

'''simple docstring'''
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class 							A__    (       nn.Module  ):





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





       '''simple docstring'''






       super().__init__()
       _SCREAMING_SNAKE_CASE							    =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
       _SCREAMING_SNAKE_CASE							    =0.5

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





  def 						A  (		self      :	Any							,					_a      :	List[Any]							,					_a      :	Union[str, Any]							,					_a      :	Tuple=None							,					_a      :	Any=None							,					_a      :	List[Any]=None							,					_a      :	bool = True							,					)	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =hidden_states

       _SCREAMING_SNAKE_CASE							    =[]
       _SCREAMING_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
            _SCREAMING_SNAKE_CASE							    =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            _SCREAMING_SNAKE_CASE							    =self.transformer_index_for_condition[i]
            _SCREAMING_SNAKE_CASE							    =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]

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

       if not return_dict:
            return (output_states,)

       return TransformeraDModelOutput(sample=_a )



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      int					 =     {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[Any]					 =     ["MBartTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Dict					 =     ["MBartTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     [
         "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MBartForCausalLM",
         "MBartForConditionalGeneration",
         "MBartForQuestionAnswering",
         "MBartForSequenceClassification",
         "MBartModel",
         "MBartPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TFMBartForConditionalGeneration",
         "TFMBartModel",
         "TFMBartPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     [
         "FlaxMBartForConditionalGeneration",
         "FlaxMBartForQuestionAnswering",
         "FlaxMBartForSequenceClassification",
         "FlaxMBartModel",
         "FlaxMBartPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
