


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__  :Any             =							logging.get_logger(__name__)

lowerCAmelCase__  :Optional[Any]             =							{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[Any]           =     'pegasus'
		_a     :  Union[str, Any]           =     ['past_key_values']
		_a     :  Union[str, Any]           =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=50265      ,   _SCREAMING_SNAKE_CASE=1024      ,   _SCREAMING_SNAKE_CASE=12      ,   _SCREAMING_SNAKE_CASE=4096      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=12      ,   _SCREAMING_SNAKE_CASE=4096      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE="gelu"      ,   _SCREAMING_SNAKE_CASE=1024      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=1      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      max_position_embeddings
									_UpperCAmelCase					=      d_model
									_UpperCAmelCase					=      encoder_ffn_dim
									_UpperCAmelCase					=      encoder_layers
									_UpperCAmelCase					=      encoder_attention_heads
									_UpperCAmelCase					=      decoder_ffn_dim
									_UpperCAmelCase					=      decoder_layers
									_UpperCAmelCase					=      decoder_attention_heads
									_UpperCAmelCase					=      dropout
									_UpperCAmelCase					=      attention_dropout
									_UpperCAmelCase					=      activation_dropout
									_UpperCAmelCase					=      activation_function
									_UpperCAmelCase					=      init_std
									_UpperCAmelCase					=      encoder_layerdrop
									_UpperCAmelCase					=      decoder_layerdrop
									_UpperCAmelCase					=      use_cache
									_UpperCAmelCase					=      encoder_layers
									_UpperCAmelCase					=      scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=_SCREAMING_SNAKE_CASE      ,   eos_token_id=_SCREAMING_SNAKE_CASE      ,   is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,   decoder_start_token_id=_SCREAMING_SNAKE_CASE      ,   forced_eos_token_id=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return self.encoder_attention_heads






		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return self.d_model




import math
import random

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		bool = False	)  ->		float:


							'''simple docstring'''





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


# Initial Value
lowerCAmelCase__  :Optional[Any]             =							0.02

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      float(2 * (random.randint(1      ,  1_0_0	)) - 1	)

							for _ in range(a__	):
														# Forward propagation
														_UpperCAmelCase					=      sigmoid_function(INITIAL_VALUE * weight	)
														# How much did we miss?
														_UpperCAmelCase					=      (expected / 1_0_0) - layer_a
														# Error delta
														_UpperCAmelCase					=      layer_1_error * sigmoid_function(a__      ,  a__	)
														# Update weight
														weight += INITIAL_VALUE * layer_1_delta

							return layer_a * 1_0_0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase__  :List[Any]             =							int(input('''Expected value: '''))
		lowerCAmelCase__  :Any             =							int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
