"""simple docstring"""
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase = 16    ,			lowercase = 88    ,			lowercase = None    ,			lowercase = 1    ,			lowercase = 0.0    ,			lowercase = 32    ,			lowercase = None    ,			lowercase = False    ,			lowercase = None    ,			lowercase = None    ,			lowercase = "geglu"    ,			lowercase = None    ,			)	->			str:





        '''simple docstring'''





        super().__init__()
        a__:     str										=	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
        a__:     List[str]										=	0.5

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase = True    ,			)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	hidden_states

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

        a__:     Union[str, Any]										=	encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        a__:     Any										=	output_states + input_states

        if not return_dict:
          return (output_states,)

        return TransformeraDModelOutput(sample=lowercase)



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = False   )	->str:

  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     Optional[int]										=	F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     List[str]										=	F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	input_str.split('_'   )

  a__:     List[str]										=	0 if use_pascal else 1

  a__:     List[str]										=	words[start_index:]

  a__:     List[str]										=	[word[0].upper() + word[1:] for word in words_to_capitalize]

  a__:     List[str]										=	'' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]   )


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

 testmod()


