



'''simple docstring'''






def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		float:





       '''simple docstring'''


       return price * (1 + tax_rate)


if __name__ == "__main__":
   print(f"""{price_plus_tax(1_0_0, 0.25) = }""")
   print(f"""{price_plus_tax(125.50, 0.05) = }""")








'''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()
A_							: Optional[Any]						 =	logging.get_logger(__name__)




def     snake_case_					(     lowerCAmelCase_		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = DPTConfig()

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

       if "ade" in checkpoint_url:
              _UpperCAmelCase  :						Optional[int]			     = True

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

       return config, expected_shape




def     snake_case_					(     lowerCAmelCase_		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Tuple			     = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
       for k in ignore_keys:
              state_dict.pop(lowerCAmelCase_       ,   lowerCAmelCase_		)




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

       return name




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




def     snake_case_					(     )->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase  :						List[str]			     = Image.open(requests.get(lowerCAmelCase_       ,   stream=lowerCAmelCase_		).raw		)
       return im




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


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

       # load HuggingFace model
       _UpperCAmelCase  :						Optional[int]			     = DPTForSemanticSegmentation(lowerCAmelCase_		) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase_		)
       model.load_state_dict(lowerCAmelCase_		)
       model.eval()

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

       _UpperCAmelCase  :						Optional[Any]			     = prepare_img()
       _UpperCAmelCase  :						Dict			     = image_processor(lowerCAmelCase_       ,   return_tensors="""pt"""		)

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

       # Assert logits
       _UpperCAmelCase  :						Optional[int]			     = 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  :						str			     = 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(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__":
   A_							: 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.""",
   )

   A_							: List[Any]						 =	parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



