

"""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()
__UpperCamelCase  :      List[Any]							      =		logging.get_logger(__name__)

__UpperCamelCase  :      Optional[Any]							      =		torch.device('''cpu''')

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase__	:						Any  				=	Image.open(requests.get(A_						,     stream=A_ ).raw )
	return im

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	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   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	dct.pop(A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	val

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

@torch.no_grad()
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						int  				=	SwiftFormerConfig()

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

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

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

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

	elif swiftformer_name == "swiftformer_l3":
		lowerCAmelCase__	:						str  				=	[4, 4, 12, 6]
		lowerCAmelCase__	:						List[str]  				=	[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__	:						Tuple  				=	torch.hub.load_state_dict_from_url(A_						,     map_location='''cpu'''						,     check_hash=A_ )
		else:
			lowerCAmelCase__	:						str  				=	torch.load(A_						,     map_location='''cpu''' )
	lowerCAmelCase__	:						Optional[int]  				=	checkpoint

	lowerCAmelCase__	:						Dict  				=	create_rename_keys(A_ )
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(A_						,     A_						,     A_ )

	# load HuggingFace model
	lowerCAmelCase__	:						Tuple  				=	SwiftFormerForImageClassification(A_ ).eval()
	hf_model.load_state_dict(A_ )

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

	# compare outputs from both models
	lowerCAmelCase__	:						str  				=	get_expected_output(A_ )
	lowerCAmelCase__	:						int  				=	hf_model(inputs['''pixel_values'''] ).logits

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

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


if __name__ == "__main__":
							__UpperCamelCase  :      Union[str, Any]							      =		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  :      str							      =		parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







"""simple docstring"""





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	return numa ^ numa < 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()




