


'''simple docstring'''







from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class 					__a      (nn.Module     ):

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







													"""simple docstring"""
													super().__init__()
													UpperCAmelCase_			:      List[Any]     						=  nn.ModuleList(
													    [
													        TransformeraDModel(
													            num_attention_heads=__magic_name__       ,     attention_head_dim=__magic_name__       ,     in_channels=__magic_name__       ,     num_layers=__magic_name__       ,     dropout=__magic_name__       ,     norm_num_groups=__magic_name__       ,     cross_attention_dim=__magic_name__       ,     attention_bias=__magic_name__       ,     sample_size=__magic_name__       ,     num_vector_embeds=__magic_name__       ,     activation_fn=__magic_name__       ,     num_embeds_ada_norm=__magic_name__       ,     )
													        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
													UpperCAmelCase_			:      List[str]     						=  0.5

													# The shape of `encoder_hidden_states` is expected to be
													# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
													UpperCAmelCase_			:      Optional[int]     						=  [77, 2_57]

													# 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])`
													UpperCAmelCase_			:      Tuple     						=  [1, 0]



							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Any       ,     __magic_name__							:     Optional[int]=None       ,     __magic_name__							:     Optional[int]=None       ,     __magic_name__							:     List[str]=None       ,     __magic_name__							:     bool = True       ,     )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  hidden_states

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

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

													if not return_dict:
																			return (output_states,)

													return TransformeraDModelOutput(sample=__magic_name__   )












'''simple docstring'''







class 					__a      :

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  size
													UpperCAmelCase_			:      Tuple     						=  [0] * size
													UpperCAmelCase_			:      Optional[Any]     						=  [0] * size

							@staticmethod
							def     UpperCAmelCase__	(							__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return index | (index + 1)

							@staticmethod
							def     UpperCAmelCase__	(							__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return (index & (index + 1)) - 1

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     int       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  value
													while index < self.size:
																			UpperCAmelCase_			:      str     						=  self.get_prev(__magic_name__   ) + 1
																			if current_left_border == index:
																									UpperCAmelCase_			:      List[str]     						=  value
																			else:
																									UpperCAmelCase_			:      Optional[int]     						=  max(__magic_name__       ,     __magic_name__       ,     __magic_name__   )
																			UpperCAmelCase_			:      Tuple     						=  self.get_next(__magic_name__   )



							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     int       ,     __magic_name__							:     int   )					->       int:







													"""simple docstring"""
													right -= 1  # Because of right is exclusive
													UpperCAmelCase_			:      List[str]     						=  0
													while left <= right:
																			UpperCAmelCase_			:      Optional[Any]     						=  self.get_prev(__magic_name__   )
																			if left <= current_left:
																									UpperCAmelCase_			:      Dict     						=  max(__magic_name__       ,     self.tree[right]   )
																									UpperCAmelCase_			:      Optional[Any]     						=  current_left
																			else:
																									UpperCAmelCase_			:      str     						=  max(__magic_name__       ,     self.arr[right]   )
																									right -= 1
													return result


if __name__ == "__main__":
					import doctest

					doctest.testmod()





