



"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=SwinConfig(image_size=192 )

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

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

   return config





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

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

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

   return name





def      __magic_name__				(							lowercase							,			lowercase ):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: int     			=orig_state_dict.pop(lowercase )

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

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

   return orig_state_dict





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.load(lowercase							,			map_location="""cpu""" )["""model"""]

   SCREAMING_SNAKE_CASE_: Tuple     			=get_swin_config(lowercase )
   SCREAMING_SNAKE_CASE_: str     			=SwinForMaskedImageModeling(lowercase )
   model.eval()

   SCREAMING_SNAKE_CASE_: Tuple     			=convert_state_dict(lowercase							,			lowercase )
   model.load_state_dict(lowercase )

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

   SCREAMING_SNAKE_CASE_: Dict     			=ViTImageProcessor(size={"""height""": 192, """width""": 192} )
   SCREAMING_SNAKE_CASE_: int     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   SCREAMING_SNAKE_CASE_: Tuple     			=image_processor(images=lowercase							,			return_tensors="""pt""" )

   with torch.no_grad():
      SCREAMING_SNAKE_CASE_: Any     			=model(**lowercase ).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(lowercase )

      print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
      image_processor.save_pretrained(lowercase )

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








"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 					a	(  yaml.SafeLoader       ):



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=[self.constructed_objects[key_node] for key_node, _ in node.value]
     SCREAMING_SNAKE_CASE_: Any     			=[tuple(lowerCAmelCase		) if isinstance(lowerCAmelCase       ,		lowerCAmelCase		) else key for key in keys]
     SCREAMING_SNAKE_CASE_: Dict     			=Counter(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=[key for key in counter if counter[key] > 1]
     if duplicate_keys:
        raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''		)





  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]=False		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=super().construct_mapping(lowerCAmelCase       ,		deep=lowerCAmelCase		)
     self._check_no_duplicates_on_constructed_node(lowerCAmelCase		)
     return mapping







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=list(readme_content.splitlines() )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=full_content[1:].index("""---""" ) + 1
      SCREAMING_SNAKE_CASE_: List[str]     			="""\n""".join(full_content[1:sep_idx] )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

   return None, "\n".join(lowercase )
class 					a	(  UpperCAmelCase__       ):
  # class attributes
  UpperCamelCase	:				Tuple     						=					{'train_eval_index'}  # train-eval-index in the YAML metadata



  @classmethod
  def 	lowerCamelCase__       (						cls :					List[Any]       ,		lowerCAmelCase :					Path		)     ->					"DatasetMetadata":


     '''simple docstring'''




     with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_split_yaml_from_readme(readme_file.read()		)
     if yaml_string is not None:
        return cls.from_yaml_string(lowerCAmelCase		)
     else:
        return cls()



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Path		)     ->					List[str]:


     '''simple docstring'''




     if path.exists():
        with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
           SCREAMING_SNAKE_CASE_: str     			=readme_file.read()
     else:
        SCREAMING_SNAKE_CASE_: str     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=self._to_readme(lowerCAmelCase		)
     with open(lowerCAmelCase       ,		"""w"""       ,		encoding="""utf-8"""		) as readme_file:
        readme_file.write(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[str] = None		)     ->					str:


     '''simple docstring'''




     if readme_content is not None:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=_split_yaml_from_readme(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[str]     			="""---\n""" + self.to_yaml_string() + """---\n""" + content
     else:
        SCREAMING_SNAKE_CASE_: List[Any]     			="""---\n""" + self.to_yaml_string() + """---\n"""
     return full_content



  @classmethod
  def 	lowerCamelCase__       (						cls :					Optional[int]       ,		lowerCAmelCase :					str		)     ->					"DatasetMetadata":


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=yaml.load(lowerCAmelCase       ,		Loader=_NoDuplicateSafeLoader		) or {}

     # Convert the YAML keys to DatasetMetadata fields
     SCREAMING_SNAKE_CASE_: List[Any]     			={
         (key.replace("""-"""       ,		"""_"""		) if key.replace("""-"""       ,		"""_"""		) in cls._FIELDS_WITH_DASHES else key): value
         for key, value in metadata_dict.items()
     }
     return cls(**lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     return yaml.safe_dump(
         {
             (key.replace("""_"""       ,		"""-"""		) if key in self._FIELDS_WITH_DASHES else key): value
             for key, value in self.items()
         }       ,		sort_keys=lowerCAmelCase       ,		allow_unicode=lowerCAmelCase       ,		encoding="""utf-8"""       ,		).decode("""utf-8"""		)







_UpperCAmelCase     =			{
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   _UpperCAmelCase     =			ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
   ap.add_argument("""readme_filepath""")
   _UpperCAmelCase     =			ap.parse_args()

   _UpperCAmelCase     =			Path(args.readme_filepath)
   _UpperCAmelCase     =			DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)



