
"""simple docstring"""





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 				lowerCamelCase__		(      __snake_case     )							->		List[Any]:


     """simple docstring"""





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

     return name



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


     """simple docstring"""





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

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

          else:
               _UpperCamelCase										=  val

     return orig_state_dict



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Tuple:


     """simple docstring"""





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

     _UpperCamelCase										=  ViTMAEForPreTraining(__snake_case     )

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

     _UpperCamelCase										=  ViTMAEImageProcessor(size=config.image_size     )

     _UpperCamelCase										=  convert_state_dict(__snake_case,					__snake_case     )

     model.load_state_dict(__snake_case     )
     model.eval()

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

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

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

     if "large" in checkpoint_url:
          _UpperCamelCase										=  torch.tensor(
              [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]     )
     elif "huge" in checkpoint_url:
          _UpperCamelCase										=  torch.tensor(
              [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]     )
     else:
          _UpperCamelCase										=  torch.tensor(
              [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]     )

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

     print(F'''Saving model to {pytorch_dump_folder_path}'''     )
     model.save_pretrained(__snake_case     )

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


if __name__ == "__main__":
    _a														=		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."""
    )

    _a														=		parser.parse_args()
    convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








"""simple docstring"""





from __future__ import annotations



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					if len(__snake_case     ) <= 1 or n <= 1:
										return

					insert_next(__snake_case,					n - 1     )
					rec_insertion_sort(__snake_case,					n - 1     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					if index >= len(__snake_case     ) or collection[index - 1] <= collection[index]:
										return

					# Swaps adjacent elements since they are not in ascending order
					_UpperCamelCase			,  _UpperCamelCase										=  (
					    collection[index],
					    collection[index - 1],
					)

					insert_next(__snake_case,					index + 1     )


if __name__ == "__main__":
				_a														=		input("""Enter integers separated by spaces: """)
				_a														=		[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)






