


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







def     _lowercase							(  __lowerCamelCase      :       Any      )	-> int:





     '''simple docstring'''





     UpperCamelCase__				:	Union[str, Any]										=  DPTConfig(embedding_type='''hybrid'''      )

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

     if "nyu" or "midas" in checkpoint_url:
          UpperCamelCase__				:	List[str]										=  768
          UpperCamelCase__				:	Optional[int]										=  [1, 1, 1, 0.5]
          UpperCamelCase__				:	Dict										=  [256, 512, 768, 768]
          UpperCamelCase__				:	Dict										=  150
          UpperCamelCase__				:	Dict										=  16
          UpperCamelCase__				:	List[str]										=  (1, 384, 384)
          UpperCamelCase__				:	Union[str, Any]										=  False
          UpperCamelCase__				:	Dict										=  '''project'''

     if "ade" in checkpoint_url:
          UpperCamelCase__				:	List[str]										=  True
          UpperCamelCase__				:	Any										=  768
          UpperCamelCase__				:	Union[str, Any]										=  [1, 1, 1, 0.5]
          UpperCamelCase__				:	Optional[int]										=  150
          UpperCamelCase__				:	Dict										=  16
          UpperCamelCase__				:	List[str]										=  '''huggingface/label-files'''
          UpperCamelCase__				:	List[Any]										=  '''ade20k-id2label.json'''
          UpperCamelCase__				:	List[Any]										=  json.load(open(cached_download(hf_hub_url(UpperCamelCase__		,UpperCamelCase__		,repo_type='''dataset'''      )      )		,'''r'''      )      )
          UpperCamelCase__				:	Optional[Any]										=  {int(UpperCamelCase__      ): v for k, v in idalabel.items()}
          UpperCamelCase__				:	Any										=  idalabel
          UpperCamelCase__				:	Union[str, Any]										=  {v: k for k, v in idalabel.items()}
          UpperCamelCase__				:	Optional[Any]										=  [1, 150, 480, 480]

     return config, expected_shape







def     _lowercase							(  __lowerCamelCase      :       str      )	-> List[Any]:





     '''simple docstring'''





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







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

     if ".." in name:
          UpperCamelCase__				:	Dict										=  name.replace('''..'''		,'''.'''      )

     if "stem.conv" in name:
          UpperCamelCase__				:	str										=  name.replace('''stem.conv'''		,'''bit.embedder.convolution'''      )
     if "blocks" in name:
          UpperCamelCase__				:	str										=  name.replace('''blocks'''		,'''layers'''      )
     if "convolution" in name and "backbone" in name:
          UpperCamelCase__				:	int										=  name.replace('''convolution'''		,'''conv'''      )
     if "layer" in name and "backbone" in name:
          UpperCamelCase__				:	List[Any]										=  name.replace('''layer'''		,'''layers'''      )
     if "backbone.bit.encoder.bit" in name:
          UpperCamelCase__				:	int										=  name.replace('''backbone.bit.encoder.bit'''		,'''backbone.bit'''      )
     if "embedder.conv" in name:
          UpperCamelCase__				:	Optional[Any]										=  name.replace('''embedder.conv'''		,'''embedder.convolution'''      )
     if "backbone.bit.encoder.stem.norm" in name:
          UpperCamelCase__				:	List[Any]										=  name.replace('''backbone.bit.encoder.stem.norm'''		,'''backbone.bit.embedder.norm'''      )
     return name







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







def     _lowercase							(  )	-> str:





     '''simple docstring'''





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







@torch.no_grad()
def     _lowercase							(  __lowerCamelCase      :       str		,__lowerCamelCase      :       Optional[int]		,__lowerCamelCase      :       Optional[Any]		,__lowerCamelCase      :       int		,__lowerCamelCase      :       Union[str, Any]      )	-> List[Any]:





     '''simple docstring'''





