






"""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			:    int				 =		logging.get_logger(__name__)

a			:    Optional[Any]				 =		torch.device("""cpu""")





def 						lowercase__()    ->Union[str, Any]:




						"""simple docstring"""



						lowercase__    :				int=	"http://images.cocodataset.org/val2017/000000039769.jpg"
						lowercase__    :				List[str]=	Image.open(requests.get(A ,		stream=A						).raw						)
						return im





def 						lowercase__(A						)    ->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 						lowercase__(A ,		A ,		A						)    ->Union[str, Any]:




						"""simple docstring"""



						lowercase__    :				List[str]=	dct.pop(A						)
						lowercase__    :				Optional[int]=	val





def 						lowercase__(A						)    ->int:




						"""simple docstring"""



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





@torch.no_grad()
def 						lowercase__(A ,		A ,		A						)    ->Dict:




						"""simple docstring"""



						lowercase__    :				List[Any]=	SwiftFormerConfig()

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

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

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

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

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

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

						lowercase__    :				Tuple=	create_rename_keys(A						)
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(A ,		A ,		A						)

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

						# prepare test inputs
						lowercase__    :				List[str]=	prepare_img()
						lowercase__    :				str=	ViTImageProcessor.from_pretrained("preprocessor_config"						)
						lowercase__    :				Dict=	processor(images=A ,		return_tensors="pt"						)

						# compare outputs from both models
						lowercase__    :				Union[str, Any]=	get_expected_output(A						)
						lowercase__    :				str=	hf_model(inputs["pixel_values"]						).logits

						assert hf_logits.shape == torch.Size([1, 1_000]						)
						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__":
			a			:    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.""")

			a			:    str				 =		parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)














"""simple docstring"""







from pathlib import Path

import fire
from tqdm import tqdm





def 						lowercase__(A="ro" ,		A="en" ,		A="wmt16" ,		A=None						)    ->None:




						"""simple docstring"""



						try:
												import datasets
						except (ModuleNotFoundError, ImportError):
												raise ImportError("run pip install datasets"						)
						lowercase__    :				int=	f'''{src_lang}-{tgt_lang}'''
						print(f'''Converting {dataset}-{pair}'''						)
						lowercase__    :				List[Any]=	datasets.load_dataset(A ,		A						)
						if save_dir is None:
												lowercase__    :				Union[str, Any]=	f'''{dataset}-{pair}'''
						lowercase__    :				str=	Path(A						)
						save_dir.mkdir(exist_ok=A						)

						for split in ds.keys():
												print(f'''Splitting {split} with {ds[split].num_rows} records'''						)

												# to save to val.source, val.target like summary datasets
												lowercase__    :				Any=	"val" if split == "validation" else split
												lowercase__    :				List[Any]=	save_dir.joinpath(f'''{fn}.source'''						)
												lowercase__    :				Optional[Any]=	save_dir.joinpath(f'''{fn}.target'''						)
												lowercase__    :				Optional[int]=	src_path.open("w+"						)
												lowercase__    :				Any=	tgt_path.open("w+"						)

												# reader is the bottleneck so writing one record at a time doesn't slow things down
												for x in tqdm(ds[split]						):
																		lowercase__    :				int=	x["translation"]
																		src_fp.write(ex[src_lang] + "\n"						)
																		tgt_fp.write(ex[tgt_lang] + "\n"						)

						print(f'''Saved {dataset} dataset to {save_dir}'''						)


if __name__ == "__main__":
			fire.Fire(download_wmt_dataset)






