



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class 			_lowerCAmelCase   ( nn.Module			):






			"""simple docstring"""





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







									super().__init__()
									__lowercase					=				nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=lowercase			,     attention_head_dim=lowercase			,     in_channels=lowercase			,     num_layers=lowercase			,     dropout=lowercase			,     norm_num_groups=lowercase			,     cross_attention_dim=lowercase			,     attention_bias=lowercase			,     sample_size=lowercase			,     num_vector_embeds=lowercase			,     activation_fn=lowercase			,     num_embeds_ada_norm=lowercase			,     )
									        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
									__lowercase					=				0.5

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







			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       int			,     lowercase	:       Tuple			,     lowercase	:       int=None			,     lowercase	:       int=None			,     lowercase	:       List[str]=None			,     lowercase	:       bool = True			,     )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				hidden_states

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

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

									if not return_dict:
															return (output_states,)

									return TransformeraDModelOutput(sample=lowercase   )










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      bool:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1   ):
												__lowercase					=				True

						# sum is not zero and set is empty then false
						for i in range(1						,       required_sum + 1   ):
												__lowercase					=				False

						for i in range(1						,       arr_len + 1   ):
												for j in range(1						,       required_sum + 1   ):
																		if arr[i - 1] > j:
																								__lowercase					=				subset[i - 1][j]
																		if arr[i - 1] <= j:
																								__lowercase					=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





