



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :int   ,							_lowercase  :str = 16   ,							_lowercase  :str = 88   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :int = 1   ,							_lowercase  :int = 0.0   ,							_lowercase  :int = 32   ,							_lowercase  :Union[str, Any] = None   ,							_lowercase  :Optional[int] = False   ,							_lowercase  :str = None   ,							_lowercase  :Union[str, Any] = None   ,							_lowercase  :Union[str, Any] = "geglu"   ,							_lowercase  :Dict = None   ,							):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  nn.ModuleList(
         [
             TransformeraDModel(
                 num_attention_heads=__lowercase   ,							attention_head_dim=__lowercase   ,							in_channels=__lowercase   ,							num_layers=__lowercase   ,							dropout=__lowercase   ,							norm_num_groups=__lowercase   ,							cross_attention_dim=__lowercase   ,							attention_bias=__lowercase   ,							sample_size=__lowercase   ,							num_vector_embeds=__lowercase   ,							activation_fn=__lowercase   ,							num_embeds_ada_norm=__lowercase   ,							)
             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__			       =  0.5

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







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :List[Any]   ,							_lowercase  :Union[str, Any]=None   ,							_lowercase  :Optional[Any]=None   ,							_lowercase  :Any=None   ,							_lowercase  :List[str] = True   ,							):






     '''simple docstring'''





     lowercase__			       =  hidden_states

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

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

     if not return_dict:
        return (output_states,)

     return TransformeraDModelOutput(sample=__lowercase )













def _A       (   __magic_name__	):
   try:
      lowercase__			       =  float(__magic_name__	)
   except ValueError:
      raise ValueError("Please enter a valid number"	)
   lowercase__			       =  decimal - int(__magic_name__	)
   if fractional_part == 0:
      return int(__magic_name__	), 1
   else:
      lowercase__			       =  len(str(__magic_name__	).split("."	)[1]	)
      lowercase__			       =  int(decimal * (10**number_of_frac_digits)	)
      lowercase__			       =  10**number_of_frac_digits
      lowercase__       ,							lowercase__			       =  denominator, numerator
      while True:
         lowercase__			       =  dividend % divisor
         if remainder == 0:
            break
         lowercase__       ,							lowercase__			       =  divisor, remainder
      lowercase__       ,							lowercase__			       =  numerator / divisor, denominator / divisor
      return int(__magic_name__	), int(__magic_name__	)


if __name__ == "__main__":
  print(F"""{decimal_to_fraction(2) = }""")
  print(F"""{decimal_to_fraction(89.0) = }""")
  print(F"""{decimal_to_fraction("67") = }""")
  print(F"""{decimal_to_fraction("45.0") = }""")
  print(F"""{decimal_to_fraction(1.5) = }""")
  print(F"""{decimal_to_fraction("6.25") = }""")
  print(F"""{decimal_to_fraction("78td") = }""")






