"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Tuple:
   lowercase__:   List[Any]					=      s.rsplit(__UpperCAmelCase       ,    __UpperCAmelCase )
   return new.join(__UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Dict:
   # encoder.embeddings are double copied in original FLAVA
   return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Union[str, Any]:
   lowercase__:   Any					=      {}

   lowercase__:   List[Any]					=      ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
   for key, value in state_dict.items():
      for group_key in group_keys:
         if group_key in key:
            lowercase__:   List[Any]					=      key.replace(F"""{group_key}."""       ,    F"""{group_key}.group.""" )

      if "res_path" in key:
         lowercase__:   Any					=      key.replace('''res_path.'''       ,    '''res_path.path.''' )

      if key.endswith('''.w''' ):
         lowercase__:   Optional[Any]					=      rreplace(__UpperCAmelCase       ,    '''.w'''       ,    '''.weight'''       ,    1 )
      if key.endswith('''.b''' ):
         lowercase__:   List[str]					=      rreplace(__UpperCAmelCase       ,    '''.b'''       ,    '''.bias'''       ,    1 )

      lowercase__:   Dict					=      value.float()

   return upgrade



@torch.no_grad()
def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=True )  ->  List[str]:
   from dall_e import Encoder

   lowercase__:   int					=      Encoder()
   if os.path.exists(__UpperCAmelCase ):
      lowercase__:   str					=      torch.load(__UpperCAmelCase )
   else:
      lowercase__:   List[Any]					=      torch.hub.load_state_dict_from_url(__UpperCAmelCase )

   if isinstance(__UpperCAmelCase       ,    __UpperCAmelCase ):
      lowercase__:   int					=      ckpt.state_dict()
   encoder.load_state_dict(__UpperCAmelCase )

   if config_path is not None:
      lowercase__:   List[Any]					=      FlavaImageCodebookConfig.from_pretrained(__UpperCAmelCase )
   else:
      lowercase__:   int					=      FlavaImageCodebookConfig()

   lowercase__:   Tuple					=      FlavaImageCodebook(__UpperCAmelCase ).eval()
   lowercase__:   Optional[Any]					=      encoder.state_dict()

   lowercase__:   str					=      upgrade_state_dict(__UpperCAmelCase )
   hf_model.load_state_dict(__UpperCAmelCase )
   lowercase__:   List[Any]					=      hf_model.state_dict()
   lowercase__:   Tuple					=      count_parameters(__UpperCAmelCase )
   lowercase__:   Any					=      count_parameters(__UpperCAmelCase )

   assert torch.allclose(__UpperCAmelCase       ,    __UpperCAmelCase       ,    atol=1e-3 )

   if save_checkpoint:
      hf_model.save_pretrained(__UpperCAmelCase )
   else:
      return hf_state_dict


if __name__ == "__main__":
       __A			   = argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       __A			   = parser.parse_args()

       convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

"""simple docstring"""






from __future__ import annotations

from math import pi, sqrt



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  tuple:

   if inductance <= 0:
      raise ValueError('''Inductance cannot be 0 or negative''' )

   elif capacitance <= 0:
      raise ValueError('''Capacitance cannot be 0 or negative''' )

   else:
      return (
          "Resonant frequency",
          float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
