


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

A__		:					List[Any]												=  torch.device("""cpu""")



def 			UpperCAmelCase__					(	)     ->		List[str]:
     __lowerCamelCase   :	List[Any]								=		'http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowerCamelCase   :	Union[str, Any]								=		Image.open(requests.get(UpperCAmelCase_      ,    stream=UpperCAmelCase_							).raw							)
     return im



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		Optional[int]:
     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 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				List[Any]							)     ->		Tuple:
     __lowerCamelCase   :	List[str]								=		dct.pop(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		val



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



@torch.no_grad()
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				Optional[int]							)     ->		List[Any]:
     __lowerCamelCase   :	Union[str, Any]								=		SwiftFormerConfig()

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

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

     elif swiftformer_name == "swiftformer_s":
          __lowerCamelCase   :	Optional[int]								=		[3, 3, 9, 6]
          __lowerCamelCase   :	int								=		[48, 64, 1_68, 2_24]

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

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

     __lowerCamelCase   :	int								=		create_rename_keys(UpperCAmelCase_							)
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)

     # load HuggingFace model
     __lowerCamelCase   :	Dict								=		SwiftFormerForImageClassification(UpperCAmelCase_							).eval()
     hf_model.load_state_dict(UpperCAmelCase_							)

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

     # compare outputs from both models
     __lowerCamelCase   :	Tuple								=		get_expected_output(UpperCAmelCase_							)
     __lowerCamelCase   :	Dict								=		hf_model(inputs['pixel_values']							).logits

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

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


if __name__ == "__main__":
      A__		:					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.""")

      A__		:					Optional[Any]												=  parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










'''simple docstring'''



from numpy import exp, pi, sqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				float = 0.0      ,    UpperCAmelCase_    :				float = 1.0							)     ->		int:
     return 1 / sqrt(2 * pi * sigma**2							) * exp(-((x - mu) ** 2) / (2 * sigma**2)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






