




'''simple docstring'''


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class    __lowercase						(  nn.Module ):







   def __init__(	self      ,							UpperCamelCase = 16      ,							UpperCamelCase = 88      ,							UpperCamelCase = None      ,							UpperCamelCase = 1      ,							UpperCamelCase = 0.0      ,							UpperCamelCase = 32      ,							UpperCamelCase = None      ,							UpperCamelCase = False      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = "geglu"      ,							UpperCamelCase = None      ,							)				->    Tuple:
     super().__init__()
     __a			=					nn.ModuleList(
         [
             TransformeraDModel(
                 num_attention_heads=UpperCamelCase      ,							attention_head_dim=UpperCamelCase      ,							in_channels=UpperCamelCase      ,							num_layers=UpperCamelCase      ,							dropout=UpperCamelCase      ,							norm_num_groups=UpperCamelCase      ,							cross_attention_dim=UpperCamelCase      ,							attention_bias=UpperCamelCase      ,							sample_size=UpperCamelCase      ,							num_vector_embeds=UpperCamelCase      ,							activation_fn=UpperCamelCase      ,							num_embeds_ada_norm=UpperCamelCase      ,							)
             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
     __a			=					0.5

     # The shape of `encoder_hidden_states` is expected to be
     # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
     __a			=					[77, 257]

     # 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])`
     __a			=					[1, 0]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase = True      ,							)				->    List[Any]:
     __a			=					hidden_states

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

     __a			=					encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
     __a			=					output_states + input_states

     if not return_dict:
       return (output_states,)

     return TransformeraDModelOutput(sample=UpperCamelCase      )









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int			):
  __a			=					0
  while num > 0:
    digit_sum += num % 10
    num //= 10
  return digit_sum


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 100			):
  __a			=					1
  __a			=					2

  for i in range(2				,					max_n + 1			):
    __a			=					pre_numerator
    __a			=					2 * i // 3 if i % 3 == 0 else 1
    __a			=					cur_numerator
    __a			=					e_cont * pre_numerator + temp

  return sum_digits(a_			)


if __name__ == "__main__":
  print(f"""{solution() = }""")



