
"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__ :       Optional[Any]						     =			logging.get_logger(__name__)

lowerCamelCase__ :       List[str]						     =			torch.device("cpu")





def __A						(   )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE       :     Any				= Image.open(requests.get(a_		,		stream=a_     ).raw     )
		return im





def __A						(   a_ :							Any     )->							Tuple:




		'''simple docstring'''






		if swiftformer_name == "swiftformer_xs":
				return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]     )

		elif swiftformer_name == "swiftformer_s":
				return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]     )

		elif swiftformer_name == "swiftformer_l1":
				return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]     )

		elif swiftformer_name == "swiftformer_l3":
				return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]     )





def __A						(   a_ :							Any		,		a_ :							Union[str, Any]		,		a_ :							Any     )->							Optional[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= dct.pop(a_     )
		SCREAMING_SNAKE_CASE       :     Any				= val





def __A						(   a_ :							List[str]     )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= []
		for k in state_dict.keys():
				SCREAMING_SNAKE_CASE       :     Any				= k
				if ".pwconv" in k:
						SCREAMING_SNAKE_CASE       :     Dict				= k_new.replace('''.pwconv'''		,		'''.point_wise_conv'''     )
				if ".dwconv" in k:
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= k_new.replace('''.dwconv'''		,		'''.depth_wise_conv'''     )
				if ".Proj." in k:
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= k_new.replace('''.Proj.'''		,		'''.proj.'''     )
				if "patch_embed" in k_new:
						SCREAMING_SNAKE_CASE       :     Any				= k_new.replace('''patch_embed'''		,		'''swiftformer.patch_embed.patch_embedding'''     )
				if "network" in k_new:
						SCREAMING_SNAKE_CASE       :     Any				= k_new.split('''.'''     )
						if ls[2].isdigit():
								SCREAMING_SNAKE_CASE       :     int				= '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]     )
						else:
								SCREAMING_SNAKE_CASE       :     Any				= k_new.replace('''network'''		,		'''swiftformer.encoder.network'''     )
				rename_keys.append((k, k_new)     )
		return rename_keys





@torch.no_grad()
def __A						(   a_ :							str		,		a_ :							Optional[int]		,		a_ :							int     )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		SCREAMING_SNAKE_CASE       :     Tuple				= 10_00
		SCREAMING_SNAKE_CASE       :     Tuple				= '''huggingface/label-files'''
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= '''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE       :     List[Any]				= json.load(open(hf_hub_download(a_		,		a_		,		repo_type='''dataset'''     )		,		'''r'''     )     )
		SCREAMING_SNAKE_CASE       :     int				= {int(a_     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE       :     str				= idalabel
		SCREAMING_SNAKE_CASE       :     Tuple				= {v: k for k, v in idalabel.items()}

		# size of the architecture
		if swiftformer_name == "swiftformer_xs":
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [3, 3, 6, 4]
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [48, 56, 1_12, 2_20]

		elif swiftformer_name == "swiftformer_s":
				SCREAMING_SNAKE_CASE       :     Dict				= [3, 3, 9, 6]
				SCREAMING_SNAKE_CASE       :     Tuple				= [48, 64, 1_68, 2_24]

		elif swiftformer_name == "swiftformer_l1":
				SCREAMING_SNAKE_CASE       :     Dict				= [4, 3, 10, 5]
				SCREAMING_SNAKE_CASE       :     str				= [48, 96, 1_92, 3_84]

		elif swiftformer_name == "swiftformer_l3":
				SCREAMING_SNAKE_CASE       :     str				= [4, 4, 12, 6]
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [64, 1_28, 3_20, 5_12]

		# load state_dict of original model, remove and rename some keys
		if original_ckpt:
				if original_ckpt.startswith('''https'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= torch.hub.load_state_dict_from_url(a_		,		map_location='''cpu'''		,		check_hash=a_     )
				else:
						SCREAMING_SNAKE_CASE       :     List[Any]				= torch.load(a_		,		map_location='''cpu'''     )
		SCREAMING_SNAKE_CASE       :     Any				= checkpoint

		SCREAMING_SNAKE_CASE       :     int				= create_rename_keys(a_     )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(a_		,		a_		,		a_     )

		# load HuggingFace model
		SCREAMING_SNAKE_CASE       :     Dict				= SwiftFormerForImageClassification(a_     ).eval()
		hf_model.load_state_dict(a_     )

		# prepare test inputs
		SCREAMING_SNAKE_CASE       :     Optional[int]				= prepare_img()
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ViTImageProcessor.from_pretrained('''preprocessor_config'''     )
		SCREAMING_SNAKE_CASE       :     List[str]				= processor(images=a_		,		return_tensors='''pt'''     )

		# compare outputs from both models
		SCREAMING_SNAKE_CASE       :     List[str]				= get_expected_output(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= hf_model(inputs['''pixel_values''']     ).logits

		assert hf_logits.shape == torch.Size([1, 10_00]     )
		assert torch.allclose(hf_logits[0, 0:5]		,		a_		,		atol=1E-3     )

		Path(a_     ).mkdir(exist_ok=a_     )
		print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"     )
		hf_model.save_pretrained(a_     )


if __name__ == "__main__":
					lowerCamelCase__ :       int						     =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--swiftformer_name",
					    default="swiftformer_xs",
					    choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
					    type=str,
					    help="Name of the SwiftFormer model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default="./converted_outputs/",
					    type=str,
					    help="Path to the output PyTorch model directory.",
					)
					parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

					lowerCamelCase__ :       Union[str, Any]						     =			parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







"""simple docstring"""






def __A						(   a_ :							int     )->							list[int]:




		'''simple docstring'''






		if num <= 0:
				raise ValueError('''Input must be a positive integer'''     )

		SCREAMING_SNAKE_CASE       :     Optional[int]				= [True] * (num + 1)

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 2
		while p * p <= num:
				if primes[p]:
						for i in range(p * p		,		num + 1		,		a_     ):
								SCREAMING_SNAKE_CASE       :     Any				= False
				p += 1

		return [prime for prime in range(2		,		num + 1     ) if primes[prime]]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCamelCase__ :       str						     =			int(input("Enter a positive integer: ").strip())
					print(prime_sieve_eratosthenes(user_num))





