from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class       snake_case					(      nn.Module						):




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




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		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
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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__				:				str			=		[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__				:				str			=		[1, 0]




		def 	__lowercase(      self     :	Dict     , a_     :	Tuple     , a_     :	List[Any]     , a_     :	int=None     , a_     :	Dict=None     , a_     :	int=None     , a_     :	bool = True     , )->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		hidden_states

							SCREAMING_SNAKE_CASE__				:				str			=		[]
							SCREAMING_SNAKE_CASE__				:				Any			=		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__				:				List[str]			=		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
												SCREAMING_SNAKE_CASE__				:				Tuple			=		self.transformer_index_for_condition[i]
												SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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]

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

							if not return_dict:
												return (output_states,)

							return TransformeraDModelOutput(sample=a_				)




from __future__ import annotations







def     _a				(      lowercase__  :			list[int | float]     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     if len(lowercase__ ) == 0:
          raise ValueError('find_max() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(left + right) >> 1  # the middle
     SCREAMING_SNAKE_CASE__				:				int			=		find_max(lowercase__     ,  lowercase__     ,  lowercase__ )  # find max in range[left, mid]
     SCREAMING_SNAKE_CASE__				:				Tuple			=		find_max(lowercase__     ,  mid + 1     ,  lowercase__ )  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



