



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def 				lowerCamelCase__	(						_A    ):
  a						: List[str]					       =					SwinConfig(image_size=192    )

  if "base" in model_name:
    a						: Dict					       =					6
    a						: int					       =					128
    a						: Any					       =					(2, 2, 18, 2)
    a						: Tuple					       =					(4, 8, 16, 32)
  elif "large" in model_name:
    a						: int					       =					12
    a						: str					       =					192
    a						: Optional[Any]					       =					(2, 2, 18, 2)
    a						: str					       =					(6, 12, 24, 48)
  else:
    raise ValueError('Model not supported, only supports base and large variants'    )

  a						: Tuple					       =					window_size
  a						: Any					       =					embed_dim
  a						: Optional[Any]					       =					depths
  a						: Union[str, Any]					       =					num_heads

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

  if name == "encoder.norm.weight":
    a						: Tuple					       =					'layernorm.weight'
  if name == "encoder.norm.bias":
    a						: Optional[Any]					       =					'layernorm.bias'

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

  return name
def 				lowerCamelCase__	(						_A			,      _A    ):
  for key in orig_state_dict.copy().keys():
    a						: int					       =					orig_state_dict.pop(_A    )

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

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

  return orig_state_dict
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: Dict					       =					torch.load(_A			,      map_location='cpu'    )['model']

  a						: Union[str, Any]					       =					get_swin_config(_A    )
  a						: Optional[int]					       =					SwinForMaskedImageModeling(_A    )
  model.eval()

  a						: int					       =					convert_state_dict(_A			,      _A    )
  model.load_state_dict(_A    )

  a						: Optional[int]					       =					'http://images.cocodataset.org/val2017/000000039769.jpg'

  a						: Union[str, Any]					       =					ViTImageProcessor(size={'height': 192, 'width': 192}    )
  a						: List[str]					       =					Image.open(requests.get(_A			,      stream=_A    ).raw    )
  a						: Optional[Any]					       =					image_processor(images=_A			,      return_tensors='pt'    )

  with torch.no_grad():
    a						: Union[str, Any]					       =					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__":
     lowerCAmelCase: Dict							=						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: Optional[int]							=						parser.parse_args()
     convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """encoder-decoder"""
    lowercase__			=   True







    def __init__(				self			:  Dict						,      **__snake_case			:  Union[str, Any]     ):
      super().__init__(**__snake_case     )
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      a						: List[str]					       =					kwargs.pop('encoder'     )
      a						: Optional[Any]					       =					encoder_config.pop('model_type'     )
      a						: Tuple					       =					kwargs.pop('decoder'     )
      a						: Optional[int]					       =					decoder_config.pop('model_type'     )

      from ..auto.configuration_auto import AutoConfig

      a						: Any					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Optional[int]					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Tuple					       =					True







    @classmethod
    def       lowercase_	(				cls			:  int						,      __snake_case			:  PretrainedConfig						,      __snake_case			:  PretrainedConfig						,      **__snake_case			:  Union[str, Any]     ):
      logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'     )
      a						: List[Any]					       =					True
      a						: Tuple					       =					True

      return cls(encoder=encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					copy.deepcopy(self.__dict__     )
      a						: List[str]					       =					self.encoder.to_dict()
      a						: Optional[int]					       =					self.decoder.to_dict()
      a						: Optional[Any]					       =					self.__class__.model_type
      return output