



'''simple docstring'''






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 torch

from transformers import AutoModel

class 							a__( torch.nn.Module     ):







    def __init__(				self			:  Any						,      __snake_case			:  str="sayef/fsner-bert-base-uncased"     ):
      super(__snake_case						,      self     ).__init__()

      a						: List[str]					       =					AutoModel.from_pretrained(__snake_case						,      return_dict=__snake_case     )
      a						: Optional[Any]					       =					torch.nn.CosineSimilarity(3						,      1e-0_8     )
      a						: Tuple					       =					torch.nn.Softmax(dim=1     )







    def       lowercase_	(				self			:  List[str]						,      **__snake_case			:  int     ):
      return self.bert(**__snake_case     ).last_hidden_state







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]     ):
      return token_embeddings.sum(2						,      keepdim=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]=1     ):
      return self.softmax(T * self.cos(__snake_case						,      __snake_case     )     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  int						,      __snake_case			:  Tuple     ):
      a						: List[Any]					       =					W_supports['sizes'].tolist()
      a						: Any					       =					W_supports['start_token_id'].item()
      a						: int					       =					W_supports['end_token_id'].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      a						: Optional[Any]					       =					self.BERT(**__snake_case     )
      a						: Tuple					       =					self.BERT(**__snake_case     )

      a						: Dict					       =					None
      a						: Optional[Any]					       =					None

      a						: Union[str, Any]					       =					W_supports['input_ids'] == start_token_id
      a						: str					       =					W_supports['input_ids'] == end_token_id

      for i, size in enumerate(__snake_case     ):
        if i == 0:
          a						: Optional[int]					       =					0
        else:
          a						: Tuple					       =					support_sizes[i - 1]

        a						: Tuple					       =					S[s : s + size][start_token_masks[s : s + size]]
        a						: int					       =					S[s : s + size][end_token_masks[s : s + size]]

        a						: Any					       =					torch.matmul(q[i]						,      s_start.T     ).sum(1     ).softmax(0     )
        a						: List[str]					       =					torch.matmul(q[i]						,      s_end.T     ).sum(1     ).softmax(0     )

        if p_starts is not None:
          a						: List[Any]					       =					torch.vstack((p_starts, p_start)     )
          a						: List[Any]					       =					torch.vstack((p_ends, p_end)     )
        else:
          a						: List[Any]					       =					p_start
          a						: Optional[int]					       =					p_end

      return p_starts, p_ends