






"""simple docstring"""







from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class      UpperCamelCase		(nn.Module     ):






						def __init__(		self  :str			, __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			, )      ->Tuple:
										super().__init__()
										lowercase    :      Tuple       = 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
										lowercase    :      int       = 0.5

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






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

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

										lowercase    :      Tuple       = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
										lowercase    :      str       = output_states + input_states

										if not return_dict:
														return (output_states,)

										return TransformeraDModelOutput(sample=__magic_name__				)








"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
