



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				if not isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ):
								raise TypeError("""only integers accepted as input"""    )
				else:
								__a       =		str(abs(_SCREAMING_SNAKE_CASE    )    )
								__a       =		[list(_SCREAMING_SNAKE_CASE    ) for char in range(len(_SCREAMING_SNAKE_CASE    )    )]
								for index in range(len(_SCREAMING_SNAKE_CASE    )    ):
												num_transpositions[index].pop(_SCREAMING_SNAKE_CASE    )
								return max(
								    int("""""".join(list(_SCREAMING_SNAKE_CASE    )    )    ) for transposition in num_transpositions    )


if __name__ == "__main__":
						__import__("""doctest""").testmod()





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__			= logging.get_logger(__name__)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







				__a       =		DPTConfig(embedding_type="""hybrid"""    )

				if "large" in checkpoint_url:
								__a       =		1024
								__a       =		4096
								__a       =		24
								__a       =		16
								__a       =		[5, 11, 17, 23]
								__a       =		[256, 512, 1024, 1024]
								__a       =		(1, 384, 384)

				if "nyu" or "midas" in checkpoint_url:
								__a       =		768
								__a       =		[1, 1, 1, 0.5]
								__a       =		[256, 512, 768, 768]
								__a       =		150
								__a       =		16
								__a       =		(1, 384, 384)
								__a       =		False
								__a       =		"""project"""

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

				return config, expected_shape

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple    ):
				"""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
				):
								__a       =		name.replace("""pretrained.model"""				,     """dpt.encoder"""    )
				if "pretrained.model" in name:
								__a       =		name.replace("""pretrained.model"""				,     """dpt.embeddings"""    )
				if "patch_embed" in name:
								__a       =		name.replace("""patch_embed"""				,     """"""    )
				if "pos_embed" in name:
								__a       =		name.replace("""pos_embed"""				,     """position_embeddings"""    )
				if "attn.proj" in name:
								__a       =		name.replace("""attn.proj"""				,     """attention.output.dense"""    )
				if "proj" in name and "project" not in name:
								__a       =		name.replace("""proj"""				,     """projection"""    )
				if "blocks" in name:
								__a       =		name.replace("""blocks"""				,     """layer"""    )
				if "mlp.fc1" in name:
								__a       =		name.replace("""mlp.fc1"""				,     """intermediate.dense"""    )
				if "mlp.fc2" in name:
								__a       =		name.replace("""mlp.fc2"""				,     """output.dense"""    )
				if "norm1" in name and "backbone" not in name:
								__a       =		name.replace("""norm1"""				,     """layernorm_before"""    )
				if "norm2" in name and "backbone" not in name:
								__a       =		name.replace("""norm2"""				,     """layernorm_after"""    )
				if "scratch.output_conv" in name:
								__a       =		name.replace("""scratch.output_conv"""				,     """head"""    )
				if "scratch" in name:
								__a       =		name.replace("""scratch"""				,     """neck"""    )
				if "layer1_rn" in name:
								__a       =		name.replace("""layer1_rn"""				,     """convs.0"""    )
				if "layer2_rn" in name:
								__a       =		name.replace("""layer2_rn"""				,     """convs.1"""    )
				if "layer3_rn" in name:
								__a       =		name.replace("""layer3_rn"""				,     """convs.2"""    )
				if "layer4_rn" in name:
								__a       =		name.replace("""layer4_rn"""				,     """convs.3"""    )
				if "refinenet" in name:
								__a       =		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
								__a       =		name.replace(f"refinenet{layer_idx}"				,     f"fusion_stage.layers.{abs(layer_idx-4    )}"    )
				if "out_conv" in name:
								__a       =		name.replace("""out_conv"""				,     """projection"""    )
				if "resConfUnit1" in name:
								__a       =		name.replace("""resConfUnit1"""				,     """residual_layer1"""    )
				if "resConfUnit2" in name:
								__a       =		name.replace("""resConfUnit2"""				,     """residual_layer2"""    )
				if "conv1" in name:
								__a       =		name.replace("""conv1"""				,     """convolution1"""    )
				if "conv2" in name:
								__a       =		name.replace("""conv2"""				,     """convolution2"""    )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								__a       =		name.replace("""pretrained.act_postprocess1.0.project.0"""				,     """neck.reassemble_stage.readout_projects.0.0"""    )
				if "pretrained.act_postprocess2.0.project.0" in name:
								__a       =		name.replace("""pretrained.act_postprocess2.0.project.0"""				,     """neck.reassemble_stage.readout_projects.1.0"""    )
				if "pretrained.act_postprocess3.0.project.0" in name:
								__a       =		name.replace("""pretrained.act_postprocess3.0.project.0"""				,     """neck.reassemble_stage.readout_projects.2.0"""    )
				if "pretrained.act_postprocess4.0.project.0" in name:
								__a       =		name.replace("""pretrained.act_postprocess4.0.project.0"""				,     """neck.reassemble_stage.readout_projects.3.0"""    )

