from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class  __magic_name__  (		nn.Module    ):





   """simple docstring"""




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



    """simple docstring"""



    super().__init__()
    _UpperCamelCase:     Any							= 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
    _UpperCamelCase:     Any							= 0.5

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



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Optional[Any]		,  _lowercase					:  int		,  _lowercase					:  Optional[int]=None		,  _lowercase					:  List[Any]=None		,  _lowercase					:  Tuple=None		,  _lowercase					:  bool = True		,  ):



    """simple docstring"""



    _UpperCamelCase:     int							= hidden_states

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

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

    if not return_dict:
     return (output_states,)

    return TransformeraDModelOutput(sample=_lowercase							)
from __future__ import annotations




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  Dict=None							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= data
    _UpperCamelCase:     int							= None



   def __repr__(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     int							= []
    _UpperCamelCase:     str							= self
    while temp:
     string_rep.append(f"""{temp.data}"""							)
     _UpperCamelCase:     Optional[Any]							= temp.next
    return "->".join(_lowercase							)




def       lowerCAmelCase_     (		lowercase:							list		)    ->				Optional[int]:







 '''simple docstring'''
 if not elements_list:
  raise Exception('''The Elements List is empty'''		)

 _UpperCamelCase:     Optional[Any]							= Node(elements_list[0]		)
 for i in range(1  ,					len(lowercase		)		):
  _UpperCamelCase:     Union[str, Any]							= Node(elements_list[i]		)
  _UpperCamelCase:     Optional[int]							= current.next
 return head




def       lowerCAmelCase_     (		lowercase:							Node		)    ->				None:







 '''simple docstring'''
 if head_node is not None and isinstance(lowercase  ,					lowercase		):
  print_reverse(head_node.next		)
  print(head_node.data		)




def       lowerCAmelCase_     (		)    ->				Tuple:







 '''simple docstring'''
 from doctest import testmod

 testmod()

 _UpperCamelCase:     Any							= make_linked_list([14, 52, 14, 12, 43]		)
 print('''Linked List:'''		)
 print(lowercase		)
 print('''Elements in Reverse:'''		)
 print_reverse(lowercase		)


if __name__ == "__main__":
  main()