






'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 							lowercase_				(    __A :       Union[str, Any]			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   str				  =SwinConfig(image_size=1_9_2			)

 if "base" in model_name:
  lowercase					:   Dict				  =6
  lowercase					:   Union[str, Any]				  =1_2_8
  lowercase					:   int				  =(2, 2, 1_8, 2)
  lowercase					:   str				  =(4, 8, 1_6, 3_2)
 elif "large" in model_name:
  lowercase					:   List[str]				  =1_2
  lowercase					:   Optional[Any]				  =1_9_2
  lowercase					:   int				  =(2, 2, 1_8, 2)
  lowercase					:   int				  =(6, 1_2, 2_4, 4_8)
 else:
  raise ValueError('''Model not supported, only supports base and large variants'''			)

 lowercase					:   Union[str, Any]				  =window_size
 lowercase					:   List[Any]				  =embed_dim
 lowercase					:   Union[str, Any]				  =depths
 lowercase					:   List[Any]				  =num_heads

 return config


def 							lowercase_				(    __A :       Tuple			)       ->      List[str]:



 """simple docstring"""




 if "encoder.mask_token" in name:
  lowercase					:   List[Any]				  =name.replace('''encoder.mask_token'''					,      '''embeddings.mask_token'''			)
 if "encoder.patch_embed.proj" in name:
  lowercase					:   List[Any]				  =name.replace('''encoder.patch_embed.proj'''					,      '''embeddings.patch_embeddings.projection'''			)
 if "encoder.patch_embed.norm" in name:
  lowercase					:   Union[str, Any]				  =name.replace('''encoder.patch_embed.norm'''					,      '''embeddings.norm'''			)
 if "attn.proj" in name:
  lowercase					:   Optional[Any]				  =name.replace('''attn.proj'''					,      '''attention.output.dense'''			)
 if "attn" in name:
  lowercase					:   Dict				  =name.replace('''attn'''					,      '''attention.self'''			)
 if "norm1" in name:
  lowercase					:   Any				  =name.replace('''norm1'''					,      '''layernorm_before'''			)
 if "norm2" in name:
  lowercase					:   int				  =name.replace('''norm2'''					,      '''layernorm_after'''			)
 if "mlp.fc1" in name:
  lowercase					:   int				  =name.replace('''mlp.fc1'''					,      '''intermediate.dense'''			)
 if "mlp.fc2" in name:
  lowercase					:   List[str]				  =name.replace('''mlp.fc2'''					,      '''output.dense'''			)

 if name == "encoder.norm.weight":
  lowercase					:   List[Any]				  ='''layernorm.weight'''
 if name == "encoder.norm.bias":
  lowercase					:   Union[str, Any]				  ='''layernorm.bias'''

 if "decoder" in name:
  pass
 else:
  lowercase					:   Optional[int]				  ='''swin.''' + name

 return name


def 							lowercase_				(    __A :       Optional[Any]					,      __A :       List[Any]			)       ->      Any:



 """simple docstring"""




 for key in orig_state_dict.copy().keys():
  lowercase					:   Tuple				  =orig_state_dict.pop(__A			)

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

   if "weight" in key:
    lowercase					:   Tuple				  =val[:dim, :]
    lowercase					:   str				  =val[
        dim : dim * 2, :
    ]
    lowercase					:   Optional[int]				  =val[-dim:, :]
   else:
    lowercase					:   int				  =val[
        :dim
    ]
    lowercase					:   List[Any]				  =val[
        dim : dim * 2
    ]
    lowercase					:   Optional[Any]				  =val[
        -dim:
    ]
  else:
   lowercase					:   Optional[Any]				  =val

 return orig_state_dict


def 							lowercase_				(    __A :       List[str]					,      __A :       str					,      __A :       str					,      __A :       List[Any]			)       ->      int:



 """simple docstring"""




 lowercase					:   Any				  =torch.load(__A					,      map_location='''cpu'''			)['''model''']

 lowercase					:   Optional[int]				  =get_swin_config(__A			)
 lowercase					:   Optional[Any]				  =SwinForMaskedImageModeling(__A			)
 model.eval()

 lowercase					:   Optional[Any]				  =convert_state_dict(__A					,      __A			)
 model.load_state_dict(__A			)

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

 lowercase					:   Union[str, Any]				  =ViTImageProcessor(size={'''height''': 1_9_2, '''width''': 1_9_2}			)
 lowercase					:   Any				  =Image.open(requests.get(__A					,      stream=__A			).raw			)
 lowercase					:   Any				  =image_processor(images=__A					,      return_tensors='''pt'''			)

 with torch.no_grad():
  lowercase					:   Tuple				  =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__":
      SCREAMING_SNAKE_CASE						=	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.'
      )

      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						=	{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MegaForCausalLM',
          'MegaForMaskedLM',
          'MegaForMultipleChoice',
          'MegaForQuestionAnswering',
          'MegaForSequenceClassification',
          'MegaForTokenClassification',
          'MegaModel',
          'MegaPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mega import (
                MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegaForCausalLM,
                MegaForMaskedLM,
                MegaForMultipleChoice,
                MegaForQuestionAnswering,
                MegaForSequenceClassification,
                MegaForTokenClassification,
                MegaModel,
                MegaPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



