






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


lowerCamelCase_						     =  logging.get_logger(__name__)

lowerCamelCase_						     =  {
    """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 							a_ (						a_		):


							'''simple docstring'''






							__a:	Any     =    '''pegasus_x'''
							__a:	Any     =    ['''past_key_values''']
							__a:	Any     =    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

							def __init__(		self			,			lowercase_=9_6_1_0_3			,			lowercase_=1_6_3_8_4			,			lowercase_=1_6			,			lowercase_=4_0_9_6			,			lowercase_=1_6			,			lowercase_=1_6			,			lowercase_=4_0_9_6			,			lowercase_=1_6			,			lowercase_=0.0			,			lowercase_=0.0			,			lowercase_=True			,			lowercase_=True			,			lowercase_="gelu"			,			lowercase_=1_0_2_4			,			lowercase_=0.1			,			lowercase_=0.0			,			lowercase_=0.0			,			lowercase_=0.02			,			lowercase_=0			,			lowercase_=True			,			lowercase_=0			,			lowercase_=1			,			lowercase_=1			,			lowercase_=3_2			,			lowercase_=5_1_2			,			lowercase_=True			,			**lowercase_			,			)					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     vocab_size
														lowerCAmelCase_										=     max_position_embeddings
														lowerCAmelCase_										=     d_model
														lowerCAmelCase_										=     encoder_ffn_dim
														lowerCAmelCase_										=     encoder_layers
														lowerCAmelCase_										=     encoder_attention_heads
														lowerCAmelCase_										=     decoder_ffn_dim
														lowerCAmelCase_										=     decoder_layers
														lowerCAmelCase_										=     decoder_attention_heads
														lowerCAmelCase_										=     dropout
														lowerCAmelCase_										=     attention_dropout
														lowerCAmelCase_										=     activation_dropout
														lowerCAmelCase_										=     activation_function
														lowerCAmelCase_										=     init_std
														lowerCAmelCase_										=     encoder_layerdrop
														lowerCAmelCase_										=     decoder_layerdrop
														lowerCAmelCase_										=     use_cache
														lowerCAmelCase_										=     encoder_layers
														lowerCAmelCase_										=     scale_embedding  # scale factor will be sqrt(d_model) if True

														lowerCAmelCase_										=     num_global_tokens
														lowerCAmelCase_										=     block_size
														lowerCAmelCase_										=     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      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														return self.encoder_attention_heads






							@property
							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														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}''')



