






"""simple docstring"""




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       int             =	s.rsplit(__lowerCamelCase     ,   __lowerCamelCase				)
  return new.join(__lowerCamelCase				)

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  # 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 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Optional[int]             =	{}

  _a  :       List[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  :       Tuple             =	key.replace(F"""{group_key}."""     ,   F"""{group_key}.group."""				)

    if "res_path" in key:
      _a  :       Optional[Any]             =	key.replace("""res_path."""     ,   """res_path.path."""				)

    if key.endswith(""".w"""				):
      _a  :       Any             =	rreplace(__lowerCamelCase     ,   """.w"""     ,   """.weight"""     ,   1				)
    if key.endswith(""".b"""				):
      _a  :       Optional[Any]             =	rreplace(__lowerCamelCase     ,   """.b"""     ,   """.bias"""     ,   1				)

    _a  :       Optional[Any]             =	value.float()

  return upgrade

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=True				):



  '''simple docstring'''
  from dall_e import Encoder

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

  if isinstance(__lowerCamelCase     ,   __lowerCamelCase				):
    _a  :       Any             =	ckpt.state_dict()
  encoder.load_state_dict(__lowerCamelCase				)

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

  _a  :       str             =	FlavaImageCodebook(__lowerCamelCase				).eval()
  _a  :       Dict             =	encoder.state_dict()

  _a  :       List[str]             =	upgrade_state_dict(__lowerCamelCase				)
  hf_model.load_state_dict(__lowerCamelCase				)
  _a  :       List[str]             =	hf_model.state_dict()
  _a  :       List[Any]             =	count_parameters(__lowerCamelCase				)
  _a  :       List[Any]             =	count_parameters(__lowerCamelCase				)

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

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


if __name__ == "__main__":
 _snake_case          =   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')
 _snake_case          =   parser.parse_args()

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



"""simple docstring"""





from __future__ import annotations

import math







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    u
   for i in range(1,		__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    temp * (u - i)
   return temp







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    int(input("enter the numbers of values: "  )  )
   UpperCAmelCase_					:					list[list[float]]			     =    []
   for _ in range(__lowerCamelCase  ):
      y.append([]  )
   for i in range(__lowerCamelCase  ):
      for j in range(__lowerCamelCase  ):
         y[i].append(__lowerCamelCase  )
         UpperCAmelCase_					:					Tuple			     =    0

   print("enter the values of parameters in a list: "  )
   UpperCAmelCase_					:					Union[str, Any]			     =    list(map(__lowerCamelCase,		input().split()  )  )

   print("enter the values of corresponding parameters: "  )
   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    float(input()  )

   UpperCAmelCase_					:					Tuple			     =    int(input("enter the value to interpolate: "  )  )
   UpperCAmelCase_					:					Tuple			     =    (value - x[0]) / (x[1] - x[0])

   # for calculating forward difference table

   for i in range(1,		__lowerCamelCase  ):
      for j in range(n - i  ):
         UpperCAmelCase_					:					Union[str, Any]			     =    y[j + 1][i - 1] - y[j][i - 1]

   UpperCAmelCase_					:					Optional[int]			     =    y[0][0]
   for i in range(1,		__lowerCamelCase  ):
      summ += (ucal(__lowerCamelCase,		__lowerCamelCase  ) * y[0][i]) / math.factorial(__lowerCamelCase  )

   print(f"""the value at {value} is {summ}"""  )


if __name__ == "__main__":
    main()


