


"""simple docstring"""


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a = 16       ,  __a = 88       ,  __a = None       ,  __a = 1       ,  __a = 0.0       ,  __a = 32       ,  __a = None       ,  __a = False       ,  __a = None       ,  __a = None       ,  __a = "geglu"       ,  __a = None       ,  ):
										super().__init__()
										__lowerCAmelCase							       =     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
										__lowerCAmelCase							       =     0.5

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



							def      snake_case							(	self       ,  __a       ,  __a       ,  __a=None       ,  __a=None       ,  __a=None       ,  __a = True       ,  ):
										__lowerCAmelCase							       =     hidden_states

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

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

										if not return_dict:
													return (output_states,)

										return TransformeraDModelOutput(sample=__a      )








"""simple docstring"""


from __future__ import annotations
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a=None      ):
										__lowerCAmelCase							       =     data
										__lowerCAmelCase							       =     None



							def __repr__(	self      ):
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     self
										while temp:
													string_rep.append(f"{temp.data}"      )
													__lowerCAmelCase							       =     temp.next
										return "->".join(__a      )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if not elements_list:
						raise Exception("The Elements List is empty" )

			__lowerCAmelCase							       =     __lowerCAmelCase							       =     Node(elements_list[0] )
			for i in range(1       ,					len(_UpperCamelCase ) ):
						__lowerCAmelCase							       =     Node(elements_list[i] )
						__lowerCAmelCase							       =     current.next
			return head


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if head_node is not None and isinstance(_UpperCamelCase       ,					_UpperCamelCase ):
						print_reverse(head_node.next )
						print(head_node.data )


def _lowerCamelCase      (						):

			'''simple docstring'''




			from doctest import testmod

			testmod()

			__lowerCAmelCase							       =     make_linked_list([14, 52, 14, 12, 43] )
			print("Linked List:" )
			print(_UpperCamelCase )
			print("Elements in Reverse:" )
			print_reverse(_UpperCamelCase )


if __name__ == "__main__":
	main()




