


"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def 	snake_case		(  UpperCamelCase__							:    int      ,					UpperCamelCase__							:    Any      ,					UpperCamelCase__							:    List[Any]      ,					UpperCamelCase__							:    List[str]     ) ->   Optional[int]:
      lowerCamelCase       :       Tuple   =						s.rsplit(UpperCamelCase__      ,					UpperCamelCase__     )
      return new.join(UpperCamelCase__     )




def 	snake_case		(  UpperCamelCase__							:    Optional[int]     ) ->   Any:
      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()     )




def 	snake_case		(  UpperCamelCase__							:    Any     ) ->   str:
      lowerCamelCase       :       List[str]   =						{}

      lowerCamelCase       :       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:
                        lowerCamelCase       :       List[str]   =						key.replace(F'{group_key}.'      ,					F'{group_key}.group.'     )

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

            if key.endswith(""".w"""     ):
                  lowerCamelCase       :       str   =						rreplace(UpperCamelCase__      ,					""".w"""      ,					""".weight"""      ,					1     )
            if key.endswith(""".b"""     ):
                  lowerCamelCase       :       str   =						rreplace(UpperCamelCase__      ,					""".b"""      ,					""".bias"""      ,					1     )

            lowerCamelCase       :       List[Any]   =						value.float()

      return upgrade




@torch.no_grad()
def 	snake_case		(  UpperCamelCase__							:    Any      ,					UpperCamelCase__							:    str      ,					UpperCamelCase__							:    int=None      ,					UpperCamelCase__							:    Optional[Any]=True     ) ->   List[str]:
      from dall_e import Encoder

      lowerCamelCase       :       Union[str, Any]   =						Encoder()
      if os.path.exists(UpperCamelCase__     ):
            lowerCamelCase       :       int   =						torch.load(UpperCamelCase__     )
      else:
            lowerCamelCase       :       List[Any]   =						torch.hub.load_state_dict_from_url(UpperCamelCase__     )

      if isinstance(UpperCamelCase__      ,					UpperCamelCase__     ):
            lowerCamelCase       :       Union[str, Any]   =						ckpt.state_dict()
      encoder.load_state_dict(UpperCamelCase__     )

      if config_path is not None:
            lowerCamelCase       :       Union[str, Any]   =						FlavaImageCodebookConfig.from_pretrained(UpperCamelCase__     )
      else:
            lowerCamelCase       :       Dict   =						FlavaImageCodebookConfig()

      lowerCamelCase       :       Optional[int]   =						FlavaImageCodebook(UpperCamelCase__     ).eval()
      lowerCamelCase       :       Optional[int]   =						encoder.state_dict()

      lowerCamelCase       :       str   =						upgrade_state_dict(UpperCamelCase__     )
      hf_model.load_state_dict(UpperCamelCase__     )
      lowerCamelCase       :       Dict   =						hf_model.state_dict()
      lowerCamelCase       :       int   =						count_parameters(UpperCamelCase__     )
      lowerCamelCase       :       Dict   =						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__":
    __lowerCamelCase   :Any						     =		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')
    __lowerCamelCase   :Union[str, Any]						     =		parser.parse_args()

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










'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
