



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a = 16				,			__a = 88				,			__a = None				,			__a = 1				,			__a = 0.0				,			__a = 32				,			__a = None				,			__a = False				,			__a = None				,			__a = None				,			__a = "geglu"				,			__a = None				,			):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		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
           lowerCamelCase								=		0.5

           # The shape of `encoder_hidden_states` is expected to be
           # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
           lowerCamelCase								=		[77, 2_57]

           # 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])`
           lowerCamelCase								=		[1, 0]







     def 						_a      (self				,			__a				,			__a				,			__a=None				,			__a=None				,			__a=None				,			__a = True				,			):






           '''simple docstring'''



           lowerCamelCase								=		hidden_states

           lowerCamelCase								=		[]
           lowerCamelCase								=		0
           # attention_mask is not used yet
           for i in range(2    ):
                 # for each of the two transformers, pass the corresponding condition tokens
                 lowerCamelCase								=		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
                 lowerCamelCase								=		self.transformer_index_for_condition[i]
                 lowerCamelCase								=		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]

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

           if not return_dict:
                 return (output_states,)

           return TransformeraDModelOutput(sample=__a    )




from math import pi

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(9_0, 1_0))