"""simple docstring"""







from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class  _lowerCamelCase			(nn.Module     ):







				def __init__( self    :       Optional[int]			,					lowerCamelCase_    :       int = 1_6			,					lowerCamelCase_    :       int = 8_8			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       int = 1			,					lowerCamelCase_    :       float = 0.0			,					lowerCamelCase_    :       int = 3_2			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       str = "geglu"			,					lowerCamelCase_    :       Optional[int] = None			,					):

					"""simple docstring"""






					super().__init__()
					_lowercase :    Any							=							nn.ModuleList(
					    [
					        TransformeraDModel(
					            num_attention_heads=lowerCamelCase_			,					attention_head_dim=lowerCamelCase_			,					in_channels=lowerCamelCase_			,					num_layers=lowerCamelCase_			,					dropout=lowerCamelCase_			,					norm_num_groups=lowerCamelCase_			,					cross_attention_dim=lowerCamelCase_			,					attention_bias=lowerCamelCase_			,					sample_size=lowerCamelCase_			,					num_vector_embeds=lowerCamelCase_			,					activation_fn=lowerCamelCase_			,					num_embeds_ada_norm=lowerCamelCase_			,					)
					        for _ in range(2						)
					    ]						)

					# Variables that can be set by a pipeline:

					# The ratio of transformer1 to transformer2's output states to be combined during inference
					_lowercase :    int							=							0.5

					# The shape of `encoder_hidden_states` is expected to be
					# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
					_lowercase :    Dict							=							[7_7, 2_5_7]

					# Which transformer to use to encode which condition.
					# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
					_lowercase :    Union[str, Any]							=							[1, 0]







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Dict=None			,					lowerCamelCase_    :       Tuple=None			,					lowerCamelCase_    :       Optional[int]=None			,					lowerCamelCase_    :       bool = True			,					):

					"""simple docstring"""






					_lowercase :    Any							=							hidden_states

					_lowercase :    int							=							[]
					_lowercase :    Dict							=							0
					# attention_mask is not used yet
					for i in range(2						):
						# for each of the two transformers, pass the corresponding condition tokens
						_lowercase :    Tuple							=							encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
						_lowercase :    Optional[Any]							=							self.transformer_index_for_condition[i]
						_lowercase :    List[Any]							=							self.transformers[transformer_index](
						    lowerCamelCase_			,					encoder_hidden_states=lowerCamelCase_			,					timestep=lowerCamelCase_			,					cross_attention_kwargs=lowerCamelCase_			,					return_dict=lowerCamelCase_			,					)[0]
						encoded_states.append(encoded_state - input_states						)
						tokens_start += self.condition_lengths[i]

					_lowercase :    Optional[int]							=							encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
					_lowercase :    List[str]							=							output_states + input_states

					if not return_dict:
						return (output_states,)

					return TransformeraDModelOutput(sample=lowerCamelCase_						)




"""simple docstring"""







import functools


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





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

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

	if len(__UpperCAmelCase			) == 0:
		return 0

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

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

	_lowercase :    List[Any]							=							set(__UpperCAmelCase			)

	@functools.cache
	def dynamic_programming(__UpperCAmelCase			) -> 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()



