


'''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()
__A	:	Any     =     logging.get_logger(__name__)





def      UpperCAmelCase      (		lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						DPTConfig()

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

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

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

		return config, expected_shape





def      UpperCAmelCase      (		lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
		for k in ignore_keys:
				state_dict.pop(lowerCamelCase_				,      lowerCamelCase_ )





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







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

		return name





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :List[str] ):







		'''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)
				snake_case_		:							Optional[Any]			       =						state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
				snake_case_		:							Tuple			       =						state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
				# next, add query, keys and values (in that order) to the state dict
				snake_case_		:							List[Any]			       =						in_proj_weight[: config.hidden_size, :]
				snake_case_		:							Tuple			       =						in_proj_bias[: config.hidden_size]
				snake_case_		:							List[str]			       =						in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				snake_case_		:							Union[str, Any]			       =						in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				snake_case_		:							Optional[int]			       =						in_proj_weight[
				    -config.hidden_size :, :
				]
				snake_case_		:							Optional[int]			       =						in_proj_bias[-config.hidden_size :]





def      UpperCAmelCase      (		):







		'''simple docstring'''
		snake_case_		:							List[str]			       =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		snake_case_		:							Tuple			       =						Image.open(requests.get(lowerCamelCase_				,      stream=lowerCamelCase_ ).raw )
		return im





@torch.no_grad()
def      UpperCAmelCase      (		lowerCamelCase_  :Dict				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_       ,      snake_case_		:							Dict			       =						get_dpt_config(lowerCamelCase_ )
		# load original state_dict from URL
		snake_case_		:							Union[str, 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():
				snake_case_		:							List[Any]			       =						state_dict.pop(lowerCamelCase_ )
				snake_case_		:							Union[str, Any]			       =						val
		# read in qkv matrices
		read_in_q_k_v(lowerCamelCase_				,      lowerCamelCase_ )

		# load HuggingFace model
		snake_case_		:							Tuple			       =						DPTForSemanticSegmentation(lowerCamelCase_ ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase_ )
		model.load_state_dict(lowerCamelCase_ )
		model.eval()

		# Check outputs on an image
		snake_case_		:							Dict			       =						4_80 if """ade""" in checkpoint_url else 3_84
		snake_case_		:							str			       =						DPTImageProcessor(size=lowerCamelCase_ )

		snake_case_		:							Tuple			       =						prepare_img()
		snake_case_		:							Optional[int]			       =						image_processor(lowerCamelCase_				,      return_tensors="""pt""" )

		# forward pass
		snake_case_		:							Optional[int]			       =						model(**lowerCamelCase_ ).logits if """ade""" in checkpoint_url else model(**lowerCamelCase_ ).predicted_depth

		# Assert logits
		snake_case_		:							Dict			       =						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:
				snake_case_		:							Optional[int]			       =						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(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__":
							__A	:	Tuple     =     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.',
							)

							__A	:	Union[str, Any]     =     parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


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