






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  SwinConfig(image_size=192)

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

   UpperCamelCase_						=  window_size
   UpperCamelCase_						=  embed_dim
   UpperCamelCase_						=  depths
   UpperCamelCase_						=  num_heads

   return config






def      _snake_case     (__lowercase):
   if "encoder.mask_token" in name:
      UpperCamelCase_						=  name.replace('encoder.mask_token'     ,   'embeddings.mask_token')
   if "encoder.patch_embed.proj" in name:
      UpperCamelCase_						=  name.replace('encoder.patch_embed.proj'     ,   'embeddings.patch_embeddings.projection')
   if "encoder.patch_embed.norm" in name:
      UpperCamelCase_						=  name.replace('encoder.patch_embed.norm'     ,   'embeddings.norm')
   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 name == "encoder.norm.weight":
      UpperCamelCase_						=  'layernorm.weight'
   if name == "encoder.norm.bias":
      UpperCamelCase_						=  'layernorm.bias'

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

   return name






def      _snake_case     (__lowercase     ,   __lowercase):
   for key in orig_state_dict.copy().keys():
      UpperCamelCase_						=  orig_state_dict.pop(__lowercase)

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

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

   return orig_state_dict






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  torch.load(__lowercase     ,   map_location='cpu')['model']

   UpperCamelCase_						=  get_swin_config(__lowercase)
   UpperCamelCase_						=  SwinForMaskedImageModeling(__lowercase)
   model.eval()

   UpperCamelCase_						=  convert_state_dict(__lowercase     ,   __lowercase)
   model.load_state_dict(__lowercase)

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

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

   with torch.no_grad():
      UpperCamelCase_						=  model(**__lowercase).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(__lowercase)

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

   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__":
 snake_case__   :						List[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."""
 )

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












def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Dict   					=						''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	dict[str, str]:
    lowerCamelCase__       : List[str]   					=						[chr(i + 65					) for i in range(26					)]
    # Remove duplicate characters from key
    lowerCamelCase__       : Any   					=						remove_duplicates(key.upper()					)
    lowerCamelCase__       : List[Any]   					=						len(_UpperCAmelCase					)
    # First fill cipher with key characters
    lowerCamelCase__       : Optional[int]   					=						{alphabet[i]: char for i, char in enumerate(_UpperCAmelCase					)}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase					)		,      26					):
        lowerCamelCase__       : Any   					=						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            lowerCamelCase__       : Union[str, Any]   					=						alphabet[i - offset]
        lowerCamelCase__       : int   					=						char
    return cipher_alphabet


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    return "".join(cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : List[str]   					=						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    lowerCamelCase__       : str   					=						input('Enter message to encode or decode: '					).strip()
    lowerCamelCase__       : Union[str, Any]   					=						input('Enter keyword: '					).strip()
    lowerCamelCase__       : Dict   					=						input('Encipher or decipher? E/D:'					).strip()[0].lower()
    try:
        lowerCamelCase__       : Dict   					=						{'e': encipher, 'd': decipher}[option]
    except KeyError:
        raise KeyError('invalid input option'					)
    lowerCamelCase__       : int   					=						create_cipher_map(_UpperCAmelCase					)
    print(func(_UpperCAmelCase		,      _UpperCAmelCase					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





