



"""simple docstring"""


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class 			_a (					nn.Module):
			"""simple docstring"""



			def __init__(		self     :    str				,   __UpperCamelCase     :    int = 1_6				,   __UpperCamelCase     :    int = 8_8				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    int = 1				,   __UpperCamelCase     :    float = 0.0				,   __UpperCamelCase     :    int = 3_2				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    str = "geglu"				,   __UpperCamelCase     :    Optional[int] = None				,   )->List[str]:
							super().__init__()
							_UpperCAmelCase					=    nn.ModuleList(
							    [
							        TransformeraDModel(
							            num_attention_heads=__UpperCamelCase				,   attention_head_dim=__UpperCamelCase				,   in_channels=__UpperCamelCase				,   num_layers=__UpperCamelCase				,   dropout=__UpperCamelCase				,   norm_num_groups=__UpperCamelCase				,   cross_attention_dim=__UpperCamelCase				,   attention_bias=__UpperCamelCase				,   sample_size=__UpperCamelCase				,   num_vector_embeds=__UpperCamelCase				,   activation_fn=__UpperCamelCase				,   num_embeds_ada_norm=__UpperCamelCase				,   )
							        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					=    0.5

							# The shape of `encoder_hidden_states` is expected to be
							# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
							_UpperCAmelCase					=    [7_7, 2_5_7]

							# 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					=    [1, 0]



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    Optional[Any]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   __UpperCamelCase     :    Union[str, Any]=None				,   __UpperCamelCase     :    bool = True				,   )->Optional[Any]:
							_UpperCAmelCase					=    hidden_states

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

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

							if not return_dict:
											return (output_states,)

							return TransformeraDModelOutput(sample=__UpperCamelCase   )








"""simple docstring"""


import operator







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						bool = False					,     _SCREAMING_SNAKE_CASE   :						list | None = None				):






				'''simple docstring'''



				_UpperCAmelCase					=    operator.lt if reverse else operator.gt
				_UpperCAmelCase					=    solution or []

				if not arr:
								return solution

				_UpperCAmelCase					=    [arr.pop(0				)]
				for i, item in enumerate(_SCREAMING_SNAKE_CASE				):
								if _operator(_SCREAMING_SNAKE_CASE					,     sublist[-1]				):
												sublist.append(_SCREAMING_SNAKE_CASE				)
												arr.pop(_SCREAMING_SNAKE_CASE				)

    #  merging sublist into solution list
				if not solution:
								solution.extend(_SCREAMING_SNAKE_CASE				)
				else:
								while sublist:
												_UpperCAmelCase					=    sublist.pop(0				)
												for i, xx in enumerate(_SCREAMING_SNAKE_CASE				):
																if not _operator(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
																				solution.insert(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
																				break
												else:
																solution.append(_SCREAMING_SNAKE_CASE				)

				strand_sort(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				return solution


if __name__ == "__main__":
					assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
					assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]



