


'''simple docstring'''





from math import ceil






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 1_001	):





			"""simple docstring"""




			UpperCamelCase         =					1

			for i in range(1       ,  int(ceil(n / 2.0	)	)	):
						UpperCamelCase         =					2 * i + 1
						UpperCamelCase         =					2 * i
						UpperCamelCase         =					total + 4 * odd**2 - 6 * even

			return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									__snake_case            =   int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print("Invalid entry - please enter a number")








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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					DPTConfig()

			if "large" in checkpoint_url:
						UpperCamelCase         =					1_024
						UpperCamelCase         =					4_096
						UpperCamelCase         =					24
						UpperCamelCase         =					16
						UpperCamelCase         =					[5, 11, 17, 23]
						UpperCamelCase         =					[256, 512, 1_024, 1_024]
						UpperCamelCase         =					(1, 384, 384)

			if "ade" in checkpoint_url:
						UpperCamelCase         =					True

						UpperCamelCase         =					150
						UpperCamelCase         =					"""huggingface/label-files"""
						UpperCamelCase         =					"""ade20k-id2label.json"""
						UpperCamelCase         =					json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""	)	)       ,  """r"""	)	)
						UpperCamelCase         =					{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
						UpperCamelCase         =					idalabel
						UpperCamelCase         =					{v: k for k, v in idalabel.items()}
						UpperCamelCase         =					[1, 150, 480, 480]

			return config, expected_shape






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





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

			return name






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""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         =					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'	)
						UpperCamelCase         =					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         =					in_proj_weight[: config.hidden_size, :]
						UpperCamelCase         =					in_proj_bias[: config.hidden_size]
						UpperCamelCase         =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase         =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase         =					in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase         =					in_proj_bias[-config.hidden_size :]






def 							_lowercase    (		):





			"""simple docstring"""




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






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			UpperCamelCase  ,  UpperCamelCase         =					get_dpt_config(SCREAMING_SNAKE_CASE_	)
			# load original state_dict from URL
			UpperCamelCase         =					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():
						UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					val
			# read in qkv matrices
			read_in_q_k_v(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# load HuggingFace model
			UpperCamelCase         =					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
			UpperCamelCase         =					480 if """ade""" in checkpoint_url else 384
			UpperCamelCase         =					DPTImageProcessor(size=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					prepare_img()
			UpperCamelCase         =					image_processor(SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""	)

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

			# Assert logits
			UpperCamelCase         =					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:
						UpperCamelCase         =					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__":
			__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=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            =   parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




