'''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										=					logging.get_logger(__name__)






def    __UpperCAmelCase			(  a_:       List[str]							):
   _UpperCAmelCase							:	int							=   DPTConfig()

   if "large" in checkpoint_url:
      _UpperCAmelCase							:	List[Any]							=   1_024
      _UpperCAmelCase							:	Dict							=   4_096
      _UpperCAmelCase							:	List[str]							=   24
      _UpperCAmelCase							:	List[Any]							=   16
      _UpperCAmelCase							:	Optional[int]							=   [5, 11, 17, 23]
      _UpperCAmelCase							:	List[str]							=   [256, 512, 1_024, 1_024]
      _UpperCAmelCase							:	Optional[Any]							=   (1, 384, 384)

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

      _UpperCAmelCase							:	Tuple							=   150
      _UpperCAmelCase							:	Dict							=   """huggingface/label-files"""
      _UpperCAmelCase							:	Union[str, Any]							=   """ade20k-id2label.json"""
      _UpperCAmelCase							:	Dict							=   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							:	Optional[int]							=   idalabel
      _UpperCAmelCase							:	Dict							=   {v: k for k, v in idalabel.items()}
      _UpperCAmelCase							:	Optional[int]							=   [1, 150, 480, 480]

   return config, expected_shape






def    __UpperCAmelCase			(  a_:       Union[str, Any]							):
   _UpperCAmelCase							:	Tuple							=   ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
   for k in ignore_keys:
      state_dict.pop(_lowerCamelCase,					_lowerCamelCase							)






def    __UpperCAmelCase			(  a_:       List[str]							):
   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							:	int							=   name.replace("pretrained.model",					"dpt.embeddings"							)
   if "patch_embed" in name:
      _UpperCAmelCase							:	Union[str, Any]							=   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							:	Dict							=   name.replace("mlp.fc1",					"intermediate.dense"							)
   if "mlp.fc2" in name:
      _UpperCAmelCase							:	Optional[Any]							=   name.replace("mlp.fc2",					"output.dense"							)
   if "norm1" in name:
      _UpperCAmelCase							:	Dict							=   name.replace("norm1",					"layernorm_before"							)
   if "norm2" in name:
      _UpperCAmelCase							:	Tuple							=   name.replace("norm2",					"layernorm_after"							)
   if "scratch.output_conv" in name:
      _UpperCAmelCase							:	int							=   name.replace("scratch.output_conv",					"head"							)
   if "scratch" in name:
      _UpperCAmelCase							:	Union[str, Any]							=   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							:	Optional[int]							=   name.replace("layer2_rn",					"convs.1"							)
   if "layer3_rn" in name:
      _UpperCAmelCase							:	Optional[int]							=   name.replace("layer3_rn",					"convs.2"							)
   if "layer4_rn" in name:
      _UpperCAmelCase							:	Union[str, Any]							=   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							:	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							:	int							=   name.replace("resConfUnit1",					"residual_layer1"							)
   if "resConfUnit2" in name:
      _UpperCAmelCase							:	List[Any]							=   name.replace("resConfUnit2",					"residual_layer2"							)
   if "conv1" in name:
      _UpperCAmelCase							:	str							=   name.replace("conv1",					"convolution1"							)
   if "conv2" in name:
      _UpperCAmelCase							:	str							=   name.replace("conv2",					"convolution2"							)
   # readout blocks
   if "pretrained.act_postprocess1.0.project.0" in name:
      _UpperCAmelCase							:	Dict							=   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[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							:	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							:	int							=   name.replace("pretrained.act_postprocess1.4",					"neck.reassemble_stage.layers.0.resize"							)
   if "pretrained.act_postprocess2.3" in name:
      _UpperCAmelCase							:	Optional[int]							=   name.replace("pretrained.act_postprocess2.3",					"neck.reassemble_stage.layers.1.projection"							)
   if "pretrained.act_postprocess2.4" in name:
      _UpperCAmelCase							:	Union[str, Any]							=   name.replace("pretrained.act_postprocess2.4",					"neck.reassemble_stage.layers.1.resize"							)
   if "pretrained.act_postprocess3.3" in name:
      _UpperCAmelCase							:	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							:	Union[str, Any]							=   name.replace("pretrained.act_postprocess4.4",					"neck.reassemble_stage.layers.3.resize"							)
   if "pretrained" in name:
      _UpperCAmelCase							:	List[Any]							=   name.replace("pretrained",					"dpt"							)
   if "bn" in name:
      _UpperCAmelCase							:	Optional[int]							=   name.replace("bn",					"batch_norm"							)
   if "head" in name:
      _UpperCAmelCase							:	Union[str, Any]							=   name.replace("head",					"head.head"							)
   if "encoder.norm" in name:
      _UpperCAmelCase							:	Tuple							=   name.replace("encoder.norm",					"layernorm"							)
   if "auxlayer" in name:
      _UpperCAmelCase							:	Any							=   name.replace("auxlayer",					"auxiliary_head.head"							)

   return name






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






def    __UpperCAmelCase			(  ):
   _UpperCAmelCase							:	Optional[Any]							=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   _UpperCAmelCase							:	Any							=   Image.open(requests.get(_lowerCamelCase,					stream=_lowerCamelCase							).raw							)
   return im






@torch.no_grad()
def    __UpperCAmelCase			(  a_:       Dict,					a_:       List[Any],					a_:       int,					a_:       List[Any]							):
   _UpperCAmelCase							:	Tuple							=   get_dpt_config(_lowerCamelCase							)
   # load original state_dict from URL
   _UpperCAmelCase							:	Dict							=   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							:	Dict							=   state_dict.pop(_lowerCamelCase							)
      _UpperCAmelCase							:	Union[str, Any]							=   val
   # read in qkv matrices
   read_in_q_k_v(_lowerCamelCase,					_lowerCamelCase							)

   # load HuggingFace model
   _UpperCAmelCase							:	Dict							=   DPTForSemanticSegmentation(_lowerCamelCase							) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase							)
   model.load_state_dict(_lowerCamelCase							)
   model.eval()

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

   _UpperCAmelCase							:	int							=   prepare_img()
   _UpperCAmelCase							:	List[Any]							=   image_processor(_lowerCamelCase,					return_tensors="pt"							)

   # forward pass
   _UpperCAmelCase							:	Union[str, Any]							=   model(**_lowerCamelCase							).logits if """ade""" in checkpoint_url else model(**_lowerCamelCase							).predicted_depth

   # Assert logits
   _UpperCAmelCase							:	Union[str, Any]							=   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							:	int							=   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__":
     __a										=					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										=					parser.parse_args()
     convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list					):
		if any(not isinstance(_lowerCamelCase       ,      _lowerCamelCase					) or x < 0 for x in sequence					):
				raise TypeError("""Sequence must be list of non-negative integers"""					)
		for _ in range(len(_lowerCamelCase					)					):
				for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCamelCase       ,      sequence[1:]					)					):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]