






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    SwinConfig(image_size=1_9_2 )

       if "base" in model_name:
              _UpperCAmelCase							=    6
              _UpperCAmelCase							=    1_2_8
              _UpperCAmelCase							=    (2, 2, 1_8, 2)
              _UpperCAmelCase							=    (4, 8, 1_6, 3_2)
       elif "large" in model_name:
              _UpperCAmelCase							=    1_2
              _UpperCAmelCase							=    1_9_2
              _UpperCAmelCase							=    (2, 2, 1_8, 2)
              _UpperCAmelCase							=    (6, 1_2, 2_4, 4_8)
       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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						int:
       for key in orig_state_dict.copy().keys():
              _UpperCAmelCase							=    orig_state_dict.pop(__snake_case )

              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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						int:
       _UpperCAmelCase							=    torch.load(__snake_case   ,      map_location="""cpu""" )["""model"""]

       _UpperCAmelCase							=    get_swin_config(__snake_case )
       _UpperCAmelCase							=    SwinForMaskedImageModeling(__snake_case )
       model.eval()

       _UpperCAmelCase							=    convert_state_dict(__snake_case   ,      __snake_case )
       model.load_state_dict(__snake_case )

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

       _UpperCAmelCase							=    ViTImageProcessor(size={"""height""": 1_9_2, """width""": 1_9_2} )
       _UpperCAmelCase							=    Image.open(requests.get(__snake_case   ,      stream=__snake_case ).raw )
       _UpperCAmelCase							=    image_processor(images=__snake_case   ,      return_tensors="""pt""" )

       with torch.no_grad():
              _UpperCAmelCase							=    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:     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.'''
  )

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





from pathlib import Path

import fire
from tqdm import tqdm




def 		lowerCAmelCase_ (						lowerCamelCase="ro"						,  lowerCamelCase="en"						,  lowerCamelCase="wmt16"						,  lowerCamelCase=None ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets""" )
				__magic_name__						:						Dict										=F"{src_lang}-{tgt_lang}"
				print(F"Converting {dataset}-{pair}" )
				__magic_name__						:						Dict										=datasets.load_dataset(lowerCamelCase						,  lowerCamelCase )
				if save_dir is None:
								__magic_name__						:						Optional[int]										=F"{dataset}-{pair}"
				__magic_name__						:						int										=Path(lowerCamelCase )
				save_dir.mkdir(exist_ok=lowerCamelCase )

				for split in ds.keys():
								print(F"Splitting {split} with {ds[split].num_rows} records" )

								# to save to val.source, val.target like summary datasets
								__magic_name__						:						Dict										="""val""" if split == """validation""" else split
								__magic_name__						:						List[Any]										=save_dir.joinpath(F"{fn}.source" )
								__magic_name__						:						Optional[int]										=save_dir.joinpath(F"{fn}.target" )
								__magic_name__						:						Optional[Any]										=src_path.open("""w+""" )
								__magic_name__						:						List[Any]										=tgt_path.open("""w+""" )

								# reader is the bottleneck so writing one record at a time doesn't slow things down
								for x in tqdm(ds[split] ):
												__magic_name__						:						str										=x["""translation"""]
												src_fp.write(ex[src_lang] + """\n""" )
												tgt_fp.write(ex[tgt_lang] + """\n""" )

				print(F"Saved {dataset} dataset to {save_dir}" )


if __name__ == "__main__":
							fire.Fire(download_wmt_dataset)