     UpperCamelCase__						,UpperCamelCase__				:	Union[str, Any]										=  get_dpt_config(UpperCamelCase__      )
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     UpperCamelCase__				:	str										=  torch.load(UpperCamelCase__		,map_location='''cpu'''      )
     # remove certain keys
     remove_ignore_keys_(UpperCamelCase__      )
     # rename keys
     for key in state_dict.copy().keys():
          UpperCamelCase__				:	List[str]										=  state_dict.pop(UpperCamelCase__      )
          UpperCamelCase__				:	Union[str, Any]										=  val
     # read in qkv matrices
     read_in_q_k_v(UpperCamelCase__		,UpperCamelCase__      )

     # load HuggingFace model
     UpperCamelCase__				:	List[Any]										=  DPTForSemanticSegmentation(UpperCamelCase__      ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__      )
     model.load_state_dict(UpperCamelCase__      )
     model.eval()

     # Check outputs on an image
     UpperCamelCase__				:	Optional[int]										=  480 if '''ade''' in checkpoint_url else 384
     UpperCamelCase__				:	List[str]										=  DPTImageProcessor(size=UpperCamelCase__      )

     UpperCamelCase__				:	Optional[int]										=  prepare_img()
     UpperCamelCase__				:	Dict										=  image_processor(UpperCamelCase__		,return_tensors='''pt'''      )

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

     if show_prediction:
          UpperCamelCase__				:	List[str]										=  (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1      )		,size=(image.size[1], image.size[0])		,mode='''bicubic'''		,align_corners=UpperCamelCase__		,)
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255      ).show()

     if pytorch_dump_folder_path is not None:
          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:
          model.push_to_hub('''ybelkada/dpt-hybrid-midas'''      )
          image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''      )


if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE			:       Dict      =						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=False,
       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.""",
   )
   parser.add_argument(
       """--show_prediction""",
       action="""store_true""",
   )

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





'''simple docstring'''


from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class     A ( UpperCAmelCase ):





							def    snake_case__    (   self					:				str						)				->						Any:
								__UpperCAmelCase      					=      SMALL_MODEL_IDENTIFIER
								__UpperCAmelCase      					=      '''pt'''
								__UpperCAmelCase      					=      '''tf'''





							def    snake_case__    (   self					:				int  ,		__a					:				int						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      AutoModel.from_pretrained(self.test_model						)
								model_pt.save_pretrained(__a						)





							def    snake_case__    (   self					:				Any  ,		__a					:				Optional[int]						)				->						Tuple:
								__UpperCAmelCase      					=      TFAutoModel.from_pretrained(self.test_model  ,		from_pt=__a						)
								model_tf.save_pretrained(__a						)





							def    snake_case__    (   self					:				Dict						)				->						Dict:
								__UpperCAmelCase      					=      '''mock_framework'''

								# Framework provided - return whatever the user provides
								__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model  ,		__a						)
								self.assertEqual(__a  ,		__a						)

								# Local checkpoint and framework provided - return provided framework
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
									self._setup_pt_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a  ,		__a						)
									self.assertEqual(__a  ,		__a						)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
									self._setup_tf_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a  ,		__a						)
									self.assertEqual(__a  ,		__a						)





							def    snake_case__    (   self					:				Dict						)				->						List[str]:
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
									self._setup_pt_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
									self._setup_tf_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)
									self.assertEqual(__a  ,		self.framework_tf						)

								# Invalid local checkpoint
								with TemporaryDirectory() as local_invalid_ckpt:
									with self.assertRaises(__a						):
										__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)






							def    snake_case__    (   self					:				str						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# PyTorch not in environment -> use TensorFlow
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_torch_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_tf						)

								# Both in environment -> use PyTorch
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						), patch(
								    '''transformers.onnx.features.is_torch_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# Both not in environment -> raise error
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						), patch(
								    '''transformers.onnx.features.is_torch_available'''  ,		__a						):
									with self.assertRaises(__a						):
										__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
