






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


snake_case_   :	Tuple							      =logging.get_logger(__name__)

snake_case_   :	int							      ={
    '''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__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Optional[int] 					= 'pegasus_x'
		UpperCAmelCase_							:       Optional[Any] 					= ['past_key_values']
		UpperCAmelCase_							:       Tuple 					= {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






		def __init__(      self							, lowercase__=96103							, lowercase__=16384							, lowercase__=16							, lowercase__=4096							, lowercase__=16							, lowercase__=16							, lowercase__=4096							, lowercase__=16							, lowercase__=0.0							, lowercase__=0.0							, lowercase__=True							, lowercase__=True							, lowercase__="gelu"							, lowercase__=1024							, lowercase__=0.1							, lowercase__=0.0							, lowercase__=0.0							, lowercase__=0.02							, lowercase__=0							, lowercase__=True							, lowercase__=0							, lowercase__=1							, lowercase__=1							, lowercase__=32							, lowercase__=512							, lowercase__=True							, **lowercase__							, )				->				Tuple:
				__A    =							vocab_size
				__A    =							max_position_embeddings
				__A    =							d_model
				__A    =							encoder_ffn_dim
				__A    =							encoder_layers
				__A    =							encoder_attention_heads
				__A    =							decoder_ffn_dim
				__A    =							decoder_layers
				__A    =							decoder_attention_heads
				__A    =							dropout
				__A    =							attention_dropout
				__A    =							activation_dropout
				__A    =							activation_function
				__A    =							init_std
				__A    =							encoder_layerdrop
				__A    =							decoder_layerdrop
				__A    =							use_cache
				__A    =							encoder_layers
				__A    =							scale_embedding  # scale factor will be sqrt(d_model) if True

				__A    =							num_global_tokens
				__A    =							block_size
				__A    =							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 					_lowerCamelCase					(      self						)				->				int:
				return self.encoder_attention_heads






		@property
		def 					_lowerCamelCase					(      self						)				->				int:
				return self.d_model









def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		if n < 0:
				return 0
		# table[i] represents the number of ways to get to amount i
		__A    =							[0] * (n + 1)

		# There is exactly 1 way to get to zero(You pick no coins).
		__A    =							1

		# Pick all coins one by one and update table[] values
		# after the index greater than or equal to the value of the
		# picked coin
		for coin_val in s:
				for j in range(lowerCAmelCase__					,     n + 1	):
						table[j] += table[j - coin_val]

		return table[n]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

