

'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any    )				->      Dict:
     """simple docstring"""




     if "cls_token" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('cls_token'							,			'vit.embeddings.cls_token'    )
     if "mask_token" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('mask_token'							,			'decoder.mask_token'    )
     if "decoder_pos_embed" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('decoder_pos_embed'							,			'decoder.decoder_pos_embed'    )
     if "pos_embed" in name and "decoder" not in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('pos_embed'							,			'vit.embeddings.position_embeddings'    )
     if "patch_embed.proj" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('patch_embed.proj'							,			'vit.embeddings.patch_embeddings.projection'    )
     if "patch_embed.norm" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('patch_embed.norm'							,			'vit.embeddings.norm'    )
     if "decoder_blocks" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('decoder_blocks'							,			'decoder.decoder_layers'    )
     if "blocks" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('blocks'							,			'vit.encoder.layer'    )
     if "attn.proj" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('attn.proj'							,			'attention.output.dense'    )
     if "attn" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('attn'							,			'attention.self'    )
     if "norm1" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('norm1'							,			'layernorm_before'    )
     if "norm2" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('norm2'							,			'layernorm_after'    )
     if "mlp.fc1" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('mlp.fc1'							,			'intermediate.dense'    )
     if "mlp.fc2" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('mlp.fc2'							,			'output.dense'    )
     if "decoder_embed" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('decoder_embed'							,			'decoder.decoder_embed'    )
     if "decoder_norm" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('decoder_norm'							,			'decoder.decoder_norm'    )
     if "decoder_pred" in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('decoder_pred'							,			'decoder.decoder_pred'    )
     if "norm.weight" in name and "decoder" not in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('norm.weight'							,			'vit.layernorm.weight'    )
     if "norm.bias" in name and "decoder" not in name:
          _SCREAMING_SNAKE_CASE							   =name.replace('norm.bias'							,			'vit.layernorm.bias'    )

     return name


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					str    )				->      List[str]:
     """simple docstring"""




     for key in orig_state_dict.copy().keys():
          _SCREAMING_SNAKE_CASE							   =orig_state_dict.pop(_UpperCamelCase    )

          if "qkv" in key:
               _SCREAMING_SNAKE_CASE							   =key.split('.'    )
               _SCREAMING_SNAKE_CASE							   =int(key_split[1]    )
               if "decoder_blocks" in key:
                    _SCREAMING_SNAKE_CASE							   =config.decoder_hidden_size
                    _SCREAMING_SNAKE_CASE							   ='decoder.decoder_layers.'
                    if "weight" in key:
                         _SCREAMING_SNAKE_CASE							   =val[:dim, :]
                         _SCREAMING_SNAKE_CASE							   =val[dim : dim * 2, :]
                         _SCREAMING_SNAKE_CASE							   =val[-dim:, :]
                    elif "bias" in key:
                         _SCREAMING_SNAKE_CASE							   =val[:dim]
                         _SCREAMING_SNAKE_CASE							   =val[dim : dim * 2]
                         _SCREAMING_SNAKE_CASE							   =val[-dim:]
               else:
                    _SCREAMING_SNAKE_CASE							   =config.hidden_size
                    _SCREAMING_SNAKE_CASE							   ='vit.encoder.layer.'
                    if "weight" in key:
                         _SCREAMING_SNAKE_CASE							   =val[:dim, :]
                         _SCREAMING_SNAKE_CASE							   =val[dim : dim * 2, :]
                         _SCREAMING_SNAKE_CASE							   =val[-dim:, :]
                    elif "bias" in key:
                         _SCREAMING_SNAKE_CASE							   =val[:dim]
                         _SCREAMING_SNAKE_CASE							   =val[dim : dim * 2]
                         _SCREAMING_SNAKE_CASE							   =val[-dim:]

          else:
               _SCREAMING_SNAKE_CASE							   =val

     return orig_state_dict


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					int    )				->      Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =ViTMAEConfig()
     if "large" in checkpoint_url:
          _SCREAMING_SNAKE_CASE							   =10_24
          _SCREAMING_SNAKE_CASE							   =40_96
          _SCREAMING_SNAKE_CASE							   =24
          _SCREAMING_SNAKE_CASE							   =16
     elif "huge" in checkpoint_url:
          _SCREAMING_SNAKE_CASE							   =14
          _SCREAMING_SNAKE_CASE							   =12_80
          _SCREAMING_SNAKE_CASE							   =51_20
          _SCREAMING_SNAKE_CASE							   =32
          _SCREAMING_SNAKE_CASE							   =16

     _SCREAMING_SNAKE_CASE							   =ViTMAEForPreTraining(_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =torch.hub.load_state_dict_from_url(_UpperCamelCase							,			map_location='cpu'    )['model']

     _SCREAMING_SNAKE_CASE							   =ViTMAEImageProcessor(size=config.image_size    )

