

from __future__ import annotations


def       __UpperCamelCase     (lowerCAmelCase       :	Dict,     lowerCAmelCase       :	Any,     lowerCAmelCase       :	str,     lowerCAmelCase       :	Union[str, Any]  )  ->	Optional[Any]:


      if (direction == 1 and array[indexa] > array[indexa]) or (
          direction == 0 and array[indexa] < array[indexa]
      ):
            A 			=					array[indexa], array[indexa]


def       __UpperCamelCase     (lowerCAmelCase       :	str,     lowerCAmelCase       :	Tuple,     lowerCAmelCase       :	str,     lowerCAmelCase       :	Optional[int]  )  ->	str:


      if length > 1:
            A 			=					int(length / 2  )
            for i in range(lowerCamelCase_,     low + middle  ):
                  comp_and_swap(lowerCamelCase_,     lowerCamelCase_,     i + middle,     lowerCamelCase_  )
            bitonic_merge(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_  )
            bitonic_merge(lowerCamelCase_,     low + middle,     lowerCamelCase_,     lowerCamelCase_  )


def       __UpperCamelCase     (lowerCAmelCase       :	List[str],     lowerCAmelCase       :	Union[str, Any],     lowerCAmelCase       :	List[Any],     lowerCAmelCase       :	List[Any]  )  ->	Union[str, Any]:


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


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=", ")





'''simple docstring'''

import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


snake_case									=    logging.get_logger(__name__)  # pylint: disable=invalid-name




class    lowerCAmelCase       (     UpperCamelCase_  ):


 def __init__(    self  : Union[str, Any]			,							a__  : int			,							a__  : List[Any]=768    ):
  '''simple docstring'''

  super().__init__(a__    )
  lowerCAmelCase__     :   int     		=	proj_size

  lowerCAmelCase__     :   str     		=	CLIPVisionModel(a__    )
  lowerCAmelCase__     :   int     		=	PaintByExampleMapper(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	nn.LayerNorm(config.hidden_size    )
  lowerCAmelCase__     :   Optional[Any]     		=	nn.Linear(config.hidden_size			,							self.proj_size    )

  # uncondition for scaling
  lowerCAmelCase__     :   Dict     		=	nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )


 def    _A	(    self  : Tuple			,							a__  : int			,							a__  : Optional[Any]=False    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.model(pixel_values=a__    )
  lowerCAmelCase__     :   Any     		=	clip_output.pooler_output
  lowerCAmelCase__     :   Union[str, Any]     		=	self.mapper(latent_states[:, None]    )
  lowerCAmelCase__     :   List[Any]     		=	self.final_layer_norm(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	self.proj_out(a__    )
  if return_uncond_vector:
   return latent_states, self.uncond_vector

  return latent_states




class    lowerCAmelCase       (     nn.Module  ):


 def __init__(    self  : Optional[Any]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  super().__init__()
  lowerCAmelCase__     :   Optional[Any]     		=	(config.num_hidden_layers + 1) // 5
  lowerCAmelCase__     :   str     		=	config.hidden_size
  lowerCAmelCase__     :   Dict     		=	1
  lowerCAmelCase__     :   int     		=	nn.ModuleList(
      [
          BasicTransformerBlock(a__			,							a__			,							a__			,							activation_fn="gelu"			,							attention_bias=a__    )
          for _ in range(a__    )
      ]    )


 def    _A	(    self  : int			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  for block in self.blocks:
   lowerCAmelCase__     :   List[Any]     		=	block(a__    )

  return hidden_states





