



'''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()
UpperCamelCase__       :	List[Any]    = logging.get_logger(__name__)


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]					):
		__SCREAMING_SNAKE_CASE       :		int           =		DPTConfig()

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

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

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

		return config, expected_shape


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


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

		return name


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


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


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

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

		# Check outputs on an image
		__SCREAMING_SNAKE_CASE       :		str           =		4_80 if """ade""" in checkpoint_url else 3_84
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		DPTImageProcessor(size=_lowerCamelCase					)

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

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

		# Assert logits
		__SCREAMING_SNAKE_CASE       :		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:
				__SCREAMING_SNAKE_CASE       :		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__":
						UpperCamelCase__       :	int    = 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.''',
						)

						UpperCamelCase__       :	int    = 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]