





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_							=			logging.get_logger(__name__)


def 					A__	(       SCREAMING_SNAKE_CASE_		):
		lowerCamelCase   : Any								=DPTConfig(embedding_type='''hybrid'''		)

		if "large" in checkpoint_url:
				lowerCamelCase   : Union[str, Any]								=1_0_2_4
				lowerCamelCase   : int								=4_0_9_6
				lowerCamelCase   : Union[str, Any]								=2_4
				lowerCamelCase   : Tuple								=1_6
				lowerCamelCase   : Union[str, Any]								=[5, 1_1, 1_7, 2_3]
				lowerCamelCase   : Dict								=[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
				lowerCamelCase   : int								=(1, 3_8_4, 3_8_4)

		if "nyu" or "midas" in checkpoint_url:
				lowerCamelCase   : Tuple								=7_6_8
				lowerCamelCase   : Optional[int]								=[1, 1, 1, 0.5]
				lowerCamelCase   : str								=[2_5_6, 5_1_2, 7_6_8, 7_6_8]
				lowerCamelCase   : List[Any]								=1_5_0
				lowerCamelCase   : Union[str, Any]								=1_6
				lowerCamelCase   : Any								=(1, 3_8_4, 3_8_4)
				lowerCamelCase   : List[str]								=False
				lowerCamelCase   : str								='''project'''

		if "ade" in checkpoint_url:
				lowerCamelCase   : str								=True
				lowerCamelCase   : str								=7_6_8
				lowerCamelCase   : Dict								=[1, 1, 1, 0.5]
				lowerCamelCase   : List[str]								=1_5_0
				lowerCamelCase   : int								=1_6
				lowerCamelCase   : int								='''huggingface/label-files'''
				lowerCamelCase   : Tuple								='''ade20k-id2label.json'''
				lowerCamelCase   : int								=json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       repo_type='''dataset'''		)		)							,       '''r'''		)		)
				lowerCamelCase   : int								={int(SCREAMING_SNAKE_CASE_		): v for k, v in idalabel.items()}
				lowerCamelCase   : List[str]								=idalabel
				lowerCamelCase   : Tuple								={v: k for k, v in idalabel.items()}
				lowerCamelCase   : int								=[1, 1_5_0, 4_8_0, 4_8_0]

		return config, expected_shape


def 					A__	(       SCREAMING_SNAKE_CASE_		):
		lowerCamelCase   : Dict								=['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
		for k in ignore_keys:
				state_dict.pop(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)


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

		if ".." in name:
				lowerCamelCase   : str								=name.replace('''..'''							,       '''.'''		)

		if "stem.conv" in name:
				lowerCamelCase   : int								=name.replace('''stem.conv'''							,       '''bit.embedder.convolution'''		)
		if "blocks" in name:
				lowerCamelCase   : Dict								=name.replace('''blocks'''							,       '''layers'''		)
		if "convolution" in name and "backbone" in name:
				lowerCamelCase   : Union[str, Any]								=name.replace('''convolution'''							,       '''conv'''		)
		if "layer" in name and "backbone" in name:
				lowerCamelCase   : List[Any]								=name.replace('''layer'''							,       '''layers'''		)
		if "backbone.bit.encoder.bit" in name:
				lowerCamelCase   : int								=name.replace('''backbone.bit.encoder.bit'''							,       '''backbone.bit'''		)
		if "embedder.conv" in name:
				lowerCamelCase   : int								=name.replace('''embedder.conv'''							,       '''embedder.convolution'''		)
		if "backbone.bit.encoder.stem.norm" in name:
				lowerCamelCase   : int								=name.replace('''backbone.bit.encoder.stem.norm'''							,       '''backbone.bit.embedder.norm'''		)
		return name


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
		for i in range(config.num_hidden_layers		):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowerCamelCase   : List[str]								=state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"		)
				lowerCamelCase   : 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
				lowerCamelCase   : int								=in_proj_weight[: config.hidden_size, :]
				lowerCamelCase   : List[str]								=in_proj_bias[: config.hidden_size]
				lowerCamelCase   : List[str]								=in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCamelCase   : Optional[int]								=in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCamelCase   : Optional[Any]								=in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCamelCase   : List[Any]								=in_proj_bias[-config.hidden_size :]


def 					A__	(       ):
		lowerCamelCase   : Optional[Any]								='''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCamelCase   : List[Any]								=Image.open(requests.get(SCREAMING_SNAKE_CASE_							,       stream=SCREAMING_SNAKE_CASE_		).raw		)
		return im


@torch.no_grad()
def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
		lowerCamelCase   : str								=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")
		lowerCamelCase   : Optional[Any]								=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():
				lowerCamelCase   : int								=state_dict.pop(SCREAMING_SNAKE_CASE_		)
				lowerCamelCase   : Optional[int]								=val
		# read in qkv matrices
		read_in_q_k_v(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)

		# load HuggingFace model
		lowerCamelCase   : Dict								=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
		lowerCamelCase   : int								=4_8_0 if '''ade''' in checkpoint_url else 3_8_4
		lowerCamelCase   : Tuple								=DPTImageProcessor(size=SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : Tuple								=prepare_img()
		lowerCamelCase   : str								=image_processor(SCREAMING_SNAKE_CASE_							,       return_tensors='''pt'''		)

		# forward pass
		lowerCamelCase   : Any								=model(**SCREAMING_SNAKE_CASE_		).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE_		).predicted_depth

		if show_prediction:
				lowerCamelCase   : List[str]								=(
				    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()) * 2_5_5		).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__":
				snake_case_							=			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''',
				)

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








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		if len(SCREAMING_SNAKE_CASE_		) < 2:
				raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''		)
		if any(i <= 0 for i in nums		):
				raise ValueError('''All values must be greater than 0'''		)
		lowerCamelCase   : List[Any]								=nums.copy()
		copy_nums.sort()
		return copy_nums[-1] < sum(copy_nums[:-1]		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

