from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class 			UpperCAmelCase__    (		nn.Module					):



  """simple docstring"""






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




    '''simple docstring'''




    super().__init__()
    __UpperCAmelCase						    =       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
    __UpperCAmelCase						    =       0.5

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






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			Tuple=None				,				__lowerCAmelCase:			Union[str, Any]=None				,				__lowerCAmelCase:			Tuple=None				,				__lowerCAmelCase:			bool = True				,				) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       hidden_states

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

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

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=_A   )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_							=      {
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""SqueezeBertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SqueezeBertForMaskedLM""",
           """SqueezeBertForMultipleChoice""",
           """SqueezeBertForQuestionAnswering""",
           """SqueezeBertForSequenceClassification""",
           """SqueezeBertForTokenClassification""",
           """SqueezeBertModel""",
           """SqueezeBertModule""",
           """SqueezeBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