				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								__a       =		name.replace("""pretrained.act_postprocess1.3"""				,     """neck.reassemble_stage.layers.0.projection"""    )
				if "pretrained.act_postprocess1.4" in name:
								__a       =		name.replace("""pretrained.act_postprocess1.4"""				,     """neck.reassemble_stage.layers.0.resize"""    )
				if "pretrained.act_postprocess2.3" in name:
								__a       =		name.replace("""pretrained.act_postprocess2.3"""				,     """neck.reassemble_stage.layers.1.projection"""    )
				if "pretrained.act_postprocess2.4" in name:
								__a       =		name.replace("""pretrained.act_postprocess2.4"""				,     """neck.reassemble_stage.layers.1.resize"""    )
				if "pretrained.act_postprocess3.3" in name:
								__a       =		name.replace("""pretrained.act_postprocess3.3"""				,     """neck.reassemble_stage.layers.2.projection"""    )
				if "pretrained.act_postprocess4.3" in name:
								__a       =		name.replace("""pretrained.act_postprocess4.3"""				,     """neck.reassemble_stage.layers.3.projection"""    )
				if "pretrained.act_postprocess4.4" in name:
								__a       =		name.replace("""pretrained.act_postprocess4.4"""				,     """neck.reassemble_stage.layers.3.resize"""    )
				if "pretrained" in name:
								__a       =		name.replace("""pretrained"""				,     """dpt"""    )
				if "bn" in name:
								__a       =		name.replace("""bn"""				,     """batch_norm"""    )
				if "head" in name:
								__a       =		name.replace("""head"""				,     """head.head"""    )
				if "encoder.norm" in name:
								__a       =		name.replace("""encoder.norm"""				,     """layernorm"""    )
				if "auxlayer" in name:
								__a       =		name.replace("""auxlayer"""				,     """auxiliary_head.head"""    )
				if "backbone" in name:
								__a       =		name.replace("""backbone"""				,     """backbone.bit.encoder"""    )

				if ".." in name:
								__a       =		name.replace(""".."""				,     """."""    )

				if "stem.conv" in name:
								__a       =		name.replace("""stem.conv"""				,     """bit.embedder.convolution"""    )
				if "blocks" in name:
								__a       =		name.replace("""blocks"""				,     """layers"""    )
				if "convolution" in name and "backbone" in name:
								__a       =		name.replace("""convolution"""				,     """conv"""    )
				if "layer" in name and "backbone" in name:
								__a       =		name.replace("""layer"""				,     """layers"""    )
				if "backbone.bit.encoder.bit" in name:
								__a       =		name.replace("""backbone.bit.encoder.bit"""				,     """backbone.bit"""    )
				if "embedder.conv" in name:
								__a       =		name.replace("""embedder.conv"""				,     """embedder.convolution"""    )
				if "backbone.bit.encoder.stem.norm" in name:
								__a       =		name.replace("""backbone.bit.encoder.stem.norm"""				,     """backbone.bit.embedder.norm"""    )
				return name

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			Optional[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)
								__a       =		state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"    )
								__a       =		state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"    )
								# next, add query, keys and values (in that order) to the state dict
								__a       =		in_proj_weight[: config.hidden_size, :]
								__a       =		in_proj_bias[: config.hidden_size]
								__a       =		in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__a       =		in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__a       =		in_proj_weight[
								    -config.hidden_size :, :
								]
								__a       =		in_proj_bias[-config.hidden_size :]

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__a       =		Image.open(requests.get(_SCREAMING_SNAKE_CASE				,     stream=_SCREAMING_SNAKE_CASE    ).raw    )
				return im

@torch.no_grad()
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a     ,					__a       =		get_dpt_config(_SCREAMING_SNAKE_CASE    )
				# load original state_dict from URL
				# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
				__a       =		torch.load(_SCREAMING_SNAKE_CASE				,     map_location="""cpu"""    )
				# remove certain keys
				remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
				# rename keys
				for key in state_dict.copy().keys():
								__a       =		state_dict.pop(_SCREAMING_SNAKE_CASE    )
								__a       =		val
				# read in qkv matrices
				read_in_q_k_v(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# load HuggingFace model
				__a       =		DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE    ) if """ade""" in checkpoint_url else DPTForDepthEstimation(_SCREAMING_SNAKE_CASE    )
				model.load_state_dict(_SCREAMING_SNAKE_CASE    )
				model.eval()

				# Check outputs on an image
				__a       =		480 if """ade""" in checkpoint_url else 384
				__a       =		DPTImageProcessor(size=_SCREAMING_SNAKE_CASE    )

				__a       =		prepare_img()
				__a       =		image_processor(_SCREAMING_SNAKE_CASE				,     return_tensors="""pt"""    )

				# forward pass
				__a       =		model(**_SCREAMING_SNAKE_CASE    ).logits if """ade""" in checkpoint_url else model(**_SCREAMING_SNAKE_CASE    ).predicted_depth

				if show_prediction:
								__a       =		(
								    torch.nn.functional.interpolate(
								        outputs.unsqueeze(1    )				,     size=(image.size[1], image.size[0])				,     mode="""bicubic"""				,     align_corners=_SCREAMING_SNAKE_CASE				,     )
								    .squeeze()
								    .cpu()
								    .numpy()
								)

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

				if pytorch_dump_folder_path is not None:
								Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
								print(f"Saving model to {pytorch_dump_folder_path}"    )
								model.save_pretrained(_SCREAMING_SNAKE_CASE    )
								print(f"Saving image processor to {pytorch_dump_folder_path}"    )
								image_processor.save_pretrained(_SCREAMING_SNAKE_CASE    )

				if push_to_hub:
								model.push_to_hub("""ybelkada/dpt-hybrid-midas"""    )
								image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""    )


if __name__ == "__main__":
						lowerCamelCase__			= 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""",
						)

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