





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						=   logging.get_logger(__name__)

_a						=   torch.device("cpu")





def lowerCAmelCase__()       ->   List[Any]:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	if swiftformer_name == "swiftformer_xs":
		return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]							)

	elif swiftformer_name == "swiftformer_s":
		return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]							)

	elif swiftformer_name == "swiftformer_l1":
		return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]							)

	elif swiftformer_name == "swiftformer_l3":
		return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]							)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	dct.pop(__snake_case							)
	lowerCamelCase__              =	val





def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







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





@torch.no_grad()
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	SwiftFormerConfig()

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

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

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

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

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

	# load state_dict of original model, remove and rename some keys
	if original_ckpt:
		if original_ckpt.startswith('''https'''							):
			lowerCamelCase__              =	torch.hub.load_state_dict_from_url(__snake_case ,map_location='''cpu''' ,check_hash=__snake_case							)
		else:
			lowerCamelCase__              =	torch.load(__snake_case ,map_location='''cpu'''							)
	lowerCamelCase__              =	checkpoint

	lowerCamelCase__              =	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__              =	SwiftFormerForImageClassification(__snake_case							).eval()
	hf_model.load_state_dict(__snake_case							)

	# prepare test inputs
	lowerCamelCase__              =	prepare_img()
	lowerCamelCase__              =	ViTImageProcessor.from_pretrained('''preprocessor_config'''							)
	lowerCamelCase__              =	processor(images=__snake_case ,return_tensors='''pt'''							)

	# compare outputs from both models
	lowerCamelCase__              =	get_expected_output(__snake_case							)
	lowerCamelCase__              =	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__":
			_a						=   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						=   parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_a						=   open  # noqa: we just need to have a builtin inside this module to test it properly



