


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class      __lowerCAmelCase  (			nn.Module       ):






  """simple docstring"""





  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							int = 1_6     ,					_lowerCAmelCase							:							int = 8_8     ,					_lowerCAmelCase							:							Optional[int] = None     ,					_lowerCAmelCase							:							int = 1     ,					_lowerCAmelCase							:							float = 0.0     ,					_lowerCAmelCase							:							int = 3_2     ,					_lowerCAmelCase							:							Optional[int] = None     ,					_lowerCAmelCase							:							bool = False     ,					_lowerCAmelCase							:							Optional[int] = None     ,					_lowerCAmelCase							:							Optional[int] = None     ,					_lowerCAmelCase							:							str = "geglu"     ,					_lowerCAmelCase							:							Optional[int] = None     ,					) ->			List[str]:




     """simple docstring"""
     super().__init__()
     snake_case_            =       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
     snake_case_            =       0.5

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



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							str=None     ,					_lowerCAmelCase							:							Dict=None     ,					_lowerCAmelCase							:							int=None     ,					_lowerCAmelCase							:							bool = True     ,					) ->			List[str]:




     """simple docstring"""
     snake_case_            =       hidden_states

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

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

     if not return_dict:
        return (output_states,)

     return TransformeraDModelOutput(sample=_lowerCAmelCase	)








from math import factorial



def       _lowerCAmelCase    (				lowerCAmelCase_				:int = 20				)->int:
   '''simple docstring'''






   snake_case_            =       2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
   # 2, 3,...
   snake_case_            =       n // 2

   return int(factorial(lowerCAmelCase_				) / (factorial(lowerCAmelCase_				) * factorial(n - k				))				)


if __name__ == "__main__":
   import sys

   if len(sys.argv) == 1:
      print(solution(20))
   else:
      try:
         SCREAMING_SNAKE_CASE						:Any			=  int(sys.argv[1])
         print(solution(n))
      except ValueError:
         print('''Invalid entry - please enter a number.''')




