






'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def 					lowercase__				(   __UpperCamelCase					)->				List[str]:
      UpperCamelCase       						=      SwinConfig(image_size=192					)

      if "base" in model_name:
            UpperCamelCase       						=      6
            UpperCamelCase       						=      128
            UpperCamelCase       						=      (2, 2, 18, 2)
            UpperCamelCase       						=      (4, 8, 16, 32)
      elif "large" in model_name:
            UpperCamelCase       						=      12
            UpperCamelCase       						=      192
            UpperCamelCase       						=      (2, 2, 18, 2)
            UpperCamelCase       						=      (6, 12, 24, 48)
      else:
            raise ValueError("""Model not supported, only supports base and large variants"""					)

      UpperCamelCase       						=      window_size
      UpperCamelCase       						=      embed_dim
      UpperCamelCase       						=      depths
      UpperCamelCase       						=      num_heads

      return config







def 					lowercase__				(   __UpperCamelCase					)->				str:
      if "encoder.mask_token" in name:
            UpperCamelCase       						=      name.replace("""encoder.mask_token"""	,		"""embeddings.mask_token"""					)
      if "encoder.patch_embed.proj" in name:
            UpperCamelCase       						=      name.replace("""encoder.patch_embed.proj"""	,		"""embeddings.patch_embeddings.projection"""					)
      if "encoder.patch_embed.norm" in name:
            UpperCamelCase       						=      name.replace("""encoder.patch_embed.norm"""	,		"""embeddings.norm"""					)
      if "attn.proj" in name:
            UpperCamelCase       						=      name.replace("""attn.proj"""	,		"""attention.output.dense"""					)
      if "attn" in name:
            UpperCamelCase       						=      name.replace("""attn"""	,		"""attention.self"""					)
      if "norm1" in name:
            UpperCamelCase       						=      name.replace("""norm1"""	,		"""layernorm_before"""					)
      if "norm2" in name:
            UpperCamelCase       						=      name.replace("""norm2"""	,		"""layernorm_after"""					)
      if "mlp.fc1" in name:
            UpperCamelCase       						=      name.replace("""mlp.fc1"""	,		"""intermediate.dense"""					)
      if "mlp.fc2" in name:
            UpperCamelCase       						=      name.replace("""mlp.fc2"""	,		"""output.dense"""					)

      if name == "encoder.norm.weight":
            UpperCamelCase       						=      """layernorm.weight"""
      if name == "encoder.norm.bias":
            UpperCamelCase       						=      """layernorm.bias"""

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

      return name







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[str]:
      for key in orig_state_dict.copy().keys():
            UpperCamelCase       						=      orig_state_dict.pop(__UpperCamelCase					)

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

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

      return orig_state_dict







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)["""model"""]

      UpperCamelCase       						=      get_swin_config(__UpperCamelCase					)
      UpperCamelCase       						=      SwinForMaskedImageModeling(__UpperCamelCase					)
      model.eval()

      UpperCamelCase       						=      convert_state_dict(__UpperCamelCase	,		__UpperCamelCase					)
      model.load_state_dict(__UpperCamelCase					)

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

      UpperCamelCase       						=      ViTImageProcessor(size={"""height""": 192, """width""": 192}					)
      UpperCamelCase       						=      Image.open(requests.get(__UpperCamelCase	,		stream=__UpperCamelCase					).raw					)
      UpperCamelCase       						=      image_processor(images=__UpperCamelCase	,		return_tensors="""pt"""					)

      with torch.no_grad():
            UpperCamelCase       						=      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__":
     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'''

def 					lowercase__				(   __UpperCamelCase					)->				str:
      if not all(char in """01""" for char in bin_string					):
            raise ValueError("""Non-binary value was passed to the function"""					)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""					)
      UpperCamelCase       						=      """"""
      while len(__UpperCamelCase					) % 3 != 0:
            UpperCamelCase       						=      """0""" + bin_string
      UpperCamelCase       						=      [
          bin_string[index : index + 3]
          for index in range(len(__UpperCamelCase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            UpperCamelCase       						=      0
            for index, val in enumerate(__UpperCamelCase					):
                  oct_val += int(2 ** (2 - index) * int(__UpperCamelCase					)					)
            oct_string += str(__UpperCamelCase					)
      return oct_string


if __name__ == "__main__":
     from doctest import testmod

     testmod()
