




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()
lowerCamelCase__			:       Union[str, Any]         =							logging.get_logger(__name__)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]							) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      DPTConfig()

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

      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE_     						=      True

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

      return config, expected_shape

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]							) ->			Dict:
      SCREAMING_SNAKE_CASE_     						=      ['pretrained.model.head.weight', 'pretrained.model.head.bias']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase      ,      __UpperCAmelCase							)

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

      return name

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     int							) ->			Optional[int]:
      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_     						=      state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"							)
            SCREAMING_SNAKE_CASE_     						=      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_     						=      in_proj_weight[: config.hidden_size, :]
            SCREAMING_SNAKE_CASE_     						=      in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE_     						=      in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_     						=      in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE_     						=      in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE_     						=      in_proj_bias[-config.hidden_size :]

def  UpperCAmelCase_							(				) ->			int:
      SCREAMING_SNAKE_CASE_     						=      'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_     						=      Image.open(requests.get(__UpperCAmelCase      ,      stream=__UpperCAmelCase							).raw							)
      return im

@torch.no_grad()
def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     Dict							) ->			Optional[int]:
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      get_dpt_config(__UpperCAmelCase							)
      # load original state_dict from URL
      SCREAMING_SNAKE_CASE_     						=      torch.hub.load_state_dict_from_url(__UpperCAmelCase      ,      map_location='cpu'							)
      # remove certain keys
      remove_ignore_keys_(__UpperCAmelCase							)
      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_     						=      state_dict.pop(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_     						=      val
      # read in qkv matrices
      read_in_q_k_v(__UpperCAmelCase      ,      __UpperCAmelCase							)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_     						=      DPTForSemanticSegmentation(__UpperCAmelCase							) if 'ade' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase							)
      model.load_state_dict(__UpperCAmelCase							)
      model.eval()

      # Check outputs on an image
      SCREAMING_SNAKE_CASE_     						=      4_80 if 'ade' in checkpoint_url else 3_84
      SCREAMING_SNAKE_CASE_     						=      DPTImageProcessor(size=__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_     						=      prepare_img()
      SCREAMING_SNAKE_CASE_     						=      image_processor(__UpperCAmelCase      ,      return_tensors='pt'							)

      # forward pass
      SCREAMING_SNAKE_CASE_     						=      model(**__UpperCAmelCase							).logits if 'ade' in checkpoint_url else model(**__UpperCAmelCase							).predicted_depth

      # Assert logits
      SCREAMING_SNAKE_CASE_     						=      torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]							)
      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE_     						=      torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]							)
      assert outputs.shape == torch.Size(__UpperCAmelCase							)
      assert (
          torch.allclose(outputs[0, 0, :3, :3]      ,      __UpperCAmelCase      ,      atol=1E-4							)
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3]      ,      __UpperCAmelCase							)
      )

      Path(__UpperCAmelCase							).mkdir(exist_ok=__UpperCAmelCase							)
      print(f"Saving model to {pytorch_dump_folder_path}"							)
      model.save_pretrained(__UpperCAmelCase							)
      print(f"Saving image processor to {pytorch_dump_folder_path}"							)
      image_processor.save_pretrained(__UpperCAmelCase							)

      if push_to_hub:
            print('Pushing model to hub...'							)
            model.push_to_hub(
                repo_path_or_name=Path(__UpperCAmelCase      ,      __UpperCAmelCase							)      ,      organization='nielsr'      ,      commit_message='Add model'      ,      use_temp_dir=__UpperCAmelCase      ,      )
            image_processor.push_to_hub(
                repo_path_or_name=Path(__UpperCAmelCase      ,      __UpperCAmelCase							)      ,      organization='nielsr'      ,      commit_message='Add image processor'      ,      use_temp_dir=__UpperCAmelCase      ,      )


if __name__ == "__main__":
   lowerCamelCase__			:       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=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.',
   )

   lowerCamelCase__			:       Optional[int]         =							parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowerCamelCase__			:       Dict         =							logging.get_logger(__name__)





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    *_lowerCAmelCase  :		List[str]    ,    **_lowerCAmelCase  :		Optional[int]						):
       warnings.warn(
           'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use LayoutLMv2ImageProcessor instead.'    ,    _lowerCAmelCase    ,    )
       super().__init__(*_lowerCAmelCase    ,    **_lowerCAmelCase						)