






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      snake_case		(							UpperCAmelCase	:       str   ):
				A	 =      DPTConfig()

				if "large" in checkpoint_url:
								A	 =      10_24
								A	 =      40_96
								A	 =      24
								A	 =      16
								A	 =      [5, 11, 17, 23]
								A	 =      [2_56, 5_12, 10_24, 10_24]
								A	 =      (1, 3_84, 3_84)

				if "ade" in checkpoint_url:
								A	 =      True

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

				return config, expected_shape







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				A	 =      ['pretrained.model.head.weight', 'pretrained.model.head.bias']
				for k in ignore_keys:
								state_dict.pop(UpperCAmelCase,						UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       int   ):
				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								A	 =      name.replace('pretrained.model',						'dpt.encoder'   )
				if "pretrained.model" in name:
								A	 =      name.replace('pretrained.model',						'dpt.embeddings'   )
				if "patch_embed" in name:
								A	 =      name.replace('patch_embed',						'patch_embeddings'   )
				if "pos_embed" in name:
								A	 =      name.replace('pos_embed',						'position_embeddings'   )
				if "attn.proj" in name:
								A	 =      name.replace('attn.proj',						'attention.output.dense'   )
				if "proj" in name and "project" not in name:
								A	 =      name.replace('proj',						'projection'   )
				if "blocks" in name:
								A	 =      name.replace('blocks',						'layer'   )
				if "mlp.fc1" in name:
								A	 =      name.replace('mlp.fc1',						'intermediate.dense'   )
				if "mlp.fc2" in name:
								A	 =      name.replace('mlp.fc2',						'output.dense'   )
				if "norm1" in name:
								A	 =      name.replace('norm1',						'layernorm_before'   )
				if "norm2" in name:
								A	 =      name.replace('norm2',						'layernorm_after'   )
				if "scratch.output_conv" in name:
								A	 =      name.replace('scratch.output_conv',						'head'   )
				if "scratch" in name:
								A	 =      name.replace('scratch',						'neck'   )
				if "layer1_rn" in name:
								A	 =      name.replace('layer1_rn',						'convs.0'   )
				if "layer2_rn" in name:
								A	 =      name.replace('layer2_rn',						'convs.1'   )
				if "layer3_rn" in name:
								A	 =      name.replace('layer3_rn',						'convs.2'   )
				if "layer4_rn" in name:
								A	 =      name.replace('layer4_rn',						'convs.3'   )
				if "refinenet" in name:
								A	 =      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
								A	 =      name.replace(f'refinenet{layer_idx}',						f'fusion_stage.layers.{abs(layer_idx-4   )}'   )
				if "out_conv" in name:
								A	 =      name.replace('out_conv',						'projection'   )
				if "resConfUnit1" in name:
								A	 =      name.replace('resConfUnit1',						'residual_layer1'   )
				if "resConfUnit2" in name:
								A	 =      name.replace('resConfUnit2',						'residual_layer2'   )
				if "conv1" in name:
								A	 =      name.replace('conv1',						'convolution1'   )
				if "conv2" in name:
								A	 =      name.replace('conv2',						'convolution2'   )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								A	 =      name.replace('pretrained.act_postprocess1.0.project.0',						'neck.reassemble_stage.readout_projects.0.0'   )
				if "pretrained.act_postprocess2.0.project.0" in name:
								A	 =      name.replace('pretrained.act_postprocess2.0.project.0',						'neck.reassemble_stage.readout_projects.1.0'   )
				if "pretrained.act_postprocess3.0.project.0" in name:
								A	 =      name.replace('pretrained.act_postprocess3.0.project.0',						'neck.reassemble_stage.readout_projects.2.0'   )
				if "pretrained.act_postprocess4.0.project.0" in name:
								A	 =      name.replace('pretrained.act_postprocess4.0.project.0',						'neck.reassemble_stage.readout_projects.3.0'   )
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								A	 =      name.replace('pretrained.act_postprocess1.3',						'neck.reassemble_stage.layers.0.projection'   )
				if "pretrained.act_postprocess1.4" in name:
								A	 =      name.replace('pretrained.act_postprocess1.4',						'neck.reassemble_stage.layers.0.resize'   )
				if "pretrained.act_postprocess2.3" in name:
								A	 =      name.replace('pretrained.act_postprocess2.3',						'neck.reassemble_stage.layers.1.projection'   )
				if "pretrained.act_postprocess2.4" in name:
								A	 =      name.replace('pretrained.act_postprocess2.4',						'neck.reassemble_stage.layers.1.resize'   )
				if "pretrained.act_postprocess3.3" in name:
								A	 =      name.replace('pretrained.act_postprocess3.3',						'neck.reassemble_stage.layers.2.projection'   )
				if "pretrained.act_postprocess4.3" in name:
								A	 =      name.replace('pretrained.act_postprocess4.3',						'neck.reassemble_stage.layers.3.projection'   )
				if "pretrained.act_postprocess4.4" in name:
								A	 =      name.replace('pretrained.act_postprocess4.4',						'neck.reassemble_stage.layers.3.resize'   )
				if "pretrained" in name:
								A	 =      name.replace('pretrained',						'dpt'   )
				if "bn" in name:
								A	 =      name.replace('bn',						'batch_norm'   )
				if "head" in name:
								A	 =      name.replace('head',						'head.head'   )
				if "encoder.norm" in name:
								A	 =      name.replace('encoder.norm',						'layernorm'   )
				if "auxlayer" in name:
								A	 =      name.replace('auxlayer',						'auxiliary_head.head'   )

