


'''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()
A				:					Optional[int]    						=			logging.get_logger(__name__)

A				:					Union[str, Any]    						=			torch.device('''cpu''')






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






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]       ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]       )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]       )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]       )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict       ):
							_A					:     Union[str, Any]     = dct.pop(lowerCamelCase       )
							_A					:     List[str]     = val






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






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]       ):
							_A					:     Dict     = SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							_A					:     Any     = 1000
							_A					:     int     = 'huggingface/label-files'
							_A					:     List[Any]     = 'imagenet-1k-id2label.json'
							_A					:     Optional[int]     = json.load(open(hf_hub_download(lowerCamelCase  ,lowerCamelCase  ,repo_type='dataset'       )  ,'r'       )       )
							_A					:     Dict     = {int(lowerCamelCase       ): v for k, v in idalabel.items()}
							_A					:     Optional[int]     = idalabel
							_A					:     Any     = {v: k for k, v in idalabel.items()}

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

							elif swiftformer_name == "swiftformer_s":
														_A					:     List[Any]     = [3, 3, 9, 6]
														_A					:     Tuple     = [48, 64, 168, 224]

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

							elif swiftformer_name == "swiftformer_l3":
														_A					:     Optional[Any]     = [4, 4, 12, 6]
														_A					:     Any     = [64, 128, 320, 512]

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

							_A					:     List[str]     = create_rename_keys(lowerCamelCase       )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

							# load HuggingFace model
							_A					:     str     = SwiftFormerForImageClassification(lowerCamelCase       ).eval()
							hf_model.load_state_dict(lowerCamelCase       )

							# prepare test inputs
							_A					:     Any     = prepare_img()
							_A					:     Optional[int]     = ViTImageProcessor.from_pretrained('preprocessor_config'       )
							_A					:     Any     = processor(images=lowerCamelCase  ,return_tensors='pt'       )

							# compare outputs from both models
							_A					:     int     = get_expected_output(lowerCamelCase       )
							_A					:     Optional[int]     = hf_model(inputs['pixel_values']       ).logits

							assert hf_logits.shape == torch.Size([1, 1000]       )
							assert torch.allclose(hf_logits[0, 0:5]  ,lowerCamelCase  ,atol=1E-3       )

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


if __name__ == "__main__":
			A				:					Dict    						=			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.''')

			A				:					List[str]    						=			parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int = 10       ):
       if not isinstance(lowerCamelCase  ,lowerCamelCase       ) or n < 0:
              raise ValueError('Invalid input'       )
       _A					:     Optional[Any]     = 10**n
       _A					:     List[str]     = 28433 * (pow(2  ,7830457  ,lowerCamelCase       )) + 1
       return str(number % modulus       )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print(f"""{solution(10) = }""")






