





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[str]						):





  """simple docstring"""



  a__     : Optional[Any]  =SwinConfig(image_size=192						)

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

  a__     : Tuple  =window_size
  a__     : List[Any]  =embed_dim
  a__     : Optional[int]  =depths
  a__     : Union[str, Any]  =num_heads

  return config
def 					_A	(		SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



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

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

  if "decoder" in name:
    pass
  else:
    a__     : Any  ="swin." + name

  return name
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  for key in orig_state_dict.copy().keys():
    a__     : Union[str, Any]  =orig_state_dict.pop(a__						)

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

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

  return orig_state_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  a__     : Union[str, Any]  =torch.load(a__	,	map_location="cpu"						)["model"]

  a__     : Optional[Any]  =get_swin_config(a__						)
  a__     : Any  =SwinForMaskedImageModeling(a__						)
  model.eval()

  a__     : Dict  =convert_state_dict(a__	,	a__						)
  model.load_state_dict(a__						)

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

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

  with torch.no_grad():
    a__     : List[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__":
     UpperCAmelCase     :     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."""
     )

     UpperCAmelCase     :     int							=	parser.parse_args()
     convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)













import warnings

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


UpperCAmelCase     :     Dict							=	logging.get_logger(__name__)

UpperCAmelCase     :     Dict							=	{
    """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : List[Any]            =      """mvp"""
      _lowercase    : Tuple            =      ["""past_key_values"""]
      _lowercase    : Dict            =      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







      def __init__(   self		,				lowerCAmelCase__=5_0_2_6_7		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=4_0_9_6		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=4_0_9_6		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=False		,				lowerCAmelCase__=True		,				lowerCAmelCase__=1		,				lowerCAmelCase__=0		,				lowerCAmelCase__=2		,				lowerCAmelCase__=True		,				lowerCAmelCase__=2		,				lowerCAmelCase__=2		,				lowerCAmelCase__=False		,				lowerCAmelCase__=1_0_0		,				lowerCAmelCase__=8_0_0		,				**lowerCAmelCase__		,				)       ->						str:



        '''simple docstring'''




        a__     : Dict  =vocab_size
        a__     : List[str]  =max_position_embeddings
        a__     : List[str]  =d_model
        a__     : Optional[Any]  =encoder_ffn_dim
        a__     : Dict  =encoder_layers
        a__     : List[str]  =encoder_attention_heads
        a__     : List[str]  =decoder_ffn_dim
        a__     : Tuple  =decoder_layers
        a__     : Tuple  =decoder_attention_heads
        a__     : Any  =dropout
        a__     : str  =attention_dropout
        a__     : str  =activation_dropout
        a__     : Optional[int]  =activation_function
        a__     : Union[str, Any]  =init_std
        a__     : Dict  =encoder_layerdrop
        a__     : List[str]  =decoder_layerdrop
        a__     : Union[str, Any]  =classifier_dropout
        a__     : Union[str, Any]  =use_cache
        a__     : Optional[Any]  =encoder_layers
        a__     : List[str]  =scale_embedding  # scale factor will be sqrt(d_model) if True
        a__     : int  =use_prompt
        a__     : Union[str, Any]  =prompt_length
        a__     : Dict  =prompt_mid_dim

        super().__init__(
            pad_token_id=lowerCAmelCase__		,				bos_token_id=lowerCAmelCase__		,				eos_token_id=lowerCAmelCase__		,				is_encoder_decoder=lowerCAmelCase__		,				decoder_start_token_id=lowerCAmelCase__		,				forced_eos_token_id=lowerCAmelCase__		,				**lowerCAmelCase__		,				)

        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"		,				lowerCAmelCase__							):
          a__     : Tuple  =self.bos_token_id
          warnings.warn(
              F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
              "The config can simply be saved and uploaded again to be fixed."							)






