


from __future__ import annotations
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 if (direction == 1 and array[indexa] > array[indexa]) or (
     direction == 0 and array[indexa] < array[indexa]
 ):
  _lowercase      ,    _lowercase							      =array[indexa], array[indexa]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 if length > 1:
  _lowercase							      =int(length / 2						)
  for i in range(__snake_case     ,						low + middle						):
   comp_and_swap(__snake_case     ,						__snake_case     ,						i + middle     ,						__snake_case						)
  bitonic_merge(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
  bitonic_merge(__snake_case     ,						low + middle     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 if length > 1:
  _lowercase							      =int(length / 2						)
  bitonic_sort(__snake_case     ,						__snake_case     ,						__snake_case     ,						1						)
  bitonic_sort(__snake_case     ,						low + middle     ,						__snake_case     ,						0						)
  bitonic_merge(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__   	=							input('''Enter numbers separated by a comma:\n''').strip()
  UpperCAmelCase__   	=							[int(item.strip()) for item in user_input.split(''',''')]

  bitonic_sort(unsorted, 0, len(unsorted), 1)
  print('''\nSorted array in ascending order is: ''', end='''''')
  print(*unsorted, sep=''', ''')

  bitonic_merge(unsorted, 0, len(unsorted), 0)
  print('''Sorted array in descending order is: ''', end='''''')
  print(*unsorted, sep=''', ''')





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =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
      _lowercase							      =0.5

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _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](
           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]

      _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=UpperCAmelCase       )

