


"""simple docstring"""







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


lowerCamelCase_   :   str									=  logging.get_logger(__name__)

lowerCamelCase_   :   str									=  {
    """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				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  "pegasus"
 __lowerCAmelCase 					=  ["past_key_values"]
 __lowerCAmelCase 					=  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


 def __init__(		self ,    __A=5_0265 ,    __A=1024 ,    __A=12 ,    __A=4096 ,    __A=16 ,    __A=12 ,    __A=4096 ,    __A=16 ,    __A=0.0 ,    __A=0.0 ,    __A=True ,    __A=True ,    __A="gelu" ,    __A=1024 ,    __A=0.1 ,    __A=0.0 ,    __A=0.0 ,    __A=0.02 ,    __A=0 ,    __A=False ,    __A=0 ,    __A=1 ,    __A=1 ,    **__A ,    )					->     Union[str, Any]:
     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
     super().__init__(
         pad_token_id=__A ,    eos_token_id=__A ,    is_encoder_decoder=__A ,    decoder_start_token_id=__A ,    forced_eos_token_id=__A ,    **__A ,    )


 @property
 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     return self.encoder_attention_heads





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





def __UpperCamelCase			(       _A  ):
						if not numbers:
												return 0

						if not isinstance(_A ,  (list, tuple)  ) or not all(
						    isinstance(_A ,  _A  ) for number in numbers  ):
												raise ValueError('''numbers must be an iterable of integers'''  )

						lowerCAmelCase_      			=  lowerCAmelCase_      			=  lowerCAmelCase_      			=  numbers[0]

						for i in range(1 ,  len(_A  )  ):
												# update the maximum and minimum subarray products
												lowerCAmelCase_      			=  numbers[i]
												if number < 0:
																		lowerCAmelCase_			,					lowerCAmelCase_      			=  min_till_now, max_till_now
												lowerCAmelCase_      			=  max(_A ,  max_till_now * number  )
												lowerCAmelCase_      			=  min(_A ,  min_till_now * number  )

												# update the maximum product found till now
												lowerCAmelCase_      			=  max(_A ,  _A  )

						return max_prod




