UpperCAmelCase						 =      24
    _UpperCAmelCase						 =      16
    _UpperCAmelCase						 =      [5, 11, 17, 23]
    _UpperCAmelCase						 =      [2_56, 5_12, 10_24, 10_24]
    _UpperCAmelCase						 =      (1, 3_84, 3_84)

  if "ade" in checkpoint_url:
    _UpperCAmelCase						 =      True

    _UpperCAmelCase						 =      1_50
    _UpperCAmelCase						 =      """huggingface/label-files"""
    _UpperCAmelCase						 =      """ade20k-id2label.json"""
    _UpperCAmelCase						 =      json.load(open(cached_download(hf_hub_url(lowercase   ,lowercase   ,repo_type="""dataset"""		)		)   ,"""r"""		)		)
    _UpperCAmelCase						 =      {int(lowercase		): v for k, v in idalabel.items()}
    _UpperCAmelCase						 =      idalabel
    _UpperCAmelCase						 =      {v: k for k, v in idalabel.items()}
    _UpperCAmelCase						 =      [1, 1_50, 4_80, 4_80]

  return config, expected_shape






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
  for k in ignore_keys:
    state_dict.pop(lowercase   ,lowercase		)






def     __UpperCAmelCase					(			lowercase		):







  """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     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """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     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _UpperCAmelCase						 =      Image.open(requests.get(lowercase   ,stream=lowercase		).raw		)
  return im






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase ,				_UpperCAmelCase						 =      get_dpt_config(lowercase		)
  # load original state_dict from URL
  _UpperCAmelCase						 =      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():
    _UpperCAmelCase						 =      state_dict.pop(lowercase		)
    _UpperCAmelCase						 =      val
  # read in qkv matrices
  read_in_q_k_v(lowercase   ,lowercase		)

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

  # Check outputs on an image
  _UpperCAmelCase						 =      4_80 if """ade""" in checkpoint_url else 3_84
  _UpperCAmelCase						 =      DPTImageProcessor(size=lowercase		)

  _UpperCAmelCase						 =      prepare_img()
  _UpperCAmelCase						 =      image_processor(lowercase   ,return_tensors="""pt"""		)

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

  # Assert logits
  _UpperCAmelCase						 =      torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]		)
  if "ade" in checkpoint_url:
    _UpperCAmelCase						 =      torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]		)
  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"""


def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  _UpperCAmelCase						 =      0
  while n > 0:
    res += n % 10
    n //= 10
  return res






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  return n if n < 10 else n % 10 + sum_of_digits(n // 10		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return sum(int(lowercase		) for c in str(abs(lowercase		)		)		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(lowercase   ,lowercase		) -> None:
    _UpperCAmelCase						 =      f'''{func.__name__}({value})'''
    _UpperCAmelCase						 =      timeit(f'''__main__.{call}'''   ,setup="""import __main__"""		)
    print(f'''{call:56} = {func(lowercase		)} -- {timing:.4f} seconds'''		)

  for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
    for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
      benchmark_a_function(lowercase   ,lowercase		)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
