



"""simple docstring"""




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


_lowerCAmelCase				:					Union[str, Any]							=							logging.get_logger(__name__)

_lowerCAmelCase				:					Union[str, Any]							=							{
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}






class 	UpperCAmelCase_  (				a_						):
      __SCREAMING_SNAKE_CASE						:   Any      = '''pegasus_x'''
      __SCREAMING_SNAKE_CASE						:   Any      = ['''past_key_values''']
      __SCREAMING_SNAKE_CASE						:   Any      = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



      def __init__(     self     :	Union[str, Any]     ,	A     :	str=9_6_1_0_3     ,	A     :	List[Any]=1_6_3_8_4     ,	A     :	Union[str, Any]=1_6     ,	A     :	Optional[int]=4_0_9_6     ,	A     :	Union[str, Any]=1_6     ,	A     :	Optional[int]=1_6     ,	A     :	str=4_0_9_6     ,	A     :	Dict=1_6     ,	A     :	Optional[int]=0.0     ,	A     :	str=0.0     ,	A     :	str=True     ,	A     :	Optional[Any]=True     ,	A     :	str="gelu"     ,	A     :	List[str]=1_0_2_4     ,	A     :	Union[str, Any]=0.1     ,	A     :	Optional[Any]=0.0     ,	A     :	Optional[Any]=0.0     ,	A     :	Union[str, Any]=0.02     ,	A     :	Dict=0     ,	A     :	List[Any]=True     ,	A     :	Dict=0     ,	A     :	List[Any]=1     ,	A     :	int=1     ,	A     :	int=3_2     ,	A     :	int=5_1_2     ,	A     :	List[str]=True     ,	**A     :	Optional[Any]     ,	):
           _UpperCAmelCase			:	List[str]								=    vocab_size
           _UpperCAmelCase			:	List[Any]								=    max_position_embeddings
           _UpperCAmelCase			:	Any								=    d_model
           _UpperCAmelCase			:	int								=    encoder_ffn_dim
           _UpperCAmelCase			:	Union[str, Any]								=    encoder_layers
           _UpperCAmelCase			:	str								=    encoder_attention_heads
           _UpperCAmelCase			:	List[str]								=    decoder_ffn_dim
           _UpperCAmelCase			:	Optional[int]								=    decoder_layers
           _UpperCAmelCase			:	Any								=    decoder_attention_heads
           _UpperCAmelCase			:	Union[str, Any]								=    dropout
           _UpperCAmelCase			:	List[Any]								=    attention_dropout
           _UpperCAmelCase			:	Any								=    activation_dropout
           _UpperCAmelCase			:	str								=    activation_function
           _UpperCAmelCase			:	List[str]								=    init_std
           _UpperCAmelCase			:	Optional[int]								=    encoder_layerdrop
           _UpperCAmelCase			:	Any								=    decoder_layerdrop
           _UpperCAmelCase			:	Tuple								=    use_cache
           _UpperCAmelCase			:	Any								=    encoder_layers
           _UpperCAmelCase			:	str								=    scale_embedding  # scale factor will be sqrt(d_model) if True

           _UpperCAmelCase			:	str								=    num_global_tokens
           _UpperCAmelCase			:	Any								=    block_size
           _UpperCAmelCase			:	Union[str, Any]								=    stagger_local_blocks

           super().__init__(
               pad_token_id=lowercase_     ,	eos_token_id=lowercase_     ,	is_encoder_decoder=lowercase_     ,	decoder_start_token_id=lowercase_     ,	forced_eos_token_id=lowercase_     ,	**lowercase_     ,	)



      @property
      def 						snake_case_					(     self     :	Union[str, Any]						):
           return self.encoder_attention_heads



      @property
      def 						snake_case_					(     self     :	List[Any]						):
           return self.d_model








import math
def      lowerCamelCase					(						a_	)	->			Dict:
							lowerCAmelCase_										=     0
							if n % 2 == 0:
														count += 1
														while n % 2 == 0:
																					lowerCAmelCase_										=     int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

							lowerCAmelCase_										=     3

							while i <= int(math.sqrt(a_	)	):
														if n % i == 0:
																					count += 1
																					while n % i == 0:
																												lowerCAmelCase_										=     int(n / i	)
														lowerCAmelCase_										=     i + 2

							# this condition checks the prime
							# number n is greater than 2

							if n > 2:
														count += 1
							return count


if __name__ == "__main__":
		lowerCamelCase_						     =  5_1_2_4_2_1_8_3
		print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
		print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')



