
'''simple docstring'''

import string


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	""
 for i in sequence:
  lowerCAmelCase__     :   Optional[Any]     		=	ord(lowerCamelCase_			)
  if 6_5 <= extract <= 9_0:
   output += chr(1_5_5 - extract			)
  elif 9_7 <= extract <= 1_2_2:
   output += chr(2_1_9 - extract			)
  else:
   output += i
 return output


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	string.ascii_letters
 lowerCAmelCase__     :   Tuple     		=	string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
 return "".join(
     letters_reversed[letters.index(lowerCamelCase_			)] if c in letters else c for c in sequence			)


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 from timeit import timeit

 print("Running performance benchmarks..."			)
 lowerCAmelCase__     :   Union[str, Any]     		=	"from string import printable ; from __main__ import atbash, atbash_slow"
 print(f'''> atbash_slow(): {timeit('atbash_slow(printable)'      ,			setup=lowerCamelCase_			)} seconds'''			)
 print(f'''>      atbash(): {timeit('atbash(printable)'      ,			setup=lowerCamelCase_			)} seconds'''			)


if __name__ == "__main__":
     for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
          print(f'{example} encrypted in atbash: {atbash(example)}')
     benchmark()







'''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





