




'''simple docstring'''



def    lowercase__(       __UpperCamelCase: List[Any]	,__UpperCamelCase: Tuple  ):

      """simple docstring"""






      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2




def    lowercase__(       __UpperCamelCase: str	,__UpperCamelCase: Any=0  ):

      """simple docstring"""






      return sorted(__lowerCAmelCase	,key=lambda __UpperCamelCase  : x[column]  )




def    lowercase__(       __UpperCamelCase: Dict	,__UpperCamelCase: Tuple	,__UpperCamelCase: Optional[int]=float('inf'  )  ):

      """simple docstring"""






      for i in range(points_counts - 1  ):
            for j in range(i + 1	,__lowerCAmelCase  ):
                  SCREAMING_SNAKE_CASE					:	List[str]   =		euclidean_distance_sqr(points[i]	,points[j]  )
                  if current_dis < min_dis:
                        SCREAMING_SNAKE_CASE					:	Any   =		current_dis
      return min_dis




def    lowercase__(       __UpperCamelCase: Any	,__UpperCamelCase: str	,__UpperCamelCase: List[str]=float('inf'  )  ):

      """simple docstring"""






      for i in range(min(6	,points_counts - 1  )	,__lowerCAmelCase  ):
            for j in range(max(0	,i - 6  )	,__lowerCAmelCase  ):
                  SCREAMING_SNAKE_CASE					:	Tuple   =		euclidean_distance_sqr(points[i]	,points[j]  )
                  if current_dis < min_dis:
                        SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		current_dis
      return min_dis




def    lowercase__(       __UpperCamelCase: Dict	,__UpperCamelCase: List[str]	,__UpperCamelCase: Any  ):

      """simple docstring"""






      if points_counts <= 3:
            return dis_between_closest_pair(__lowerCAmelCase	,__lowerCAmelCase  )

      # recursion
      SCREAMING_SNAKE_CASE					:	Optional[Any]   =		points_counts // 2
      SCREAMING_SNAKE_CASE					:	str   =		closest_pair_of_points_sqr(
          __lowerCAmelCase	,points_sorted_on_y[:mid]	,__lowerCAmelCase  )
      SCREAMING_SNAKE_CASE					:	List[Any]   =		closest_pair_of_points_sqr(
          __lowerCAmelCase	,points_sorted_on_y[mid:]	,points_counts - mid  )
      SCREAMING_SNAKE_CASE					:	Dict   =		min(__lowerCAmelCase	,__lowerCAmelCase  )
      SCREAMING_SNAKE_CASE					:	Optional[int]   =		[]
      for point in points_sorted_on_x:
            if abs(point[0] - points_sorted_on_x[mid][0]  ) < closest_pair_dis:
                  cross_strip.append(__lowerCAmelCase  )

      SCREAMING_SNAKE_CASE					:	Optional[Any]   =		dis_between_closest_in_strip(
          __lowerCAmelCase	,len(__lowerCAmelCase  )	,__lowerCAmelCase  )
      return min(__lowerCAmelCase	,__lowerCAmelCase  )




def    lowercase__(       __UpperCamelCase: Optional[int]	,__UpperCamelCase: Optional[int]  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	List[str]   =		column_based_sort(__lowerCAmelCase	,column=0  )
      SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		column_based_sort(__lowerCAmelCase	,column=1  )
      return (
          closest_pair_of_points_sqr(
              __lowerCAmelCase	,__lowerCAmelCase	,__lowerCAmelCase  )
      ) ** 0.5


if __name__ == "__main__":
      UpperCamelCase_       =							[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
      print("Distance:", closest_pair_of_points(points, len(points)))






'''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()
lowerCamelCase__					=   logging.get_logger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					DPTConfig()

		if "large" in checkpoint_url:
				_UpperCAmelCase						:				List[str]	    =					1_024
				_UpperCAmelCase						:				Optional[int]	    =					4_096
				_UpperCAmelCase						:				Union[str, Any]	    =					24
				_UpperCAmelCase						:				List[Any]	    =					16
				_UpperCAmelCase						:				List[Any]	    =					[5, 11, 17, 23]
				_UpperCAmelCase						:				int	    =					[256, 512, 1_024, 1_024]
				_UpperCAmelCase						:				Optional[Any]	    =					(1, 384, 384)

		if "ade" in checkpoint_url:
				_UpperCAmelCase						:				Optional[int]	    =					True

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

		return config, expected_shape






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






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

		return name






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






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






@torch.no_grad()
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Dict	    =					get_dpt_config(__lowerCAmelCase      )
		# load original state_dict from URL
		_UpperCAmelCase						:				List[Any]	    =					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():
				_UpperCAmelCase						:				Tuple	    =					state_dict.pop(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					val
		# read in qkv matrices
		read_in_q_k_v(__lowerCAmelCase					,       __lowerCAmelCase      )

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

		# Check outputs on an image
		_UpperCAmelCase						:				Any	    =					480 if "ade" in checkpoint_url else 384
		_UpperCAmelCase						:				List[str]	    =					DPTImageProcessor(size=__lowerCAmelCase      )

		_UpperCAmelCase						:				Any	    =					prepare_img()
		_UpperCAmelCase						:				Dict	    =					image_processor(__lowerCAmelCase					,       return_tensors="pt"      )

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

		# Assert logits
		_UpperCAmelCase						:				Dict	    =					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						:				str	    =					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(__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__":
						lowerCamelCase__					=   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.',
						)

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



