



"""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				:				int   		=		logging.get_logger(__name__)

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






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				='''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase__	:	List[Any]				=Image.open(requests.get(__snake_case							,	stream=__snake_case			).raw			)
			return im






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]			):
			"""simple docstring"""







			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   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Union[str, Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	Any				=dct.pop(__snake_case			)
			lowerCamelCase__	:	Any				=val






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






@torch.no_grad()
def   snake_case__     (	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Any			):
			"""simple docstring"""
			lowerCamelCase__	:	Any				=SwiftFormerConfig()

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

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

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

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

			elif swiftformer_name == "swiftformer_l3":
						lowerCamelCase__	:	str				=[4, 4, 12, 6]
						lowerCamelCase__	:	List[Any]				=[64, 128, 320, 512]

			# 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(__snake_case							,	map_location='''cpu'''							,	check_hash=__snake_case			)
						else:
									lowerCamelCase__	:	Tuple				=torch.load(__snake_case							,	map_location='''cpu'''			)
			lowerCamelCase__	:	Union[str, Any]				=checkpoint

			lowerCamelCase__	:	Dict				=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
			lowerCamelCase__	:	Any				=SwiftFormerForImageClassification(__snake_case			).eval()
			hf_model.load_state_dict(__snake_case			)

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

			# compare outputs from both models
			lowerCamelCase__	:	int				=get_expected_output(__snake_case			)
			lowerCamelCase__	:	Tuple				=hf_model(inputs['''pixel_values''']			).logits

			assert hf_logits.shape == torch.Size([1, 1000]			)
			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				:				Optional[Any]   		=		parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









"""simple docstring"""






from __future__ import annotations

from collections.abc import Iterator

class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						str,						lowerCamelCase							:						int  )->      None:
								lowerCamelCase__	:	str				=value
								lowerCamelCase__	:	Node | None				=None
								lowerCamelCase__	:	Node | None				=None





class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						int,						lowerCamelCase							:						Node  )->      None:
								lowerCamelCase__	:	Any				=tree



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Node | None  )->      int:
								if node is None:
											return 0
								return node.value + (
								    self.depth_first_search(node.left  ) + self.depth_first_search(node.right  )
								)




					def __iter__( self							:						Dict  )->      Iterator[int]:
								yield self.depth_first_search(self.tree  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




