'''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()
_lowercase			:  List[Any]   							=	logging.get_logger(__name__)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]							):



						"""simple docstring"""





						lowercase_     :					Any													=	DPTConfig()

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

						if "ade" in checkpoint_url:
												lowercase_     :					int													=	True

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

						return config, expected_shape
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Optional[Any]													=	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
						for k in ignore_keys:
												state_dict.pop(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __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
						):
												lowercase_     :					Optional[Any]													=	name.replace('''pretrained.model'''				,				'''dpt.encoder'''							)
						if "pretrained.model" in name:
												lowercase_     :					Union[str, Any]													=	name.replace('''pretrained.model'''				,				'''dpt.embeddings'''							)
						if "patch_embed" in name:
												lowercase_     :					Dict													=	name.replace('''patch_embed'''				,				'''patch_embeddings'''							)
						if "pos_embed" in name:
												lowercase_     :					Optional[int]													=	name.replace('''pos_embed'''				,				'''position_embeddings'''							)
						if "attn.proj" in name:
												lowercase_     :					List[Any]													=	name.replace('''attn.proj'''				,				'''attention.output.dense'''							)
						if "proj" in name and "project" not in name:
												lowercase_     :					Tuple													=	name.replace('''proj'''				,				'''projection'''							)
						if "blocks" in name:
												lowercase_     :					List[Any]													=	name.replace('''blocks'''				,				'''layer'''							)
						if "mlp.fc1" in name:
												lowercase_     :					str													=	name.replace('''mlp.fc1'''				,				'''intermediate.dense'''							)
						if "mlp.fc2" in name:
												lowercase_     :					List[Any]													=	name.replace('''mlp.fc2'''				,				'''output.dense'''							)
						if "norm1" in name:
												lowercase_     :					int													=	name.replace('''norm1'''				,				'''layernorm_before'''							)
						if "norm2" in name:
												lowercase_     :					List[str]													=	name.replace('''norm2'''				,				'''layernorm_after'''							)
						if "scratch.output_conv" in name:
												lowercase_     :					Union[str, Any]													=	name.replace('''scratch.output_conv'''				,				'''head'''							)
						if "scratch" in name:
												lowercase_     :					Dict													=	name.replace('''scratch'''				,				'''neck'''							)
						if "layer1_rn" in name:
												lowercase_     :					Tuple													=	name.replace('''layer1_rn'''				,				'''convs.0'''							)
						if "layer2_rn" in name:
												lowercase_     :					Optional[int]													=	name.replace('''layer2_rn'''				,				'''convs.1'''							)
						if "layer3_rn" in name:
												lowercase_     :					List[str]													=	name.replace('''layer3_rn'''				,				'''convs.2'''							)
						if "layer4_rn" in name:
												lowercase_     :					Dict													=	name.replace('''layer4_rn'''				,				'''convs.3'''							)
						if "refinenet" in name:
												lowercase_     :					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
												lowercase_     :					Optional[int]													=	name.replace(F'''refinenet{layer_idx}'''				,				F'''fusion_stage.layers.{abs(layer_idx-4							)}'''							)
						if "out_conv" in name:
												lowercase_     :					Any													=	name.replace('''out_conv'''				,				'''projection'''							)
						if "resConfUnit1" in name:
												lowercase_     :					str													=	name.replace('''resConfUnit1'''				,				'''residual_layer1'''							)
						if "resConfUnit2" in name:
												lowercase_     :					int													=	name.replace('''resConfUnit2'''				,				'''residual_layer2'''							)
						if "conv1" in name:
												lowercase_     :					Tuple													=	name.replace('''conv1'''				,				'''convolution1'''							)
						if "conv2" in name:
												lowercase_     :					List[str]													=	name.replace('''conv2'''				,				'''convolution2'''							)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												lowercase_     :					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:
												lowercase_     :					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:
												lowercase_     :					Optional[Any]													=	name.replace('''pretrained.act_postprocess3.0.project.0'''				,				'''neck.reassemble_stage.readout_projects.2.0'''							)
						if "pretrained.act_postprocess4.0.project.0" in name:
												lowercase_     :					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:
												lowercase_     :					str													=	name.replace('''pretrained.act_postprocess1.3'''				,				'''neck.reassemble_stage.layers.0.projection'''							)
						if "pretrained.act_postprocess1.4" in name:
												lowercase_     :					Tuple													=	name.replace('''pretrained.act_postprocess1.4'''				,				'''neck.reassemble_stage.layers.0.resize'''							)
						if "pretrained.act_postprocess2.3" in name:
												lowercase_     :					str													=	name.replace('''pretrained.act_postprocess2.3'''				,				'''neck.reassemble_stage.layers.1.projection'''							)
						if "pretrained.act_postprocess2.4" in name:
												lowercase_     :					Tuple													=	name.replace('''pretrained.act_postprocess2.4'''				,				'''neck.reassemble_stage.layers.1.resize'''							)
						if "pretrained.act_postprocess3.3" in name:
												lowercase_     :					List[Any]													=	name.replace('''pretrained.act_postprocess3.3'''				,				'''neck.reassemble_stage.layers.2.projection'''							)
						if "pretrained.act_postprocess4.3" in name:
												lowercase_     :					Tuple													=	name.replace('''pretrained.act_postprocess4.3'''				,				'''neck.reassemble_stage.layers.3.projection'''							)
						if "pretrained.act_postprocess4.4" in name:
												lowercase_     :					int													=	name.replace('''pretrained.act_postprocess4.4'''				,				'''neck.reassemble_stage.layers.3.resize'''							)
						if "pretrained" in name:
												lowercase_     :					Tuple													=	name.replace('''pretrained'''				,				'''dpt'''							)
						if "bn" in name:
												lowercase_     :					List[Any]													=	name.replace('''bn'''				,				'''batch_norm'''							)
						if "head" in name:
												lowercase_     :					Tuple													=	name.replace('''head'''				,				'''head.head'''							)
						if "encoder.norm" in name:
												lowercase_     :					List[str]													=	name.replace('''encoder.norm'''				,				'''layernorm'''							)
						if "auxlayer" in name:
												lowercase_     :					List[str]													=	name.replace('''auxlayer'''				,				'''auxiliary_head.head'''							)

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



						"""simple docstring"""





						lowercase_     :					Tuple													=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase_     :					Optional[Any]													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE				,				stream=__SCREAMING_SNAKE_CASE							).raw							)
						return im
@torch.no_grad()
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





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

						# load HuggingFace model
						lowercase_     :					Optional[int]													=	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
						lowercase_     :					str													=	480 if '''ade''' in checkpoint_url else 384
						lowercase_     :					Tuple													=	DPTImageProcessor(size=__SCREAMING_SNAKE_CASE							)

						lowercase_     :					Dict													=	prepare_img()
						lowercase_     :					Union[str, Any]													=	image_processor(__SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''							)

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

						# Assert logits
						lowercase_     :					Tuple													=	torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]							)
						if "ade" in checkpoint_url:
												lowercase_     :					Union[str, Any]													=	torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]							)
						assert outputs.shape == torch.Size(__SCREAMING_SNAKE_CASE							)
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]				,				__SCREAMING_SNAKE_CASE				,				atol=1E-4							)
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]				,				__SCREAMING_SNAKE_CASE							)
						)

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


if __name__ == "__main__":
							_lowercase			:  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.",
							)

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





'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float(moles / volume							) * nfactor							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((moles * 0.0821 * temperature) / (volume)							)							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((moles * 0.0821 * temperature) / (pressure)							)							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((pressure * volume) / (0.0821 * moles)							)							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