				return name







def      snake_case		(							UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       str   ):
				for i in range(config.num_hidden_layers   ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								A	 =      state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'   )
								A	 =      state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'   )
								# next, add query, keys and values (in that order) to the state dict
								A	 =      in_proj_weight[: config.hidden_size, :]
								A	 =      in_proj_bias[: config.hidden_size]
								A	 =      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								A	 =      in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								A	 =      in_proj_weight[
								    -config.hidden_size :, :
								]
								A	 =      in_proj_bias[-config.hidden_size :]







def      snake_case		(							):
				A	 =      'http://images.cocodataset.org/val2017/000000039769.jpg'
				A	 =      Image.open(requests.get(UpperCAmelCase,						stream=UpperCAmelCase   ).raw   )
				return im







@torch.no_grad()
def      snake_case		(							UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       List[str]   ):
				A ,		A	 =      get_dpt_config(UpperCAmelCase   )
				# load original state_dict from URL
				A	 =      torch.hub.load_state_dict_from_url(UpperCAmelCase,						map_location='cpu'   )
				# remove certain keys
				remove_ignore_keys_(UpperCAmelCase   )
				# rename keys
				for key in state_dict.copy().keys():
								A	 =      state_dict.pop(UpperCAmelCase   )
								A	 =      val
				# read in qkv matrices
				read_in_q_k_v(UpperCAmelCase,						UpperCAmelCase   )

				# load HuggingFace model
				A	 =      DPTForSemanticSegmentation(UpperCAmelCase   ) if 'ade' in checkpoint_url else DPTForDepthEstimation(UpperCAmelCase   )
				model.load_state_dict(UpperCAmelCase   )
				model.eval()

				# Check outputs on an image
				A	 =      4_80 if 'ade' in checkpoint_url else 3_84
				A	 =      DPTImageProcessor(size=UpperCAmelCase   )

				A	 =      prepare_img()
				A	 =      image_processor(UpperCAmelCase,						return_tensors='pt'   )

				# forward pass
				A	 =      model(**UpperCAmelCase   ).logits if 'ade' in checkpoint_url else model(**UpperCAmelCase   ).predicted_depth

				# Assert logits
				A	 =      torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]   )
				if "ade" in checkpoint_url:
								A	 =      torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]   )
				assert outputs.shape == torch.Size(UpperCAmelCase   )
				assert (
				    torch.allclose(outputs[0, 0, :3, :3],						UpperCAmelCase,						atol=1E-4   )
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3],						UpperCAmelCase   )
				)

				Path(UpperCAmelCase   ).mkdir(exist_ok=UpperCAmelCase   )
				print(f'Saving model to {pytorch_dump_folder_path}'   )
				model.save_pretrained(UpperCAmelCase   )
				print(f'Saving image processor to {pytorch_dump_folder_path}'   )
				image_processor.save_pretrained(UpperCAmelCase   )

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


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)













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





