





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class        _a    (    nn.Module):

      """simple docstring"""


      def __init__(		self:			Tuple	,    __lowerCamelCase:			int = 16	,    __lowerCamelCase:			int = 88	,    __lowerCamelCase:			Optional[int] = None	,    __lowerCamelCase:			int = 1	,    __lowerCamelCase:			float = 0.0	,    __lowerCamelCase:			int = 32	,    __lowerCamelCase:			Optional[int] = None	,    __lowerCamelCase:			bool = False	,    __lowerCamelCase:			Optional[int] = None	,    __lowerCamelCase:			Optional[int] = None	,    __lowerCamelCase:			str = "geglu"	,    __lowerCamelCase:			Optional[int] = None	,    ):
          '''simple docstring'''


          super().__init__()
          UpperCamelCase__:	List[str]					=     nn.ModuleList(
              [
                  TransformeraDModel(
                      num_attention_heads=__lowerCamelCase	,    attention_head_dim=__lowerCamelCase	,    in_channels=__lowerCamelCase	,    num_layers=__lowerCamelCase	,    dropout=__lowerCamelCase	,    norm_num_groups=__lowerCamelCase	,    cross_attention_dim=__lowerCamelCase	,    attention_bias=__lowerCamelCase	,    sample_size=__lowerCamelCase	,    num_vector_embeds=__lowerCamelCase	,    activation_fn=__lowerCamelCase	,    num_embeds_ada_norm=__lowerCamelCase	,    )
                  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
          UpperCamelCase__:	Optional[int]					=     0.5

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






      def 			UpperCAmelCase_       (		self:			List[str]	,    __lowerCamelCase:			List[Any]	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Dict=None	,    __lowerCamelCase:			Dict=None	,    __lowerCamelCase:			Dict=None	,    __lowerCamelCase:			bool = True	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     hidden_states

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

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

          if not return_dict:
              return (output_states,)

          return TransformeraDModelOutput(sample=__lowerCamelCase					)








def 				lowerCAmelCase_ (							A_):
    if not all(char in "01" for char in bin_string):
        raise ValueError("Non-binary value was passed to the function")
    if not bin_string:
        raise ValueError("Empty string was passed to the function")
    UpperCamelCase__:	List[Any]					=     ""
    while len(A_) % 3 != 0:
        UpperCamelCase__:	int					=     "0" + bin_string
    UpperCamelCase__:	Optional[int]					=     [
        bin_string[index : index + 3]
        for index in range(len(A_))
        if index % 3 == 0
    ]
    for bin_group in bin_string_in_3_list:
        UpperCamelCase__:	Union[str, Any]					=     0
        for index, val in enumerate(A_):
            oct_val += int(2 ** (2 - index) * int(A_))
        oct_string += str(A_)
    return oct_string


if __name__ == "__main__":
   from doctest import testmod

   testmod()

