

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class 				UpperCAmelCase_		(			nn.Module   ):
							"""simple docstring"""







							def __init__(			self       ,							_a = 1_6       ,							_a = 8_8       ,							_a = None       ,							_a = 1       ,							_a = 0.0       ,							_a = 3_2       ,							_a = None       ,							_a = False       ,							_a = None       ,							_a = None       ,							_a = "geglu"       ,							_a = None       ,							)    ->    Optional[int]:
												super().__init__()
												_a	:    Tuple         =		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
												_a	:    int         =		0.5

												# The shape of `encoder_hidden_states` is expected to be
												# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
												_a	:    Optional[int]         =		[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])`
												_a	:    Any         =		[1, 0]




							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a=None       ,							_a=None       ,							_a=None       ,							_a = True       ,							)    ->    Optional[int]:
												_a	:    Optional[Any]         =		hidden_states

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

												_a	:    Dict         =		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
												_a	:    int         =		output_states + input_states

												if not return_dict:
																	return (output_states,)

												return TransformeraDModelOutput(sample=_a       )






from diffusers.utils.testing_utils import require_onnxruntime

@require_onnxruntime
class 				UpperCAmelCase_		:
							"""simple docstring"""







							pass



