



"""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()
__a      =				logging.get_logger(__name__)

__a      =				torch.device("cpu")
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						snake_case_ :Tuple						=			Image.open(requests.get(_lowercase,					stream=_lowercase	).raw	)
						return im
def    A_  (			_lowercase	):







						'''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    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			dct.pop(_lowercase	)
						snake_case_ :List[str]						=			val
def    A_  (			_lowercase	):







						'''simple docstring'''


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







						'''simple docstring'''


						snake_case_ :Tuple						=			SwiftFormerConfig()

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

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

						elif swiftformer_name == "swiftformer_s":
												snake_case_ :Union[str, Any]						=			[3, 3, 9, 6]
												snake_case_ :Any						=			[48, 64, 168, 224]

						elif swiftformer_name == "swiftformer_l1":
												snake_case_ :Any						=			[4, 3, 10, 5]
												snake_case_ :Union[str, Any]						=			[48, 96, 192, 384]

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

						# load state_dict of original model, remove and rename some keys
						if original_ckpt:
												if original_ckpt.startswith("""https"""	):
																		snake_case_ :str						=			torch.hub.load_state_dict_from_url(_lowercase,					map_location="""cpu""",					check_hash=_lowercase	)
												else:
																		snake_case_ :List[str]						=			torch.load(_lowercase,					map_location="""cpu"""	)
						snake_case_ :Union[str, Any]						=			checkpoint

						snake_case_ :Union[str, Any]						=			create_rename_keys(_lowercase	)
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(_lowercase,					_lowercase,					_lowercase	)

						# load HuggingFace model
						snake_case_ :Optional[int]						=			SwiftFormerForImageClassification(_lowercase	).eval()
						hf_model.load_state_dict(_lowercase	)

						# prepare test inputs
						snake_case_ :Any						=			prepare_img()
						snake_case_ :Optional[int]						=			ViTImageProcessor.from_pretrained("""preprocessor_config"""	)
						snake_case_ :Optional[int]						=			processor(images=_lowercase,					return_tensors="""pt"""	)

						# compare outputs from both models
						snake_case_ :List[Any]						=			get_expected_output(_lowercase	)
						snake_case_ :int						=			hf_model(inputs["""pixel_values"""]	).logits

						assert hf_logits.shape == torch.Size([1, 1000]	)
						assert torch.allclose(hf_logits[0, 0:5],					_lowercase,					atol=1e-3	)

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


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)





"""simple docstring"""
import math
class 				lowerCamelCase  :






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							list[list[float]]						,     snake_case:							list[int]						)   ->	int:
											snake_case_ :Any						=			0.0
											snake_case_ :Tuple						=			0.0
											for i in range(len(snake_case						)						):
																	da += math.pow((sample[i] - weights[0][i])						,     2						)
																	da += math.pow((sample[i] - weights[1][i])						,     2						)
																	return 0 if da > da else 1
											return 0






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							list[list[int | float]]						,     snake_case:							list[int]						,     snake_case:							int						,     snake_case:							float						)   ->	list[list[int | float]]:
											for i in range(len(snake_case						)						):
																	weights[j][i] += alpha * (sample[i] - weights[j][i])
											return weights

def    A_  (			):







						'''simple docstring'''


						snake_case_ :Dict						=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						snake_case_ :List[Any]						=			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						snake_case_ :Optional[Any]						=			SelfOrganizingMap()
						snake_case_ :Dict						=			3
						snake_case_ :Dict						=			0.5

						for _ in range(_lowercase	):
												for j in range(len(_lowercase	)	):
																		# training sample
																		snake_case_ :List[Any]						=			training_samples[j]

																		# Compute the winning vector
																		snake_case_ :Optional[int]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

																		# Update the winning vector
																		snake_case_ :List[str]						=			self_organizing_map.update(_lowercase,					_lowercase,					_lowercase,					_lowercase	)

    # classify test sample
						snake_case_ :str						=			[0, 0, 0, 1]
						snake_case_ :List[Any]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""	)
						print(f"""Weights that have been trained : {weights}"""	)


# running the main() function
if __name__ == "__main__":
						main()
