from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class       snake_case					(      nn.Module						):




  def __init__(      self     :	str     , a_     :	int = 16     , a_     :	int = 88     , a_     :	Optional[int] = None     , a_     :	int = 1     , a_     :	float = 0.0     , a_     :	int = 32     , a_     :	Optional[int] = None     , a_     :	bool = False     , a_     :	Optional[int] = None     , a_     :	Optional[int] = None     , a_     :	str = "geglu"     , a_     :	Optional[int] = None     , )->       Optional[Any]:




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		nn.ModuleList(
           [
               TransformeraDModel(
                   num_attention_heads=a_     , attention_head_dim=a_     , in_channels=a_     , num_layers=a_     , dropout=a_     , norm_num_groups=a_     , cross_attention_dim=a_     , attention_bias=a_     , sample_size=a_     , num_vector_embeds=a_     , activation_fn=a_     , num_embeds_ada_norm=a_     , )
               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
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		0.5

       # The shape of `encoder_hidden_states` is expected to be
       # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
       SCREAMING_SNAKE_CASE__				:				str			=		[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])`
       SCREAMING_SNAKE_CASE__				:				str			=		[1, 0]




  def 	__lowercase(      self     :	Dict     , a_     :	Tuple     , a_     :	List[Any]     , a_     :	int=None     , a_     :	Dict=None     , a_     :	int=None     , a_     :	bool = True     , )->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		hidden_states

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

       SCREAMING_SNAKE_CASE__				:				Dict			=		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		output_states + input_states

       if not return_dict:
            return (output_states,)

       return TransformeraDModelOutput(sample=a_				)




from math import factorial, radians







def     _a				(      lowercase__  :			float     ,  lowercase__  :			int = 18     ,  lowercase__  :			int = 10 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

     # Converting from degrees to radians
     SCREAMING_SNAKE_CASE__				:				int			=		radians(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		angle_in_radians
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		3
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		-1

     for _ in range(lowercase__ ):
          result += (b * (angle_in_radians**a)) / factorial(lowercase__ )

          SCREAMING_SNAKE_CASE__				:				Any			=		-b  # One positive term and the next will be negative and so on...
          a += 2  # Increased by 2 for every term.

     return round(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      __import__("doctest").testmod()



