



'''simple docstring'''





from ..utils import DummyObject, requires_backends






class 					lowerCamelCase_    (				metaclass=SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=["""keras_nlp"""]




  def __init__( self      :	Optional[int]			,	*_a      :	Tuple			,	**_a      :	Any   )					->					Any:
    requires_backends(self			,	['keras_nlp']   )











'''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        a_     (							_lowerCAmelCase       )      ->      Tuple:
  __lowerCamelCase       :       Optional[int]           = DPTConfig()

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

  if "ade" in checkpoint_url:
    __lowerCamelCase       :       Tuple           = True

    __lowerCamelCase       :       Union[str, Any]           = 150
    __lowerCamelCase       :       Any           = 'huggingface/label-files'
    __lowerCamelCase       :       Dict           = 'ade20k-id2label.json'
    __lowerCamelCase       :       Tuple           = 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       :       List[str]           = {v: k for k, v in idalabel.items()}
    __lowerCamelCase       :       Optional[int]           = [1, 150, 480, 480]

  return config, expected_shape

def        a_     (							_lowerCAmelCase       )      ->      Tuple:
  __lowerCamelCase       :       Optional[int]           = ['pretrained.model.head.weight', 'pretrained.model.head.bias']
  for k in ignore_keys:
    state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )

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

  return name

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[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       :       Union[str, Any]           = state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'       )
    __lowerCamelCase       :       Optional[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
    __lowerCamelCase       :       Union[str, Any]           = in_proj_weight[: config.hidden_size, :]
    __lowerCamelCase       :       List[Any]           = in_proj_bias[: config.hidden_size]
    __lowerCamelCase       :       Union[str, Any]           = in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    __lowerCamelCase       :       Any           = in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    __lowerCamelCase       :       Any           = in_proj_weight[
        -config.hidden_size :, :
    ]
    __lowerCamelCase       :       Optional[Any]           = in_proj_bias[-config.hidden_size :]

def        a_     (							)      ->      Optional[int]:
  __lowerCamelCase       :       int           = 'http://images.cocodataset.org/val2017/000000039769.jpg'
  __lowerCamelCase       :       Dict           = Image.open(requests.get(_lowerCAmelCase       ,stream=_lowerCAmelCase       ).raw       )
  return im

@torch.no_grad()
def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      List[Any]:
  __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = get_dpt_config(_lowerCAmelCase       )
  # load original state_dict from URL
  __lowerCamelCase       :       str           = 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():
    __lowerCamelCase       :       int           = state_dict.pop(_lowerCAmelCase       )
    __lowerCamelCase       :       List[str]           = val
  # read in qkv matrices
  read_in_q_k_v(_lowerCAmelCase       ,_lowerCAmelCase       )

  # load HuggingFace model
  __lowerCamelCase       :       Tuple           = DPTForSemanticSegmentation(_lowerCAmelCase       ) if 'ade' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase       )
  model.load_state_dict(_lowerCAmelCase       )
  model.eval()

  # Check outputs on an image
  __lowerCamelCase       :       Dict           = 480 if 'ade' in checkpoint_url else 384
  __lowerCamelCase       :       Dict           = DPTImageProcessor(size=_lowerCAmelCase       )

  __lowerCamelCase       :       Optional[int]           = prepare_img()
  __lowerCamelCase       :       Optional[int]           = image_processor(_lowerCAmelCase       ,return_tensors='pt'       )

  # forward pass
  __lowerCamelCase       :       List[Any]           = model(**_lowerCAmelCase       ).logits if 'ade' in checkpoint_url else model(**_lowerCAmelCase       ).predicted_depth

  # Assert logits
  __lowerCamelCase       :       Optional[int]           = 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:
    __lowerCamelCase       :       List[str]           = 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__":
   _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)






