






"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def     snake_case						(A_   :Any			,    A_   :int			,    A_   :Optional[int]			,    A_   :Optional[int]   ):







  '''simple docstring'''

  a  :    int       =     s.rsplit(A_			,    A_   )
  return new.join(A_   )




def     snake_case						(A_   :Any   ):







  '''simple docstring'''

  return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()   )




def     snake_case						(A_   :Tuple   ):







  '''simple docstring'''

  a  :    Any       =     {}

  a  :    str       =     ['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:
        a  :    Optional[int]       =     key.replace(f'''{group_key}.'''			,    f'''{group_key}.group.'''   )

    if "res_path" in key:
      a  :    int       =     key.replace('res_path.'			,    'res_path.path.'   )

    if key.endswith('.w'   ):
      a  :    Tuple       =     rreplace(A_			,    '.w'			,    '.weight'			,    1   )
    if key.endswith('.b'   ):
      a  :    int       =     rreplace(A_			,    '.b'			,    '.bias'			,    1   )

    a  :    Dict       =     value.float()

  return upgrade




@torch.no_grad()
def     snake_case						(A_   :int			,    A_   :List[Any]			,    A_   :Optional[int]=None			,    A_   :List[str]=True   ):







  '''simple docstring'''

  from dall_e import Encoder

  a  :    int       =     Encoder()
  if os.path.exists(A_   ):
    a  :    int       =     torch.load(A_   )
  else:
    a  :    int       =     torch.hub.load_state_dict_from_url(A_   )

  if isinstance(A_			,    A_   ):
    a  :    List[Any]       =     ckpt.state_dict()
  encoder.load_state_dict(A_   )

  if config_path is not None:
    a  :    int       =     FlavaImageCodebookConfig.from_pretrained(A_   )
  else:
    a  :    List[str]       =     FlavaImageCodebookConfig()

  a  :    Optional[int]       =     FlavaImageCodebook(A_   ).eval()
  a  :    List[str]       =     encoder.state_dict()

  a  :    List[Any]       =     upgrade_state_dict(A_   )
  hf_model.load_state_dict(A_   )
  a  :    Optional[Any]       =     hf_model.state_dict()
  a  :    Tuple       =     count_parameters(A_   )
  a  :    List[str]       =     count_parameters(A_   )

  assert torch.allclose(A_			,    A_			,    atol=1E-3   )

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


if __name__ == "__main__":
   _UpperCamelCase       :					List[str]									=		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')
   _UpperCamelCase       :					int									=		parser.parse_args()

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












"""simple docstring"""


def     snake_case						(A_   :int   ):







  '''simple docstring'''

  if upper_limit < 0:
    raise ValueError('Limit for the Catalan sequence must be ≥ 0'   )

  a  :    Any       =     [0] * (upper_limit + 1)

  # Base case: C(0) = C(1) = 1
  a  :    Optional[int]       =     1
  if upper_limit > 0:
    a  :    List[str]       =     1

  # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
  for i in range(2			,    upper_limit + 1   ):
    for j in range(A_   ):
      catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

  return catalan_list


if __name__ == "__main__":
   print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
   print('\n*** Enter -1 at any time to quit ***')
   print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
   try:
      while True:
         _UpperCamelCase       :					Union[str, Any]									=		int(input().strip())
         if N < 0:
            print('\n********* Goodbye!! ************')
            break
         else:
            print(f'''The Catalan numbers from 0 through {N} are:''')
            print(catalan_numbers(N))
            print('Try another upper limit for the sequence: ', end='')
   except (NameError, ValueError):
      print('\n********* Invalid input, goodbye! ************\n')

   import doctest

   doctest.testmod()