     _SCREAMING_SNAKE_CASE							   =convert_state_dict(_UpperCamelCase							,			_UpperCamelCase    )

     model.load_state_dict(_UpperCamelCase    )
     model.eval()

     _SCREAMING_SNAKE_CASE							   ='https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

     _SCREAMING_SNAKE_CASE							   =Image.open(requests.get(_UpperCamelCase							,			stream=_UpperCamelCase    ).raw    )
     _SCREAMING_SNAKE_CASE							   =ViTMAEImageProcessor(size=config.image_size    )
     _SCREAMING_SNAKE_CASE							   =image_processor(images=_UpperCamelCase							,			return_tensors='pt'    )

     # forward pass
     torch.manual_seed(2    )
     _SCREAMING_SNAKE_CASE							   =model(**_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =outputs.logits

     if "large" in checkpoint_url:
          _SCREAMING_SNAKE_CASE							   =torch.tensor(
              [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]    )
     elif "huge" in checkpoint_url:
          _SCREAMING_SNAKE_CASE							   =torch.tensor(
              [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]    )
     else:
          _SCREAMING_SNAKE_CASE							   =torch.tensor(
              [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]    )

     # verify logits
     assert torch.allclose(logits[0, :3, :3]							,			_UpperCamelCase							,			atol=1E-4    )

     print(f"Saving model to {pytorch_dump_folder_path}"    )
     model.save_pretrained(_UpperCamelCase    )

     print(f"Saving image processor to {pytorch_dump_folder_path}"    )
     image_processor.save_pretrained(_UpperCamelCase    )


if __name__ == "__main__":
     lowerCamelCase			:				Dict 			=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
         type=str,
         help="URL of the checkpoint you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )

     lowerCamelCase			:				Any 			=					parser.parse_args()
     convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''
from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt


def 	_lowerCAmelCase (						_UpperCamelCase						:					int    )				->      bool:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =int(number**0.5    )
     return number == sq * sq


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					int    )				->      tuple[int, int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
     _SCREAMING_SNAKE_CASE							   =x_den * y_den * z_den
     _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
     top //= hcf
     bottom //= hcf
     return top, bottom


def 	_lowerCAmelCase (						_UpperCamelCase						:					int = 35    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =set()
     _SCREAMING_SNAKE_CASE							   =42
     _SCREAMING_SNAKE_CASE							   =Fraction(0    )
     _SCREAMING_SNAKE_CASE							   =42

     for x_num in range(1							,			order + 1    ):
          for x_den in range(x_num + 1							,			order + 1    ):
               for y_num in range(1							,			order + 1    ):
                    for y_den in range(y_num + 1							,			order + 1    ):
                         # n=1
                         _SCREAMING_SNAKE_CASE							   =x_num * y_den + x_den * y_num
                         _SCREAMING_SNAKE_CASE							   =x_den * y_den
                         _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              _SCREAMING_SNAKE_CASE							   =add_three(
                                  _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
                              unique_s.add(_UpperCamelCase    )

                         # n=2
                         _SCREAMING_SNAKE_CASE							   =(
                             x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                         )
                         _SCREAMING_SNAKE_CASE							   =x_den * x_den * y_den * y_den
                         if is_sq(_UpperCamelCase    ) and is_sq(_UpperCamelCase    ):
                              _SCREAMING_SNAKE_CASE							   =int(sqrt(_UpperCamelCase    )    )
                              _SCREAMING_SNAKE_CASE							   =int(sqrt(_UpperCamelCase    )    )
                              _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   _SCREAMING_SNAKE_CASE							   =add_three(
                                       _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
                                   unique_s.add(_UpperCamelCase    )

                    # n=-1
                         _SCREAMING_SNAKE_CASE							   =x_num * y_num
                         _SCREAMING_SNAKE_CASE							   =x_den * y_num + x_num * y_den
                         _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              _SCREAMING_SNAKE_CASE							   =add_three(
                                  _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
                              unique_s.add(_UpperCamelCase    )

                         # n=2
                         _SCREAMING_SNAKE_CASE							   =x_num * x_num * y_num * y_num
                         _SCREAMING_SNAKE_CASE							   =(
                             x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                         )
                         if is_sq(_UpperCamelCase    ) and is_sq(_UpperCamelCase    ):
                              _SCREAMING_SNAKE_CASE							   =int(sqrt(_UpperCamelCase    )    )
                              _SCREAMING_SNAKE_CASE							   =int(sqrt(_UpperCamelCase    )    )
                              _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   _SCREAMING_SNAKE_CASE							   =add_three(
                                       _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
                                   unique_s.add(_UpperCamelCase    )

     for num, den in unique_s:
          total += Fraction(_UpperCamelCase							,			_UpperCamelCase    )

     return total.denominator + total.numerator


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