






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def       __lowerCAmelCase      (						__snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case       ):
     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       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     for key in orig_state_dict.copy().keys():
          __lowerCAmelCase								=					orig_state_dict.pop(__snake_case       )

          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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					torch.load(__snake_case					, map_location="cpu"       )["model"]

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

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

     __lowerCAmelCase								=					"http://images.cocodataset.org/val2017/000000039769.jpg"

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

     with torch.no_grad():
          __lowerCAmelCase								=					model(**__snake_case       ).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(__snake_case       )

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

     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__":
 lowerCamelCase			:					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.'''
 )

 lowerCamelCase			:					Dict         =	parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)














import random



class 							_UpperCamelCase			:


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase	)->	tuple[list[int], list[int]]:
           __lowerCAmelCase								=					[ord(__UpperCamelCase	) for i in text]
           __lowerCAmelCase								=					[]
           __lowerCAmelCase								=					[]
           for i in plain:
                __lowerCAmelCase								=					random.randint(1					,						3_0_0	)
                __lowerCAmelCase								=					(i + k) * k
                cipher.append(__UpperCamelCase	)
                key.append(__UpperCamelCase	)
           return cipher, key


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					[]
           for i in range(len(__UpperCamelCase	)	):
                __lowerCAmelCase								=					int((cipher[i] - (key[i]) ** 2) / key[i]	)
                plain.append(chr(__UpperCamelCase	)	)
           return "".join(__UpperCamelCase	)


if __name__ == "__main__":
 lowerCamelCase      ,					lowerCamelCase			:					List[Any]         =	Onepad().encrypt('''Hello''')
 print(c, k)
 print(Onepad().decrypt(c, k))






