



def A    (							)    ->   int:
			return [
			    a * b * (1_000 - a - b)
			    for a in range(1			,						999       )
			    for b in range(__UpperCamelCase			,						999       )
			    if (a * a + b * b == (1_000 - a - b) ** 2)
			][0]


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









import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	SwinConfig(image_size=192						)

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

      lowercase__  =	window_size
      lowercase__  =	embed_dim
      lowercase__  =	depths
      lowercase__  =	num_heads

      return config

def 	_lowerCAmelCase (    A__						):
      if "encoder.mask_token" in name:
            lowercase__  =	name.replace('encoder.mask_token'    ,	'embeddings.mask_token'						)
      if "encoder.patch_embed.proj" in name:
            lowercase__  =	name.replace('encoder.patch_embed.proj'    ,	'embeddings.patch_embeddings.projection'						)
      if "encoder.patch_embed.norm" in name:
            lowercase__  =	name.replace('encoder.patch_embed.norm'    ,	'embeddings.norm'						)
      if "attn.proj" in name:
            lowercase__  =	name.replace('attn.proj'    ,	'attention.output.dense'						)
      if "attn" in name:
            lowercase__  =	name.replace('attn'    ,	'attention.self'						)
      if "norm1" in name:
            lowercase__  =	name.replace('norm1'    ,	'layernorm_before'						)
      if "norm2" in name:
            lowercase__  =	name.replace('norm2'    ,	'layernorm_after'						)
      if "mlp.fc1" in name:
            lowercase__  =	name.replace('mlp.fc1'    ,	'intermediate.dense'						)
      if "mlp.fc2" in name:
            lowercase__  =	name.replace('mlp.fc2'    ,	'output.dense'						)

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

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

      return name

def 	_lowerCAmelCase (    A__    ,	A__						):
      for key in orig_state_dict.copy().keys():
            lowercase__  =	orig_state_dict.pop(A__						)

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

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

      return orig_state_dict

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__						):
      lowercase__  =	torch.load(A__    ,	map_location='cpu'						)['model']

      lowercase__  =	get_swin_config(A__						)
      lowercase__  =	SwinForMaskedImageModeling(A__						)
      model.eval()

      lowercase__  =	convert_state_dict(A__    ,	A__						)
      model.load_state_dict(A__						)

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

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

      with torch.no_grad():
            lowercase__  =	model(**A__						).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(A__						)

            print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
            image_processor.save_pretrained(A__						)

      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__":
       a__   :					Optional[Any] 						=      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."
       )

       a__   :					Optional[int] 						=      parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
