






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     :							List[str]    =logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      ):
     UpperCamelCase__      :       Optional[Any]	    =  DPTConfig()

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

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

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

     return config, expected_shape







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      ):
     UpperCamelCase__      :       Optional[int]	    =  ["pretrained.model.head.weight", "pretrained.model.head.bias"]
     for k in ignore_keys:
          state_dict.pop(snake_case_ ,   snake_case_      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      ):




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

     return name







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      ):




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







def 		SCREAMING_SNAKE_CASE							( ):
     UpperCamelCase__      :       List[Any]	    =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCamelCase__      :       Union[str, Any]	    =  Image.open(requests.get(snake_case_ ,   stream=snake_case_      ).raw      )
     return im







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      ):
     UpperCamelCase__    ,					UpperCamelCase__      :       Optional[Any]	    =  get_dpt_config(snake_case_      )
     # load original state_dict from URL
     UpperCamelCase__      :       Tuple	    =  torch.hub.load_state_dict_from_url(snake_case_ ,   map_location="cpu"      )
     # remove certain keys
     remove_ignore_keys_(snake_case_      )
     # rename keys
     for key in state_dict.copy().keys():
          UpperCamelCase__      :       Optional[int]	    =  state_dict.pop(snake_case_      )
          UpperCamelCase__      :       List[Any]	    =  val
     # read in qkv matrices
     read_in_q_k_v(snake_case_ ,   snake_case_      )

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

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

     UpperCamelCase__      :       Tuple	    =  prepare_img()
     UpperCamelCase__      :       Dict	    =  image_processor(snake_case_ ,   return_tensors="pt"      )

     # forward pass
     UpperCamelCase__      :       str	    =  model(**snake_case_      ).logits if "ade" in checkpoint_url else model(**snake_case_      ).predicted_depth

     # Assert logits
     UpperCamelCase__      :       Union[str, Any]	    =  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:
          UpperCamelCase__      :       Union[str, Any]	    =  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(snake_case_      )
     assert (
         torch.allclose(outputs[0, 0, :3, :3] ,   snake_case_ ,   atol=1E-4      )
         if "ade" in checkpoint_url
         else torch.allclose(outputs[0, :3, :3] ,   snake_case_      )
     )

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

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


if __name__ == "__main__":
 lowerCamelCase     :							List[str]    =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     :							Union[str, Any]    =parser.parse_args()
 convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor





class       __a  (   unittest.TestCase	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       List[Any]=7							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Dict=18							,       SCREAMING_SNAKE_CASE :       str=30							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=4_00							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Any=32							,       SCREAMING_SNAKE_CASE :       int=True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  parent
      UpperCamelCase__      :       Dict	    =  batch_size
      UpperCamelCase__      :       Optional[Any]	    =  num_channels
      UpperCamelCase__      :       int	    =  image_size
      UpperCamelCase__      :       Dict	    =  min_resolution
      UpperCamelCase__      :       List[str]	    =  max_resolution
      UpperCamelCase__      :       Union[str, Any]	    =  do_resize
      UpperCamelCase__      :       Optional[Any]	    =  size_divisor
      UpperCamelCase__      :       int	    =  do_rescale







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }







@require_torch
@require_vision
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	List[Any]				=			GLPNImageProcessor if is_vision_available() else None







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  GLPNImageProcessingTester(self						)







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size_divisor"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "resample"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_rescale"						)						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       Image.Image						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       List[Any]	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__      :       Optional[Any]	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       np.ndarray						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       str	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       torch.Tensor						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       Dict	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)