import math





def    __lowercase     (						lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					math.loga(math.sqrt(4 * positive_integer + 1	) / 2 + 1 / 2	)

 return exponent == int(_SCREAMING_SNAKE_CASE	)





def    __lowercase     (						lowerCamelCase					:     float = 1 / 12345	):
 UpperCamelCase_					:						List[str] =					0
 UpperCamelCase_					:						Any =					0

 UpperCamelCase_					:						int =					3
 while True:
  UpperCamelCase_					:						Tuple =					(integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(_SCREAMING_SNAKE_CASE	):
   UpperCamelCase_					:						str =					int(_SCREAMING_SNAKE_CASE	)
   total_partitions += 1
   if check_partition_perfect(_SCREAMING_SNAKE_CASE	):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(_SCREAMING_SNAKE_CASE	)
  integer += 1


if __name__ == "__main__":
   print(F"""{solution() = }""")







'''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()
__lowercase	:						Tuple       =	logging.get_logger(__name__)

__lowercase	:						List[Any]       =	torch.device('cpu')






def   lowerCamelCase							():
					__a			:       int					    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a			:       Tuple					    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,      stream=_SCREAMING_SNAKE_CASE   ).raw   )
					return im






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					if swiftformer_name == "swiftformer_xs":
										return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]   )

					elif swiftformer_name == "swiftformer_s":
										return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]   )

					elif swiftformer_name == "swiftformer_l1":
										return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]   )

					elif swiftformer_name == "swiftformer_l3":
										return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       int					    =  dct.pop(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  val






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






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Union[str, Any]					    =  SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					__a			:       List[str]					    =  1_000
					__a			:       Tuple					    =  'huggingface/label-files'
					__a			:       str					    =  'imagenet-1k-id2label.json'
					__a			:       Dict					    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      repo_type='dataset'   )						,      'r'   )   )
					__a			:       Optional[int]					    =  {int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
					__a			:       Any					    =  idalabel
					__a			:       str					    =  {v: k for k, v in idalabel.items()}

					# size of the architecture
					if swiftformer_name == "swiftformer_xs":
										__a			:       Dict					    =  [3, 3, 6, 4]
										__a			:       int					    =  [48, 56, 112, 220]

					elif swiftformer_name == "swiftformer_s":
										__a			:       Dict					    =  [3, 3, 9, 6]
										__a			:       List[str]					    =  [48, 64, 168, 224]

					elif swiftformer_name == "swiftformer_l1":
										__a			:       Dict					    =  [4, 3, 10, 5]
										__a			:       Optional[int]					    =  [48, 96, 192, 384]

					elif swiftformer_name == "swiftformer_l3":
										__a			:       Tuple					    =  [4, 4, 12, 6]
										__a			:       Dict					    =  [64, 128, 320, 512]

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

					__a			:       Dict					    =  create_rename_keys(_SCREAMING_SNAKE_CASE   )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# load HuggingFace model
					__a			:       Tuple					    =  SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()
					hf_model.load_state_dict(_SCREAMING_SNAKE_CASE   )

					# prepare test inputs
					__a			:       Tuple					    =  prepare_img()
					__a			:       str					    =  ViTImageProcessor.from_pretrained('preprocessor_config'   )
					__a			:       Tuple					    =  processor(images=_SCREAMING_SNAKE_CASE						,      return_tensors='pt'   )

					# compare outputs from both models
					__a			:       List[Any]					    =  get_expected_output(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  hf_model(inputs['pixel_values']   ).logits

					assert hf_logits.shape == torch.Size([1, 1_000]   )
					assert torch.allclose(hf_logits[0, 0:5]						,      _SCREAMING_SNAKE_CASE						,      atol=1e-3   )

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


if __name__ == "__main__":
	__lowercase	:						Optional[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.')

	__lowercase	:						Tuple       =	parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



