

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


A									=  logging.get_logger(__name__)

A									=  {
    '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 SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




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


			def __init__(     self			,					__UpperCamelCase=5_02_65			,					__UpperCamelCase=10_24			,					__UpperCamelCase=12			,					__UpperCamelCase=40_96			,					__UpperCamelCase=16			,					__UpperCamelCase=12			,					__UpperCamelCase=40_96			,					__UpperCamelCase=16			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=True			,					__UpperCamelCase=True			,					__UpperCamelCase="gelu"			,					__UpperCamelCase=10_24			,					__UpperCamelCase=0.1			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.02			,					__UpperCamelCase=0			,					__UpperCamelCase=False			,					__UpperCamelCase=0			,					__UpperCamelCase=1			,					__UpperCamelCase=1			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				snake_case_   		=			vocab_size
				snake_case_   		=			max_position_embeddings
				snake_case_   		=			d_model
				snake_case_   		=			encoder_ffn_dim
				snake_case_   		=			encoder_layers
				snake_case_   		=			encoder_attention_heads
				snake_case_   		=			decoder_ffn_dim
				snake_case_   		=			decoder_layers
				snake_case_   		=			decoder_attention_heads
				snake_case_   		=			dropout
				snake_case_   		=			attention_dropout
				snake_case_   		=			activation_dropout
				snake_case_   		=			activation_function
				snake_case_   		=			init_std
				snake_case_   		=			encoder_layerdrop
				snake_case_   		=			decoder_layerdrop
				snake_case_   		=			use_cache
				snake_case_   		=			encoder_layers
				snake_case_   		=			scale_embedding  # scale factor will be sqrt(d_model) if True
				super().__init__(
				    pad_token_id=__UpperCamelCase			,					eos_token_id=__UpperCamelCase			,					is_encoder_decoder=__UpperCamelCase			,					decoder_start_token_id=__UpperCamelCase			,					forced_eos_token_id=__UpperCamelCase			,					**__UpperCamelCase			,					)


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.encoder_attention_heads







			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.d_model



import functools





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''

	# Validation
	if not isinstance(lowercase__      ,			lowercase__						) or not all(isinstance(lowercase__      ,			lowercase__						) for day in days						):
		raise ValueError('The parameter days should be a list of integers'						)

	if len(lowercase__						) != 3 or not all(isinstance(lowercase__      ,			lowercase__						) for cost in costs						):
		raise ValueError('The parameter costs should be a list of three integers'						)

	if len(lowercase__						) == 0:
		return 0

	if min(lowercase__						) <= 0:
		raise ValueError('All days elements should be greater than 0'						)

	if max(lowercase__						) >= 366:
		raise ValueError('All days elements should be less than 366'						)

	snake_case_   		=			set(lowercase__						)

	@functools.cache
	def dynamic_programming(lowercase__						) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1						)

		return min(
		    costs[0] + dynamic_programming(index + 1						)      ,			costs[1] + dynamic_programming(index + 7						)      ,			costs[2] + dynamic_programming(index + 30						)      ,			)

	return dynamic_programming(1						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
