



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class     _A       (	nn.Module    ):



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


           '''simple docstring'''

           super().__init__()
           __a						       =				nn.ModuleList(
               [
                   TransformeraDModel(
                       num_attention_heads=__SCREAMING_SNAKE_CASE     ,		attention_head_dim=__SCREAMING_SNAKE_CASE     ,		in_channels=__SCREAMING_SNAKE_CASE     ,		num_layers=__SCREAMING_SNAKE_CASE     ,		dropout=__SCREAMING_SNAKE_CASE     ,		norm_num_groups=__SCREAMING_SNAKE_CASE     ,		cross_attention_dim=__SCREAMING_SNAKE_CASE     ,		attention_bias=__SCREAMING_SNAKE_CASE     ,		sample_size=__SCREAMING_SNAKE_CASE     ,		num_vector_embeds=__SCREAMING_SNAKE_CASE     ,		activation_fn=__SCREAMING_SNAKE_CASE     ,		num_embeds_ada_norm=__SCREAMING_SNAKE_CASE     ,		)
                   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
           __a						       =				0.5

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





       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		):


           '''simple docstring'''

           __a						       =				hidden_states

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

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

           if not return_dict:
               return (output_states,)

           return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE)









"""simple docstring"""






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    ImageTextPipelineOutput,
							    UniDiffuserPipeline,
							)
else:
							from .modeling_text_decoder import UniDiffuserTextDecoder
							from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
							from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
