





'''simple docstring'''



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class       __SCREAMING_SNAKE_CASE	(						nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase = 16						,   lowerCamelCase = 88						,   lowerCamelCase = None						,   lowerCamelCase = 1						,   lowerCamelCase = 0.0						,   lowerCamelCase = 32						,   lowerCamelCase = None						,   lowerCamelCase = False						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = "geglu"						,   lowerCamelCase = None						,   ):
       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     		=				[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])`
       _snake_case     		=				[1, 0]






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase = True						,   ):
       _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	)









'''simple docstring'''



import functools




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )

  @functools.cache
  def min_distance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ) -> int:
    # if first word index is overflow - delete all from the second word
    if indexa >= len_worda:
      return len_worda - indexa
    # if second word index is overflow - delete all from the first word
    if indexa >= len_worda:
      return len_worda - indexa
    _snake_case     		=				int(worda[indexa] != worda[indexa]       )  # current letters not identical
    return min(
        1 + min_distance(indexa + 1					,	SCREAMING_SNAKE_CASE__       )					,	1 + min_distance(SCREAMING_SNAKE_CASE__					,	indexa + 1       )					,	diff + min_distance(indexa + 1					,	indexa + 1       )					,	)

  return min_distance(0					,	0       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


