






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

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class        _UpperCAmelCase						(		nn.Module			):




    def __init__(			self  :       Any  ,     a  :       int = 1_6  ,     a  :       int = 8_8  ,     a  :       Optional[int] = None  ,     a  :       int = 1  ,     a  :       float = 0.0  ,     a  :       int = 3_2  ,     a  :       Optional[int] = None  ,     a  :       bool = False  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = None  ,     a  :       str = "geglu"  ,     a  :       Optional[int] = None  ,     ):






       '''simple docstring'''






       super().__init__()
       lowercase_	:    Dict           =     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
       lowercase_	:    Union[str, Any]           =     0.5

       # The shape of `encoder_hidden_states` is expected to be
       # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
       lowercase_	:    Optional[Any]           =     [7_7, 2_5_7]

       # 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])`
       lowercase_	:    List[Any]           =     [1, 0]





    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Tuple  ,     a  :       Tuple  ,     a  :       Union[str, Any]=None  ,     a  :       Optional[Any]=None  ,     a  :       Tuple=None  ,     a  :       bool = True  ,     ):






       '''simple docstring'''






       lowercase_	:    Any           =     hidden_states

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

       lowercase_	:    Tuple           =     encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
       lowercase_	:    str           =     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_torch_available,
)


UpperCamelCase__ =			{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__ =			[
     'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
     'EncodecModel',
     'EncodecPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__ =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




