

"""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()
snake_case_   :   str        =       logging.get_logger(__name__)



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				DPTConfig()

					if "large" in checkpoint_url:
										UpperCAmelCase			:   List[str]      =				10_24
										UpperCAmelCase			:   Dict      =				40_96
										UpperCAmelCase			:   Union[str, Any]      =				24
										UpperCAmelCase			:   List[str]      =				16
										UpperCAmelCase			:   List[str]      =				[5, 11, 17, 23]
										UpperCAmelCase			:   Any      =				[2_56, 5_12, 10_24, 10_24]
										UpperCAmelCase			:   Tuple      =				(1, 3_84, 3_84)

					if "ade" in checkpoint_url:
										UpperCAmelCase			:   Optional[Any]      =				True

										UpperCAmelCase			:   str      =				1_50
										UpperCAmelCase			:   Optional[int]      =				"huggingface/label-files"
										UpperCAmelCase			:   Any      =				"ade20k-id2label.json"
										UpperCAmelCase			:   Optional[Any]      =				json.load(open(cached_download(hf_hub_url(_lowercase							,      _lowercase							,      repo_type="dataset"	)	)							,      "r"	)	)
										UpperCAmelCase			:   Any      =				{int(_lowercase	): v for k, v in idalabel.items()}
										UpperCAmelCase			:   Tuple      =				idalabel
										UpperCAmelCase			:   int      =				{v: k for k, v in idalabel.items()}
										UpperCAmelCase			:   List[str]      =				[1, 1_50, 4_80, 4_80]

					return config, expected_shape



def   lowercase_	(    _lowercase       :		Optional[Any]	):




					'''simple docstring'''






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



def   lowercase_	(    _lowercase       :		int	):




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

					return name



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		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)
										UpperCAmelCase			:   Union[str, 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			:   int      =				in_proj_weight[: config.hidden_size, :]
										UpperCAmelCase			:   Any      =				in_proj_bias[: config.hidden_size]
										UpperCAmelCase			:   Optional[int]      =				in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										UpperCAmelCase			:   Dict      =				in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										UpperCAmelCase			:   Any      =				in_proj_weight[
										    -config.hidden_size :, :
										]
										UpperCAmelCase			:   str      =				in_proj_bias[-config.hidden_size :]



def   lowercase_	(    ):




					'''simple docstring'''






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



@torch.no_grad()
def   lowercase_	(    _lowercase       :		Optional[Any]							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		int							,      _lowercase       :		Tuple	):




					'''simple docstring'''






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

					# load HuggingFace model
					UpperCAmelCase			:   Any      =				DPTForSemanticSegmentation(_lowercase	) if "ade" in checkpoint_url else DPTForDepthEstimation(_lowercase	)
					model.load_state_dict(_lowercase	)
					model.eval()

					# Check outputs on an image
					UpperCAmelCase			:   Optional[int]      =				4_80 if "ade" in checkpoint_url else 3_84
					UpperCAmelCase			:   Optional[Any]      =				DPTImageProcessor(size=_lowercase	)

					UpperCAmelCase			:   Optional[int]      =				prepare_img()
					UpperCAmelCase			:   Optional[Any]      =				image_processor(_lowercase							,      return_tensors="pt"	)

					# forward pass
					UpperCAmelCase			:   Tuple      =				model(**_lowercase	).logits if "ade" in checkpoint_url else model(**_lowercase	).predicted_depth

					# Assert logits
					UpperCAmelCase			:   Optional[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			:   int      =				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(_lowercase	)
					assert (
					    torch.allclose(outputs[0, 0, :3, :3]							,      _lowercase							,      atol=1E-4	)
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3]							,      _lowercase	)
					)

					Path(_lowercase	).mkdir(exist_ok=_lowercase	)
					print(F"""Saving model to {pytorch_dump_folder_path}"""	)
					model.save_pretrained(_lowercase	)
					print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
					image_processor.save_pretrained(_lowercase	)

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


if __name__ == "__main__":
	snake_case_   :   Optional[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.""",
	)

	snake_case_   :   Tuple        =       parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





"""simple docstring"""






def   lowercase_	(    _lowercase       :		list							,      _lowercase       :		int							,      _lowercase       :		int = 0							,      _lowercase       :		int = 0	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				right or len(_lowercase	) - 1
					if left > right:
										return -1
					elif list_data[left] == key:
										return left
					elif list_data[right] == key:
										return right
					else:
										return search(_lowercase							,      _lowercase							,      left + 1							,      right - 1	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


