
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def  __magic_name__ (   __lowerCAmelCase	:						Optional[Any]							)			->   Optional[int]:
				__lowerCamelCase    					=	SwinConfig(image_size=192							)

				if "base" in model_name:
								__lowerCamelCase    					=	6
								__lowerCamelCase    					=	128
								__lowerCamelCase    					=	(2, 2, 18, 2)
								__lowerCamelCase    					=	(4, 8, 16, 32)
				elif "large" in model_name:
								__lowerCamelCase    					=	12
								__lowerCamelCase    					=	192
								__lowerCamelCase    					=	(2, 2, 18, 2)
								__lowerCamelCase    					=	(6, 12, 24, 48)
				else:
								raise ValueError('''Model not supported, only supports base and large variants'''							)

				__lowerCamelCase    					=	window_size
				__lowerCamelCase    					=	embed_dim
				__lowerCamelCase    					=	depths
				__lowerCamelCase    					=	num_heads

				return config




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   List[Any]:
				if "encoder.mask_token" in name:
								__lowerCamelCase    					=	name.replace('''encoder.mask_token'''		,  '''embeddings.mask_token'''							)
				if "encoder.patch_embed.proj" in name:
								__lowerCamelCase    					=	name.replace('''encoder.patch_embed.proj'''		,  '''embeddings.patch_embeddings.projection'''							)
				if "encoder.patch_embed.norm" in name:
								__lowerCamelCase    					=	name.replace('''encoder.patch_embed.norm'''		,  '''embeddings.norm'''							)
				if "attn.proj" in name:
								__lowerCamelCase    					=	name.replace('''attn.proj'''		,  '''attention.output.dense'''							)
				if "attn" in name:
								__lowerCamelCase    					=	name.replace('''attn'''		,  '''attention.self'''							)
				if "norm1" in name:
								__lowerCamelCase    					=	name.replace('''norm1'''		,  '''layernorm_before'''							)
				if "norm2" in name:
								__lowerCamelCase    					=	name.replace('''norm2'''		,  '''layernorm_after'''							)
				if "mlp.fc1" in name:
								__lowerCamelCase    					=	name.replace('''mlp.fc1'''		,  '''intermediate.dense'''							)
				if "mlp.fc2" in name:
								__lowerCamelCase    					=	name.replace('''mlp.fc2'''		,  '''output.dense'''							)

				if name == "encoder.norm.weight":
								__lowerCamelCase    					=	'''layernorm.weight'''
				if name == "encoder.norm.bias":
								__lowerCamelCase    					=	'''layernorm.bias'''

				if "decoder" in name:
								pass
				else:
								__lowerCamelCase    					=	'''swin.''' + name

				return name




def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Tuple							)			->   List[str]:
				for key in orig_state_dict.copy().keys():
								__lowerCamelCase    					=	orig_state_dict.pop(__lowerCAmelCase							)

								if "attn_mask" in key:
												pass
								elif "qkv" in key:
												__lowerCamelCase    					=	key.split('''.'''							)
												__lowerCamelCase    					=	int(key_split[2]							)
												__lowerCamelCase    					=	int(key_split[4]							)
												__lowerCamelCase    					=	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																__lowerCamelCase    					=	val[:dim, :]
																__lowerCamelCase    					=	val[
																    dim : dim * 2, :
																]
																__lowerCamelCase    					=	val[-dim:, :]
												else:
																__lowerCamelCase    					=	val[
																    :dim
																]
																__lowerCamelCase    					=	val[
																    dim : dim * 2
																]
																__lowerCamelCase    					=	val[
																    -dim:
																]
								else:
												__lowerCamelCase    					=	val

				return orig_state_dict




def  __magic_name__ (   __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Tuple							)			->   Dict:
				__lowerCamelCase    					=	torch.load(__lowerCAmelCase		,  map_location='''cpu'''							)['''model''']

				__lowerCamelCase    					=	get_swin_config(__lowerCAmelCase							)
				__lowerCamelCase    					=	SwinForMaskedImageModeling(__lowerCAmelCase							)
				model.eval()

				__lowerCamelCase    					=	convert_state_dict(__lowerCAmelCase		,  __lowerCAmelCase							)
				model.load_state_dict(__lowerCAmelCase							)

				__lowerCamelCase    					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

				__lowerCamelCase    					=	ViTImageProcessor(size={'''height''': 192, '''width''': 192}							)
				__lowerCamelCase    					=	Image.open(requests.get(__lowerCAmelCase		,  stream=__lowerCAmelCase							).raw							)
				__lowerCamelCase    					=	image_processor(images=__lowerCAmelCase		,  return_tensors='''pt'''							)

				with torch.no_grad():
								__lowerCamelCase    					=	model(**__lowerCAmelCase							).logits

				print(outputs.keys()							)
				print('''Looks ok!'''							)

				if pytorch_dump_folder_path is not None:
								print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
								model.save_pretrained(__lowerCAmelCase							)

								print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
								image_processor.save_pretrained(__lowerCAmelCase							)

				if push_to_hub:
								print(f'''Pushing model and image processor for {model_name} to hub'''							)
								model.push_to_hub(f'''microsoft/{model_name}'''							)
								image_processor.push_to_hub(f'''microsoft/{model_name}'''							)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			Tuple												=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--model_name",
					    default="swin-base-simmim-window6-192",
					    type=str,
					    choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
					    help="Name of the Swin SimMIM model you'd like to convert.",
					)
					parser.add_argument(
					    "--checkpoint_path",
					    default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
					    type=str,
					    help="Path to the original PyTorch checkpoint (.pth file).",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
					)

					SCREAMING_SNAKE_CASE__   :			Tuple												=    parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				__lowerCamelCase    					=	prime_factors(__lowerCAmelCase							)
				if is_square_free(__lowerCAmelCase							):
								return -1 if len(__lowerCAmelCase							) % 2 else 1
				return 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()



