




'''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()
_SCREAMING_SNAKE_CASE							:       Tuple				=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       str				=			torch.device("cpu")


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				"http://images.cocodataset.org/val2017/000000039769.jpg"
							snake_case_      =				Image.open(requests.get(snake_case							,    stream=snake_case					).raw					)
							return im


def 					UpperCamelCase_(     snake_case							:	Optional[int]					):

							'''simple docstring'''






							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]					)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]					)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]					)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]					)


def 					UpperCamelCase_(     snake_case							:	Any							,    snake_case							:	Tuple							,    snake_case							:	List[Any]					):

							'''simple docstring'''






							snake_case_      =				dct.pop(snake_case					)
							snake_case_      =				val


def 					UpperCamelCase_(     snake_case							:	Tuple					):

							'''simple docstring'''






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


@torch.no_grad()
def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	Dict							,    snake_case							:	Union[str, Any]					):

							'''simple docstring'''






							snake_case_      =				SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							snake_case_      =				1_0_0_0
							snake_case_      =				"huggingface/label-files"
							snake_case_      =				"imagenet-1k-id2label.json"
							snake_case_      =				json.load(open(hf_hub_download(snake_case							,    snake_case							,    repo_type="dataset"					)							,    "r"					)					)
							snake_case_      =				{int(snake_case					): v for k, v in idalabel.items()}
							snake_case_      =				idalabel
							snake_case_      =				{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														snake_case_      =				[3, 3, 6, 4]
														snake_case_      =				[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														snake_case_      =				[3, 3, 9, 6]
														snake_case_      =				[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														snake_case_      =				[4, 3, 1_0, 5]
														snake_case_      =				[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														snake_case_      =				[4, 4, 1_2, 6]
														snake_case_      =				[6_4, 1_2_8, 3_2_0, 5_1_2]

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

							snake_case_      =				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
							snake_case_      =				SwiftFormerForImageClassification(snake_case					).eval()
							hf_model.load_state_dict(snake_case					)

							# prepare test inputs
							snake_case_      =				prepare_img()
							snake_case_      =				ViTImageProcessor.from_pretrained("preprocessor_config"					)
							snake_case_      =				processor(images=snake_case							,    return_tensors="pt"					)

							# compare outputs from both models
							snake_case_      =				get_expected_output(snake_case					)
							snake_case_      =				hf_model(inputs["pixel_values"]					).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]					)
							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__":
		_SCREAMING_SNAKE_CASE							:       Tuple				=			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.")

		_SCREAMING_SNAKE_CASE							:       int				=			parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0_0_0_0					):

							'''simple docstring'''






							snake_case_      =				set(range(3							,    snake_case							,    2					)					)
							primes.add(2					)
							for p in range(3							,    snake_case							,    2					):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p							,    snake_case							,    snake_case					)					)					)

							snake_case_      =				[float(snake_case					) for n in range(limit + 1					)]

							for p in primes:
														for n in range(snake_case							,    limit + 1							,    snake_case					):
																					phi[n] *= 1 - 1 / p

							return int(sum(phi[2:]					)					)


if __name__ == "__main__":
		print(F"{solution() = }")




