
"""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      :			Optional[Any]					=							logging.get_logger(__name__)
def 				a__   (   a   :				Tuple		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   DPTConfig()

  if "large" in checkpoint_url:
    _snake_case     :						Tuple						=   1_024
    _snake_case     :						Optional[Any]						=   4_096
    _snake_case     :						Dict						=   24
    _snake_case     :						Any						=   16
    _snake_case     :						Tuple						=   [5, 11, 17, 23]
    _snake_case     :						str						=   [256, 512, 1_024, 1_024]
    _snake_case     :						int						=   (1, 384, 384)

  if "ade" in checkpoint_url:
    _snake_case     :						str						=   True

    _snake_case     :						Optional[int]						=   150
    _snake_case     :						Optional[Any]						=   'huggingface/label-files'
    _snake_case     :						Any						=   'ade20k-id2label.json'
    _snake_case     :						List[Any]						=   json.load(open(cached_download(hf_hub_url(_lowerCamelCase       ,     _lowerCamelCase       ,     repo_type="dataset"		)		)       ,     "r"		)		)
    _snake_case     :						List[Any]						=   {int(_lowerCamelCase		): v for k, v in idalabel.items()}
    _snake_case     :						int						=   idalabel
    _snake_case     :						Optional[Any]						=   {v: k for k, v in idalabel.items()}
    _snake_case     :						str						=   [1, 150, 480, 480]

  return config, expected_shape
def 				a__   (   a   :				Dict		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   ['pretrained.model.head.weight', 'pretrained.model.head.bias']
  for k in ignore_keys:
    state_dict.pop(_lowerCamelCase       ,     _lowerCamelCase		)
def 				a__   (   a   :				List[Any]		):

  """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
  ):
    _snake_case     :						Tuple						=   name.replace("pretrained.model"       ,     "dpt.encoder"		)
  if "pretrained.model" in name:
    _snake_case     :						Tuple						=   name.replace("pretrained.model"       ,     "dpt.embeddings"		)
  if "patch_embed" in name:
    _snake_case     :						int						=   name.replace("patch_embed"       ,     "patch_embeddings"		)
  if "pos_embed" in name:
    _snake_case     :						List[str]						=   name.replace("pos_embed"       ,     "position_embeddings"		)
  if "attn.proj" in name:
    _snake_case     :						List[Any]						=   name.replace("attn.proj"       ,     "attention.output.dense"		)
  if "proj" in name and "project" not in name:
    _snake_case     :						Optional[int]						=   name.replace("proj"       ,     "projection"		)
  if "blocks" in name:
    _snake_case     :						Optional[Any]						=   name.replace("blocks"       ,     "layer"		)
  if "mlp.fc1" in name:
    _snake_case     :						Union[str, Any]						=   name.replace("mlp.fc1"       ,     "intermediate.dense"		)
  if "mlp.fc2" in name:
    _snake_case     :						Union[str, Any]						=   name.replace("mlp.fc2"       ,     "output.dense"		)
  if "norm1" in name:
    _snake_case     :						List[Any]						=   name.replace("norm1"       ,     "layernorm_before"		)
  if "norm2" in name:
    _snake_case     :						List[Any]						=   name.replace("norm2"       ,     "layernorm_after"		)
  if "scratch.output_conv" in name:
    _snake_case     :						List[str]						=   name.replace("scratch.output_conv"       ,     "head"		)
  if "scratch" in name:
    _snake_case     :						Dict						=   name.replace("scratch"       ,     "neck"		)
  if "layer1_rn" in name:
    _snake_case     :						Union[str, Any]						=   name.replace("layer1_rn"       ,     "convs.0"		)
  if "layer2_rn" in name:
    _snake_case     :						Any						=   name.replace("layer2_rn"       ,     "convs.1"		)
  if "layer3_rn" in name:
    _snake_case     :						Any						=   name.replace("layer3_rn"       ,     "convs.2"		)
  if "layer4_rn" in name:
    _snake_case     :						List[Any]						=   name.replace("layer4_rn"       ,     "convs.3"		)
  if "refinenet" in name:
    _snake_case     :						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
    _snake_case     :						List[Any]						=   name.replace(f'refinenet{layer_idx}'       ,     f'fusion_stage.layers.{abs(layer_idx-4		)}'		)
  if "out_conv" in name:
    _snake_case     :						str						=   name.replace("out_conv"       ,     "projection"		)
  if "resConfUnit1" in name:
    _snake_case     :						Tuple						=   name.replace("resConfUnit1"       ,     "residual_layer1"		)
  if "resConfUnit2" in name:
    _snake_case     :						List[str]						=   name.replace("resConfUnit2"       ,     "residual_layer2"		)
  if "conv1" in name:
    _snake_case     :						Dict						=   name.replace("conv1"       ,     "convolution1"		)
  if "conv2" in name:
    _snake_case     :						List[Any]						=   name.replace("conv2"       ,     "convolution2"		)
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    _snake_case     :						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:
    _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:
    _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:
    _snake_case     :						List[str]						=   name.replace("pretrained.act_postprocess4.0.project.0"       ,     "neck.reassemble_stage.readout_projects.3.0"		)
  # resize blocks
  if "pretrained.act_postprocess1.3" in name:
    _snake_case     :						str						=   name.replace("pretrained.act_postprocess1.3"       ,     "neck.reassemble_stage.layers.0.projection"		)
  if "pretrained.act_postprocess1.4" in name:
    _snake_case     :						Dict						=   name.replace("pretrained.act_postprocess1.4"       ,     "neck.reassemble_stage.layers.0.resize"		)
  if "pretrained.act_postprocess2.3" in name:
    _snake_case     :						List[str]						=   name.replace("pretrained.act_postprocess2.3"       ,     "neck.reassemble_stage.layers.1.projection"		)
  if "pretrained.act_postprocess2.4" in name:
    _snake_case     :						List[str]						=   name.replace("pretrained.act_postprocess2.4"       ,     "neck.reassemble_stage.layers.1.resize"		)
  if "pretrained.act_postprocess3.3" in name:
    _snake_case     :						int						=   name.replace("pretrained.act_postprocess3.3"       ,     "neck.reassemble_stage.layers.2.projection"		)
  if "pretrained.act_postprocess4.3" in name:
    _snake_case     :						Optional[int]						=   name.replace("pretrained.act_postprocess4.3"       ,     "neck.reassemble_stage.layers.3.projection"		)
  if "pretrained.act_postprocess4.4" in name:
    _snake_case     :						Optional[int]						=   name.replace("pretrained.act_postprocess4.4"       ,     "neck.reassemble_stage.layers.3.resize"		)
  if "pretrained" in name:
    _snake_case     :						Dict						=   name.replace("pretrained"       ,     "dpt"		)
  if "bn" in name:
    _snake_case     :						Dict						=   name.replace("bn"       ,     "batch_norm"		)
  if "head" in name:
    _snake_case     :						Optional[Any]						=   name.replace("head"       ,     "head.head"		)
  if "encoder.norm" in name:
    _snake_case     :						List[str]						=   name.replace("encoder.norm"       ,     "layernorm"		)
  if "auxlayer" in name:
    _snake_case     :						Dict						=   name.replace("auxlayer"       ,     "auxiliary_head.head"		)

