


'''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()
lowercase				=logging.get_logger(__name__)





def lowerCamelCase__     (		__lowerCamelCase			:				Any				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =DPTConfig()

   if "large" in checkpoint_url:
      _UpperCAmelCase  :  Tuple				       =1_0_2_4
      _UpperCAmelCase  :  int				       =4_0_9_6
      _UpperCAmelCase  :  List[Any]				       =2_4
      _UpperCAmelCase  :  int				       =1_6
      _UpperCAmelCase  :  int				       =[5, 1_1, 1_7, 2_3]
      _UpperCAmelCase  :  List[Any]				       =[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
      _UpperCAmelCase  :  Any				       =(1, 3_8_4, 3_8_4)

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

      _UpperCAmelCase  :  Optional[Any]				       =1_5_0
      _UpperCAmelCase  :  Union[str, Any]				       ='huggingface/label-files'
      _UpperCAmelCase  :  Any				       ='ade20k-id2label.json'
      _UpperCAmelCase  :  int				       =json.load(open(cached_download(hf_hub_url(__lowerCamelCase			,       __lowerCamelCase			,       repo_type='dataset'				)				)			,       'r'				)				)
      _UpperCAmelCase  :  Optional[int]				       ={int(__lowerCamelCase				): v for k, v in idalabel.items()}
      _UpperCAmelCase  :  Tuple				       =idalabel
      _UpperCAmelCase  :  str				       ={v: k for k, v in idalabel.items()}
      _UpperCAmelCase  :  Dict				       =[1, 1_5_0, 4_8_0, 4_8_0]

   return config, expected_shape





def lowerCamelCase__     (		__lowerCamelCase			:				Dict				):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       =['pretrained.model.head.weight', 'pretrained.model.head.bias']
   for k in ignore_keys:
      state_dict.pop(__lowerCamelCase			,       __lowerCamelCase				)





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

   return name





def lowerCamelCase__     (		__lowerCamelCase			:				List[str]			,       __lowerCamelCase			:				Any				):







   '''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  :  Union[str, Any]				       =state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"				)
      _UpperCAmelCase  :  Dict				       =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  :  List[Any]				       =in_proj_weight[: config.hidden_size, :]
      _UpperCAmelCase  :  Optional[Any]				       =in_proj_bias[: config.hidden_size]
      _UpperCAmelCase  :  Tuple				       =in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _UpperCAmelCase  :  List[str]				       =in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      _UpperCAmelCase  :  str				       =in_proj_weight[
          -config.hidden_size :, :
      ]
      _UpperCAmelCase  :  Dict				       =in_proj_bias[-config.hidden_size :]





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _UpperCAmelCase  :  Any				       =Image.open(requests.get(__lowerCamelCase			,       stream=__lowerCamelCase				).raw				)
   return im





@torch.no_grad()
def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Any				):







   '''simple docstring'''




   _UpperCAmelCase       ,			_UpperCAmelCase  :  List[Any]				       =get_dpt_config(__lowerCamelCase				)
   # load original state_dict from URL
   _UpperCAmelCase  :  List[Any]				       =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():
      _UpperCAmelCase  :  Optional[int]				       =state_dict.pop(__lowerCamelCase				)
      _UpperCAmelCase  :  List[str]				       =val
   # read in qkv matrices
   read_in_q_k_v(__lowerCamelCase			,       __lowerCamelCase				)

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

   # Check outputs on an image
   _UpperCAmelCase  :  List[Any]				       =4_8_0 if 'ade' in checkpoint_url else 3_8_4
   _UpperCAmelCase  :  str				       =DPTImageProcessor(size=__lowerCamelCase				)

   _UpperCAmelCase  :  Dict				       =prepare_img()
   _UpperCAmelCase  :  int				       =image_processor(__lowerCamelCase			,       return_tensors='pt'				)

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

   # Assert logits
   _UpperCAmelCase  :  List[str]				       =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  :  Dict				       =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(__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__":
 lowercase				=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.',
 )

 lowercase				=parser.parse_args()
 convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["image_processor", "tokenizer"]
       UpperCAmelCase		 ="CLIPImageProcessor"
       UpperCAmelCase		 =("CLIPTokenizer", "CLIPTokenizerFast")






       def __init__(     self    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'    ,				snake_case    ,				)
             _UpperCAmelCase  :  str				       =kwargs.pop('feature_extractor')

          _UpperCAmelCase  :  List[str]				       =image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.')
          if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.')

          super().__init__(snake_case    ,				snake_case)






       def __call__(     self    ,				snake_case=None    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''

          if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.')

          if text is not None:
             _UpperCAmelCase  :  List[Any]				       =self.tokenizer(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if images is not None:
             _UpperCAmelCase  :  List[str]				       =self.image_processor(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if text is not None and images is not None:
             _UpperCAmelCase  :  Any				       =image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**snake_case)    ,				tensor_type=snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    int:




          '''simple docstring'''
          return self.tokenizer.batch_decode(*snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Optional[int]:




          '''simple docstring'''
          return self.tokenizer.decode(*snake_case    ,				**snake_case)






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.tokenizer.model_input_names
          _UpperCAmelCase  :  Any				       =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






       @property
       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,				snake_case    ,				)
          return self.image_processor_class






       @property
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				snake_case    ,				)
          return self.image_processor

