





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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Tuple 						=      DPTConfig()

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

		if "ade" in checkpoint_url:
				lowerCamelCase_	:     Any 						=      True

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

		return config, expected_shape


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Dict 						=      ['pretrained.model.head.weight', 'pretrained.model.head.bias']
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase__							,lowerCAmelCase__						)


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

		return name


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


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Tuple 						=      'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase_	:     Optional[Any] 						=      Image.open(requests.get(lowerCAmelCase__							,stream=lowerCAmelCase__						).raw						)
		return im


@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_						,      lowerCamelCase_	:     List[str] 						=      get_dpt_config(lowerCAmelCase__						)
		# load original state_dict from URL
		lowerCamelCase_	:     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():
				lowerCamelCase_	:     int 						=      state_dict.pop(lowerCAmelCase__						)
				lowerCamelCase_	:     Dict 						=      val
		# read in qkv matrices
		read_in_q_k_v(lowerCAmelCase__							,lowerCAmelCase__						)

		# load HuggingFace model
		lowerCamelCase_	:     Dict 						=      DPTForSemanticSegmentation(lowerCAmelCase__						) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase__						)
		model.load_state_dict(lowerCAmelCase__						)
		model.eval()

		# Check outputs on an image
		lowerCamelCase_	:     str 						=      4_80 if 'ade' in checkpoint_url else 3_84
		lowerCamelCase_	:     Optional[int] 						=      DPTImageProcessor(size=lowerCAmelCase__						)

		lowerCamelCase_	:     Union[str, Any] 						=      prepare_img()
		lowerCamelCase_	:     List[Any] 						=      image_processor(lowerCAmelCase__							,return_tensors='pt'						)

		# forward pass
		lowerCamelCase_	:     Dict 						=      model(**lowerCAmelCase__						).logits if 'ade' in checkpoint_url else model(**lowerCAmelCase__						).predicted_depth

		# Assert logits
		lowerCamelCase_	:     List[str] 						=      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:
				lowerCamelCase_	:     Any 						=      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__":
		_lowercase		:  List[Any]			       =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.""",
		)

		_lowercase		:  int			       =parser.parse_args()
		convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







from __future__ import annotations

import math


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		if num <= 0:
				lowerCamelCase_	:     Optional[int] 						=      F"{num}: Invalid input, please enter a positive integer."
				raise ValueError(lowerCAmelCase__						)

		lowerCamelCase_	:     str 						=      [True] * (num + 1)
		lowerCamelCase_	:     List[str] 						=      []
		lowerCamelCase_	:     Optional[int] 						=      2
		lowerCamelCase_	:     List[str] 						=      int(math.sqrt(lowerCAmelCase__						)						)

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(lowerCAmelCase__						)

						# Set multiples of start be False
						for i in range(start * start							,num + 1							,lowerCAmelCase__						):
								if sieve[i] is True:
										lowerCamelCase_	:     Tuple 						=      False

				start += 1

		for j in range(end + 1							,num + 1						):
				if sieve[j] is True:
						prime.append(lowerCAmelCase__						)

		return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
