



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class       _SCREAMING_SNAKE_CASE	(nn.Module ):





     def __init__(       self :							Optional[int]     ,  UpperCamelCase :							int = 1_6     ,  UpperCamelCase :							int = 8_8     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							int = 1     ,  UpperCamelCase :							float = 0.0     ,  UpperCamelCase :							int = 3_2     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							str = "geglu"     ,  UpperCamelCase :							Optional[int] = None     ,  )->Union[str, Any]:
      super().__init__()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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       :    Tuple       =	[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])`
      __SCREAMING_SNAKE_CASE       :    str       =	[1, 0]






     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Union[str, Any]=None     ,  UpperCamelCase :							Tuple=None     ,  UpperCamelCase :							Any=None     ,  UpperCamelCase :							bool = True     ,  )->Any:
      __SCREAMING_SNAKE_CASE       :    Dict       =	hidden_states

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	[]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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[Any]       =	encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.transformer_index_for_condition[i]
       __SCREAMING_SNAKE_CASE       :    int       =	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]

      __SCREAMING_SNAKE_CASE       :    Any       =	encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCamelCase				)











def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 if height >= 1:
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
  move_disk(__lowerCamelCase		,	__lowerCamelCase     )
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 print("moving disk from"		,	__lowerCamelCase		,	"to"		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	int(input("Height of hanoi: "     ).strip()     )
 move_tower(__lowerCamelCase		,	"A"		,	"B"		,	"C"     )


if __name__ == "__main__":
      main()






