
"""simple docstring"""





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class __lowerCAmelCase					(       nn.Module   ):




			"""simple docstring"""







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







								"""simple docstring"""
								super().__init__()
								A_								=    nn.ModuleList(
								    [
								        TransformeraDModel(
								            num_attention_heads=_snake_case							, attention_head_dim=_snake_case							, in_channels=_snake_case							, num_layers=_snake_case							, dropout=_snake_case							, norm_num_groups=_snake_case							, cross_attention_dim=_snake_case							, attention_bias=_snake_case							, sample_size=_snake_case							, num_vector_embeds=_snake_case							, activation_fn=_snake_case							, num_embeds_ada_norm=_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    :       Tuple							, _snake_case    :       Optional[Any]							, _snake_case    :       Tuple							, _snake_case    :       Any=None							, _snake_case    :       List[Any]=None							, _snake_case    :       Tuple=None							, _snake_case    :       bool = True							, )   ->			Any:







								"""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](
													    _snake_case							, encoder_hidden_states=_snake_case							, timestep=_snake_case							, cross_attention_kwargs=_snake_case							, return_dict=_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=_snake_case						)






"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					, __a = None					, __a = None					):

					'''simple docstring'''





					if start is None:
										A_								=    0

					if end is None:
										A_								=    len(__a					) - 1

					if start >= end:
										return

					A_								=    (start + end) // 2

					slowsort(__a					, __a					, __a					)
					slowsort(__a					, mid + 1					, __a					)

					if sequence[end] < sequence[mid]:
										A_			,     A_								=    sequence[mid], sequence[end]

					slowsort(__a					, __a					, end - 1					)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




