


'''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     :						str	=     logging.get_logger(__name__)

__lowercase     :						str	=     torch.device('cpu')






def   lowerCamelCase							():
					__a      :	Optional[int]    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a      :	List[str]    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,				stream=_SCREAMING_SNAKE_CASE       ).raw       )
					return im






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[str]       ):
					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   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
					__a      :	Tuple    =  dct.pop(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  val






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






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
					__a      :	Union[str, Any]    =  SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					__a      :	List[Any]    =  1_000
					__a      :	List[str]    =  'huggingface/label-files'
					__a      :	Union[str, Any]    =  'imagenet-1k-id2label.json'
					__a      :	List[str]    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				repo_type='dataset'       )						,				'r'       )       )
					__a      :	List[Any]    =  {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
					__a      :	List[Any]    =  idalabel
					__a      :	str    =  {v: k for k, v in idalabel.items()}

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

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

					elif swiftformer_name == "swiftformer_l1":
										__a      :	List[Any]    =  [4, 3, 10, 5]
										__a      :	Tuple    =  [48, 96, 192, 384]

					elif swiftformer_name == "swiftformer_l3":
										__a      :	Union[str, Any]    =  [4, 4, 12, 6]
										__a      :	Optional[int]    =  [64, 128, 320, 512]

					# load state_dict of original model, remove and rename some keys
					if original_ckpt:
										if original_ckpt.startswith('https'       ):
															__a      :	Union[str, Any]    =  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE						,				map_location='cpu'						,				check_hash=_SCREAMING_SNAKE_CASE       )
										else:
															__a      :	Dict    =  torch.load(_SCREAMING_SNAKE_CASE						,				map_location='cpu'       )
					__a      :	Optional[int]    =  checkpoint

					__a      :	Tuple    =  create_rename_keys(_SCREAMING_SNAKE_CASE       )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )

					# load HuggingFace model
					__a      :	Optional[Any]    =  SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE       ).eval()
					hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

					# prepare test inputs
					__a      :	Dict    =  prepare_img()
					__a      :	Tuple    =  ViTImageProcessor.from_pretrained('preprocessor_config'       )
					__a      :	List[Any]    =  processor(images=_SCREAMING_SNAKE_CASE						,				return_tensors='pt'       )

					# compare outputs from both models
					__a      :	str    =  get_expected_output(_SCREAMING_SNAKE_CASE       )
					__a      :	Any    =  hf_model(inputs['pixel_values']       ).logits

					assert hf_logits.shape == torch.Size([1, 1_000]       )
					assert torch.allclose(hf_logits[0, 0:5]						,				_SCREAMING_SNAKE_CASE						,				atol=1e-3       )

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


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







'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int       ):
					return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()



