





'''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__   :				List[str]      =   logging.get_logger(__name__)

lowercase__   :				int      =   torch.device('''cpu''')






def 	_lowerCAmelCase      (  )					->					List[str]:
     __A						:			Tuple								=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						:			int								=	Image.open(requests.get(__snake_case			,							stream=__snake_case      ).raw      )
     return im






def 	_lowerCAmelCase      (  __snake_case  :			Tuple      )					->					Dict:
     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      (  __snake_case  :			Tuple			,							__snake_case  :			List[Any]			,							__snake_case  :			Any      )					->					Dict:
     __A						:			List[Any]								=	dct.pop(__snake_case      )
     __A						:			int								=	val






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






@torch.no_grad()
def 	_lowerCAmelCase      (  __snake_case  :			Any			,							__snake_case  :			List[str]			,							__snake_case  :			Optional[int]      )					->					List[str]:
     __A						:			Tuple								=	SwiftFormerConfig()

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

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

     elif swiftformer_name == "swiftformer_s":
          __A						:			List[str]								=	[3, 3, 9, 6]
          __A						:			Tuple								=	[48, 64, 1_68, 2_24]

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

     elif swiftformer_name == "swiftformer_l3":
          __A						:			Optional[Any]								=	[4, 4, 12, 6]
          __A						:			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'      ):
               __A						:			Any								=	torch.hub.load_state_dict_from_url(__snake_case			,							map_location='cpu'			,							check_hash=__snake_case      )
          else:
               __A						:			List[str]								=	torch.load(__snake_case			,							map_location='cpu'      )
     __A						:			Union[str, Any]								=	checkpoint

     __A						:			str								=	create_rename_keys(__snake_case      )
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(__snake_case			,							__snake_case			,							__snake_case      )

     # load HuggingFace model
     __A						:			int								=	SwiftFormerForImageClassification(__snake_case      ).eval()
     hf_model.load_state_dict(__snake_case      )

     # prepare test inputs
     __A						:			Dict								=	prepare_img()
     __A						:			Optional[Any]								=	ViTImageProcessor.from_pretrained('preprocessor_config'      )
     __A						:			Union[str, Any]								=	processor(images=__snake_case			,							return_tensors='pt'      )

     # compare outputs from both models
     __A						:			str								=	get_expected_output(__snake_case      )
     __A						:			Optional[Any]								=	hf_model(inputs['pixel_values']      ).logits

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

     Path(__snake_case      ).mkdir(exist_ok=__snake_case      )
     print(f'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'      )
     hf_model.save_pretrained(__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__   :				int      =   parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






'''simple docstring'''




import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5			,							int(math.sqrt(__snake_case      ) + 1      )			,							6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def 	_lowerCAmelCase      (  __snake_case  :			float = 0.1      )					->					int:
     __A						:			Dict								=	3
     __A						:			int								=	3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1			,							(j + 2) * (j + 2)			,							j + 1      ):
               primes += is_prime(__snake_case      )
          j += 2
     return j


if __name__ == "__main__":
  import doctest

  doctest.testmod()