

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class 	UpperCAmelCase__		(	nn.Module	):






  """simple docstring"""


  def __init__(							self						:		Dict     ,       __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[int]:
       super().__init__()
       SCREAMING_SNAKE_CASE__   =   nn.ModuleList(
           [
               TransformeraDModel(
                   num_attention_heads=__lowerCamelCase     ,       attention_head_dim=__lowerCamelCase     ,       in_channels=__lowerCamelCase     ,       num_layers=__lowerCamelCase     ,       dropout=__lowerCamelCase     ,       norm_num_groups=__lowerCamelCase     ,       cross_attention_dim=__lowerCamelCase     ,       attention_bias=__lowerCamelCase     ,       sample_size=__lowerCamelCase     ,       num_vector_embeds=__lowerCamelCase     ,       activation_fn=__lowerCamelCase     ,       num_embeds_ada_norm=__lowerCamelCase     ,       )
               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 				lowercase_		(							self						:		Any     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		str=None     ,       __lowerCamelCase						:		Tuple=None     ,       __lowerCamelCase						:		Tuple=None     ,       __lowerCamelCase						:		bool = True     ,       )		->							Optional[int]:
       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](
                __lowerCamelCase     ,       encoder_hidden_states=__lowerCamelCase     ,       timestep=__lowerCamelCase     ,       cross_attention_kwargs=__lowerCamelCase     ,       return_dict=__lowerCamelCase     ,       )[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=__lowerCamelCase )





import warnings

from .generation import TFGenerationMixin






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  warnings.warn(
      "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
      "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."						,					A__						,					)


