"""simple docstring"""




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class 		__A							(				nn.Module	):






     def __init__(	self       ,  a__ = 16       ,  a__ = 88       ,  a__ = None       ,  a__ = 1       ,  a__ = 0.0       ,  a__ = 32       ,  a__ = None       ,  a__ = False       ,  a__ = None       ,  a__ = None       ,  a__ = "geglu"       ,  a__ = None       ,  ):
       super().__init__()
       _lowerCAmelCase		: Optional[int]						    =  nn.ModuleList(
           [
               TransformeraDModel(
                   num_attention_heads=a__       ,  attention_head_dim=a__       ,  in_channels=a__       ,  num_layers=a__       ,  dropout=a__       ,  norm_num_groups=a__       ,  cross_attention_dim=a__       ,  attention_bias=a__       ,  sample_size=a__       ,  num_vector_embeds=a__       ,  activation_fn=a__       ,  num_embeds_ada_norm=a__       ,  )
               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
       _lowerCAmelCase		: List[Any]						    =  0.5

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






     def 					__A						(	self       ,  a__       ,  a__       ,  a__=None       ,  a__=None       ,  a__=None       ,  a__ = True       ,  ):
       _lowerCAmelCase		: Any						    =  hidden_states

       _lowerCAmelCase		: Optional[Any]						    =  []
       _lowerCAmelCase		: Dict						    =  0
       # attention_mask is not used yet
       for i in range(2						):
         # for each of the two transformers, pass the corresponding condition tokens
         _lowerCAmelCase		: List[str]						    =  encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
         _lowerCAmelCase		: Union[str, Any]						    =  self.transformer_index_for_condition[i]
         _lowerCAmelCase		: str						    =  self.transformers[transformer_index](
             a__       ,  encoder_hidden_states=a__       ,  timestep=a__       ,  cross_attention_kwargs=a__       ,  return_dict=a__       ,  )[0]
         encoded_states.append(encoded_state - input_states						)
         tokens_start += self.condition_lengths[i]

       _lowerCAmelCase		: Tuple						    =  encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
       _lowerCAmelCase		: str						    =  output_states + input_states

       if not return_dict:
         return (output_states,)

       return TransformeraDModelOutput(sample=a__						)





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  torch.nn.Linear(10       ,  10						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.optim.SGD(model.parameters()       ,  0.1						)
       _lowerCAmelCase		: Optional[Any]						    =  Accelerator()
       _lowerCAmelCase		: Tuple						    =  accelerator.prepare(a__						)
       try:
         pickle.loads(pickle.dumps(a__						)						)
       except Exception as e:
         self.fail(F"Accelerated optimizer pickling failed with {e}"						)
       AcceleratorState._reset_state()