  return name
def 				a__   (   a   :				Optional[int]       ,     a   :				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)
    _snake_case     :						List[str]						=   state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'		)
    _snake_case     :						Tuple						=   state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'		)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case     :						Optional[int]						=   in_proj_weight[: config.hidden_size, :]
    _snake_case     :						int						=   in_proj_bias[: config.hidden_size]
    _snake_case     :						List[str]						=   in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case     :						str						=   in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case     :						Dict						=   in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case     :						int						=   in_proj_bias[-config.hidden_size :]
def 				a__   (   ):

  """simple docstring"""







  _snake_case     :						int						=   'http://images.cocodataset.org/val2017/000000039769.jpg'
  _snake_case     :						List[str]						=   Image.open(requests.get(_lowerCamelCase       ,     stream=_lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 				a__   (   a   :				List[str]       ,     a   :				Optional[Any]       ,     a   :				List[str]       ,     a   :				List[Any]		):

  """simple docstring"""







  _snake_case     :						Dict						=   get_dpt_config(_lowerCamelCase		)
  # load original state_dict from URL
  _snake_case     :						Tuple						=   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():
    _snake_case     :						Dict						=   state_dict.pop(_lowerCamelCase		)
    _snake_case     :						Optional[int]						=   val
  # read in qkv matrices
  read_in_q_k_v(_lowerCamelCase       ,     _lowerCamelCase		)

  # load HuggingFace model
  _snake_case     :						Union[str, Any]						=   DPTForSemanticSegmentation(_lowerCamelCase		) if 'ade' in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase		)
  model.load_state_dict(_lowerCamelCase		)
  model.eval()

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

  _snake_case     :						Any						=   prepare_img()
  _snake_case     :						Tuple						=   image_processor(_lowerCamelCase       ,     return_tensors="pt"		)

  # forward pass
  _snake_case     :						Union[str, Any]						=   model(**_lowerCamelCase		).logits if 'ade' in checkpoint_url else model(**_lowerCamelCase		).predicted_depth

  # Assert logits
  _snake_case     :						Union[str, Any]						=   torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]		)
  if "ade" in checkpoint_url:
    _snake_case     :						int						=   torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]		)
  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__":
    _a      :			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.""",
    )

    _a      :			Tuple					=							parser.parse_args()
    convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_a      :			List[Any]					=							{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			List[Any]					=							["""BartphoTokenizer"""]

if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_bartpho import BartphoTokenizer

else:
    import sys

    _a      :			Optional[int]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

