





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 							_lowerCAmelCase     (      nn.Module				):


  '''simple docstring'''







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


    '''simple docstring'''





    super().__init__()
    _snake_case :					Union[str, Any]								=						nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=snake_case__   ,					attention_head_dim=snake_case__   ,					in_channels=snake_case__   ,					num_layers=snake_case__   ,					dropout=snake_case__   ,					norm_num_groups=snake_case__   ,					cross_attention_dim=snake_case__   ,					attention_bias=snake_case__   ,					sample_size=snake_case__   ,					num_vector_embeds=snake_case__   ,					activation_fn=snake_case__   ,					num_embeds_ada_norm=snake_case__   ,					)
            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 :					str								=						0.5

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







  def UpperCamelCase_			(   self   :       int   ,					UpperCamelCase   :       Optional[int]   ,					UpperCamelCase   :       Union[str, Any]   ,					UpperCamelCase   :       Dict=None   ,					UpperCamelCase   :       Tuple=None   ,					UpperCamelCase   :       int=None   ,					UpperCamelCase   :       bool = True   ,					):


    '''simple docstring'''





    _snake_case :					Tuple								=						hidden_states

    _snake_case :					List[str]								=						[]
    _snake_case :					List[Any]								=						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 :					Dict								=						encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      _snake_case :					Dict								=						self.transformer_index_for_condition[i]
      _snake_case :					Tuple								=						self.transformers[transformer_index](
          snake_case__   ,					encoder_hidden_states=snake_case__   ,					timestep=snake_case__   ,					cross_attention_kwargs=snake_case__   ,					return_dict=snake_case__   ,					)[0]
      encoded_states.append(encoded_state - input_states				)
      tokens_start += self.condition_lengths[i]

    _snake_case :					str								=						encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    _snake_case :					Optional[int]								=						output_states + input_states

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=snake_case__				)





"""simple docstring"""




from __future__ import annotations

from typing import Any

class    a_   :




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  int = 6  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							self.create_linked_list(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  int  ):
							lowerCAmelCase__   = Node()
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							for _ in range(1	,    snake_case__  ):
									lowerCAmelCase__   = Node()
									lowerCAmelCase__   = current_node
									lowerCAmelCase__   = previous_node
									lowerCAmelCase__   = current_node
							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = previous_node




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return (
							    self.front == self.rear
							    and self.front is not None
							    and self.front.data is None
							)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							return self.front.data if self.front else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Any  ):
							if self.rear is None:
									return

							self.check_is_full()
							if not self.is_empty():
									lowerCAmelCase__   = self.rear.next
							if self.rear:
									lowerCAmelCase__   = data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							if self.rear is None or self.front is None:
									return None
							if self.front == self.rear:
									lowerCAmelCase__   = self.front.data
									lowerCAmelCase__   = None
									return data

							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = old_front.next
							lowerCAmelCase__   = old_front.data
							lowerCAmelCase__   = None
							return data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							if self.is_empty():
									raise Exception("""Empty Queue"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							if self.rear and self.rear.next == self.front:
									raise Exception("""Full Queue"""  )

class    a_   :




					def __init__(						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None


if __name__ == "__main__":
			import doctest

			doctest.testmod()




