



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    SwinConfig(image_size=1_92							)

   if "base" in model_name:
      __lowerCamelCase													=    6
      __lowerCamelCase													=    1_28
      __lowerCamelCase													=    (2, 2, 18, 2)
      __lowerCamelCase													=    (4, 8, 16, 32)
   elif "large" in model_name:
      __lowerCamelCase													=    12
      __lowerCamelCase													=    1_92
      __lowerCamelCase													=    (2, 2, 18, 2)
      __lowerCamelCase													=    (6, 12, 24, 48)
   else:
      raise ValueError('''Model not supported, only supports base and large variants'''							)

   __lowerCamelCase													=    window_size
   __lowerCamelCase													=    embed_dim
   __lowerCamelCase													=    depths
   __lowerCamelCase													=    num_heads

   return config





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

   if name == "encoder.norm.weight":
      __lowerCamelCase													=    '''layernorm.weight'''
   if name == "encoder.norm.bias":
      __lowerCamelCase													=    '''layernorm.bias'''

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

   return name





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Optional[int]							):
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase													=    orig_state_dict.pop(_UpperCamelCase							)

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

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

   return orig_state_dict





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: int ,_UpperCamelCase			: Optional[int]							):
   __lowerCamelCase													=    torch.load(_UpperCamelCase ,map_location='''cpu'''							)['''model''']

   __lowerCamelCase													=    get_swin_config(_UpperCamelCase							)
   __lowerCamelCase													=    SwinForMaskedImageModeling(_UpperCamelCase							)
   model.eval()

   __lowerCamelCase													=    convert_state_dict(_UpperCamelCase ,_UpperCamelCase							)
   model.load_state_dict(_UpperCamelCase							)

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

   __lowerCamelCase													=    ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92}							)
   __lowerCamelCase													=    Image.open(requests.get(_UpperCamelCase ,stream=_UpperCamelCase							).raw							)
   __lowerCamelCase													=    image_processor(images=_UpperCamelCase ,return_tensors='''pt'''							)

   with torch.no_grad():
      __lowerCamelCase													=    model(**_UpperCamelCase							).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(_UpperCamelCase							)

      print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
      image_processor.save_pretrained(_UpperCamelCase							)

   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__":
  a_			=					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."""
  )

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











from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_			=					logging.get_logger(__name__)

a_			=					{
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """poolformer"""




 def __init__(						self	,				__UpperCAmelCase=3	,				__UpperCAmelCase=16	,				__UpperCAmelCase=16	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4.0	,				__UpperCAmelCase=[2, 2, 6, 2]	,				__UpperCAmelCase=[64, 128, 320, 512]	,				__UpperCAmelCase=[7, 3, 3, 3]	,				__UpperCAmelCase=[4, 2, 2, 2]	,				__UpperCAmelCase=[2, 1, 1, 1]	,				__UpperCAmelCase=4	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=True	,				__UpperCAmelCase=1E-5	,				__UpperCAmelCase=0.02	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    num_channels
    __lowerCamelCase													=    patch_size
    __lowerCamelCase													=    stride
    __lowerCamelCase													=    padding
    __lowerCamelCase													=    pool_size
    __lowerCamelCase													=    hidden_sizes
    __lowerCamelCase													=    mlp_ratio
    __lowerCamelCase													=    depths
    __lowerCamelCase													=    patch_sizes
    __lowerCamelCase													=    strides
    __lowerCamelCase													=    num_encoder_blocks
    __lowerCamelCase													=    drop_path_rate
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    use_layer_scale
    __lowerCamelCase													=    layer_scale_init_value
    __lowerCamelCase													=    initializer_range
    super().__init__(**__UpperCAmelCase				)



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  version.parse("""1.11"""   )




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return OrderedDict(
        [
            ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
        ]				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return 2E-3






