




"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]					):
  """simple docstring"""

  _snake_case							:	str			=						SwinConfig(image_size=1_92					)

  if "base" in model_name:
    _snake_case							:	str			=						6
    _snake_case							:	Any			=						1_28
    _snake_case							:	Optional[Any]			=						(2, 2, 18, 2)
    _snake_case							:	Any			=						(4, 8, 16, 32)
  elif "large" in model_name:
    _snake_case							:	Optional[int]			=						12
    _snake_case							:	Optional[int]			=						1_92
    _snake_case							:	int			=						(2, 2, 18, 2)
    _snake_case							:	Any			=						(6, 12, 24, 48)
  else:
    raise ValueError("""Model not supported, only supports base and large variants"""					)

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

  return config


def       UpperCAmelCase__ (snake_case__							:      str					):
  """simple docstring"""

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

  if name == "encoder.norm.weight":
    _snake_case							:	List[str]			=						"""layernorm.weight"""
  if name == "encoder.norm.bias":
    _snake_case							:	int			=						"""layernorm.bias"""

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

  return name


def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  for key in orig_state_dict.copy().keys():
    _snake_case							:	int			=						orig_state_dict.pop(snake_case__					)

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

      if "weight" in key:
        _snake_case							:	Any			=						val[:dim, :]
        _snake_case							:	Any			=						val[
            dim : dim * 2, :
        ]
        _snake_case							:	Dict			=						val[-dim:, :]
      else:
        _snake_case							:	Tuple			=						val[
            :dim
        ]
        _snake_case							:	Optional[Any]			=						val[
            dim : dim * 2
        ]
        _snake_case							:	Union[str, Any]			=						val[
            -dim:
        ]
    else:
      _snake_case							:	str			=						val

  return orig_state_dict


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      Tuple	,     snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case							:	Optional[int]			=						torch.load(snake_case__	,     map_location="""cpu"""					)["""model"""]

  _snake_case							:	Tuple			=						get_swin_config(snake_case__					)
  _snake_case							:	str			=						SwinForMaskedImageModeling(snake_case__					)
  model.eval()

  _snake_case							:	Tuple			=						convert_state_dict(snake_case__	,     snake_case__					)
  model.load_state_dict(snake_case__					)

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

  _snake_case							:	Dict			=						ViTImageProcessor(size={"""height""": 1_92, """width""": 1_92}					)
  _snake_case							:	int			=						Image.open(requests.get(snake_case__	,     stream=snake_case__					).raw					)
  _snake_case							:	Tuple			=						image_processor(images=snake_case__	,     return_tensors="""pt"""					)

  with torch.no_grad():
    _snake_case							:	Any			=						model(**snake_case__					).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(snake_case__					)

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

  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)








"""simple docstring"""


def       UpperCAmelCase__ (snake_case__							:      int					):
  """simple docstring"""

  if not isinstance(snake_case__	,     snake_case__					) or number < 0:
    raise ValueError("""Input must be a non-negative integer"""					)

  _snake_case							:	Dict			=						0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
     import doctest

     doctest.testmod()


