






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()
__UpperCAmelCase							:   Any							=  logging.get_logger(__name__)

__UpperCAmelCase							:   Tuple							=  torch.device("cpu")





def 					lowercase_ (    )     -> List[Any]:





				'''simple docstring'''


				snake_case__      :int         =   "http://images.cocodataset.org/val2017/000000039769.jpg"
				snake_case__      :int         =   Image.open(requests.get(__snake_case	,	stream=__snake_case      ).raw      )
				return im





def 					lowercase_ (    __snake_case     :							List[str]      )     -> int:





				'''simple docstring'''


				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 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							Dict      )     -> Tuple:





				'''simple docstring'''


				snake_case__      :List[Any]         =   dct.pop(__snake_case      )
				snake_case__      :Optional[Any]         =   val





def 					lowercase_ (    __snake_case     :							int      )     -> int:





				'''simple docstring'''


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





@torch.no_grad()
def 					lowercase_ (    __snake_case     :							List[Any]	,	__snake_case     :							str	,	__snake_case     :							Tuple      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :Dict         =   SwiftFormerConfig()

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

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

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

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

				elif swiftformer_name == "swiftformer_l3":
								snake_case__      :Tuple         =   [4, 4, 12, 6]
								snake_case__      :List[Any]         =   [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"      ):
												snake_case__      :Any         =   torch.hub.load_state_dict_from_url(__snake_case	,	map_location="cpu"	,	check_hash=__snake_case      )
								else:
												snake_case__      :Tuple         =   torch.load(__snake_case	,	map_location="cpu"      )
				snake_case__      :List[Any]         =   checkpoint

				snake_case__      :List[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
				snake_case__      :Optional[Any]         =   SwiftFormerForImageClassification(__snake_case      ).eval()
				hf_model.load_state_dict(__snake_case      )

				# prepare test inputs
				snake_case__      :Optional[int]         =   prepare_img()
				snake_case__      :List[str]         =   ViTImageProcessor.from_pretrained("preprocessor_config"      )
				snake_case__      :Dict         =   processor(images=__snake_case	,	return_tensors="pt"      )

				# compare outputs from both models
				snake_case__      :int         =   get_expected_output(__snake_case      )
				snake_case__      :List[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__":
	__UpperCAmelCase							:   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.")

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












def 					lowercase_ (    __snake_case     :							str      )     -> list:





				'''simple docstring'''


				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(__snake_case      )      )
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
	__import__("doctest").testmod()