
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 				a						(							):


				'''simple docstring'''
				lowercase_       =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase_       =					Image.open(requests.get(__lowerCAmelCase						,   stream=__lowerCAmelCase					).raw					)
				return im




def 				a						(							snake_case__:					Optional[int]					):


				'''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 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					Union[str, Any]						,   snake_case__:					Tuple					):


				'''simple docstring'''
				lowercase_       =					dct.pop(__lowerCAmelCase					)
				lowercase_       =					val




def 				a						(							snake_case__:					Dict					):


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




@torch.no_grad()
def 				a						(							snake_case__:					Any						,   snake_case__:					Any						,   snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					SwiftFormerConfig()

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

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

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

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

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

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

				lowercase_       =					create_rename_keys(__lowerCAmelCase					)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(__lowerCAmelCase						,   __lowerCAmelCase						,   __lowerCAmelCase					)

				# load HuggingFace model
				lowercase_       =					SwiftFormerForImageClassification(__lowerCAmelCase					).eval()
				hf_model.load_state_dict(__lowerCAmelCase					)

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

				# compare outputs from both models
				lowercase_       =					get_expected_output(__lowerCAmelCase					)
				lowercase_       =					hf_model(inputs['''pixel_values''']					).logits

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

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


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)








def 							__lowercase   ( __lowerCAmelCase							:				list[int]   ):
							a__             =     []
							if len(__lowerCAmelCase   ) == 1:
														return [nums.copy()]
							for _ in range(len(__lowerCAmelCase   )   ):
														a__             =     nums.pop(0   )
														a__             =     permute(__lowerCAmelCase   )
														for perm in permutations:
																					perm.append(__lowerCAmelCase   )
														result.extend(__lowerCAmelCase   )
														nums.append(__lowerCAmelCase   )
							return result






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):

							def backtrack(__lowerCAmelCase							:				Any   ):
														if start == len(__lowerCAmelCase   ) - 1:
																					output.append(nums[:]   )
														else:
																					for i in range(__lowerCAmelCase					,    len(__lowerCAmelCase   )   ):
																												a__      ,					a__             =     nums[i], nums[start]
																												backtrack(start + 1   )
																												a__      ,					a__             =     nums[i], nums[start]  # backtrack

							a__             =     []
							backtrack(0   )
							return output


if __name__ == "__main__":
						import doctest

						# use res to print the data in permute2 function
						snake_case							:       Optional[Any]     			=      permutea([1, 2, 3])
						print(res)
						doctest.testmod()




