






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu





class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  1
           UpperCamelCase					       =  3
           UpperCamelCase					       =  (32, 32)

           UpperCamelCase					       =  floats_tensor((batch_size, num_channels) + sizes   ,						rng=random.Random(0 ) ).to(_SCREAMING_SNAKE_CASE )
           return image







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  UNetaDConditionModel(
               block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=4   ,						out_channels=4   ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,						cross_attention_dim=32   ,						)
           return model







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  AutoencoderKL(
               block_out_channels=[32, 64]   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,						latent_channels=4   ,						)
           return model







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  CLIPTextConfig(
               bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=1000   ,						)
           return CLIPTextModel(_SCREAMING_SNAKE_CASE )







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           def extract(*_SCREAMING_SNAKE_CASE      :    Optional[int]   ,						**_SCREAMING_SNAKE_CASE      :    Tuple ):





                 class 					A__     :

                      '''simple docstring'''






                      def __init__( self      :    Dict ):






                            """simple docstring"""




                            UpperCamelCase					       =  torch.ones([0] )







                      def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






                            """simple docstring"""




                            self.pixel_values.to(_SCREAMING_SNAKE_CASE )
                            return self

                 return Out()

           return extract







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase					       =  self.dummy_cond_unet
           UpperCamelCase					       =  DDIMScheduler(
               beta_start=0.0_0_0_8_5   ,						beta_end=0.0_1_2   ,						beta_schedule='scaled_linear'   ,						clip_sample=_SCREAMING_SNAKE_CASE   ,						set_alpha_to_one=_SCREAMING_SNAKE_CASE   ,						)

           UpperCamelCase					       =  self.dummy_vae
           UpperCamelCase					       =  self.dummy_text_encoder
           UpperCamelCase					       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

           # make sure here that pndm scheduler skips prk
           UpperCamelCase					       =  StableDiffusionPipeline(
               unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						safety_checker=_SCREAMING_SNAKE_CASE   ,						feature_extractor=self.dummy_extractor   ,						)
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  'A painting of a squirrel eating a burger'

           UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
           UpperCamelCase					       =  sd_pipe([prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=6.0   ,						num_inference_steps=2   ,						output_type='np' )
           UpperCamelCase					       =  output.images

           UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
           UpperCamelCase					       =  sd_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=6.0   ,						num_inference_steps=2   ,						output_type='np'   ,						return_dict=_SCREAMING_SNAKE_CASE   ,						)[0]

           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           UpperCamelCase					       =  np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4] )

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase					       =  self.dummy_cond_unet
           UpperCamelCase					       =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.dummy_vae
           UpperCamelCase					       =  self.dummy_text_encoder
           UpperCamelCase					       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

           # make sure here that pndm scheduler skips prk
           UpperCamelCase					       =  StableDiffusionPipeline(
               unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						safety_checker=_SCREAMING_SNAKE_CASE   ,						feature_extractor=self.dummy_extractor   ,						)
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  'A painting of a squirrel eating a burger'
           UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
           UpperCamelCase					       =  sd_pipe([prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=6.0   ,						num_inference_steps=2   ,						output_type='np' )

           UpperCamelCase					       =  output.images

           UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
           UpperCamelCase					       =  sd_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=6.0   ,						num_inference_steps=2   ,						output_type='np'   ,						return_dict=_SCREAMING_SNAKE_CASE   ,						)[0]

           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           UpperCamelCase					       =  np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3] )

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionPipeline.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-lms-pipe'   ,						safety_checker=_SCREAMING_SNAKE_CASE )
           assert isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           assert isinstance(pipe.scheduler   ,						_SCREAMING_SNAKE_CASE )
           assert pipe.safety_checker is None

           UpperCamelCase					       =  pipe('example prompt'   ,						num_inference_steps=2 ).images[0]
           assert image is not None

           # check that there's no error when saving a pipeline with one of the models being None
           with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE )

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           UpperCamelCase					       =  pipe('example prompt'   ,						num_inference_steps=2 ).images[0]
           assert image is not None







     @unittest.skipIf(torch_device != 'cuda'   ,						'This test requires a GPU' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  self.dummy_cond_unet
           UpperCamelCase					       =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.dummy_vae
           UpperCamelCase					       =  self.dummy_text_encoder
           UpperCamelCase					       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

           # put models in fp16
           UpperCamelCase					       =  unet.half()
           UpperCamelCase					       =  vae.half()
           UpperCamelCase					       =  bert.half()

           # make sure here that pndm scheduler skips prk
           UpperCamelCase					       =  StableDiffusionPipeline(
               unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						safety_checker=_SCREAMING_SNAKE_CASE   ,						feature_extractor=self.dummy_extractor   ,						)
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  'A painting of a squirrel eating a burger'
           UpperCamelCase					       =  sd_pipe([prompt]   ,						num_inference_steps=2   ,						output_type='np' ).images

           assert image.shape == (1, 64, 64, 3)





@nightly
@require_torch_gpu
class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'   ,						safety_checker=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  (
               'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
               ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
               ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
               ' children from bahnhof zoo, detailed '
           )
           UpperCamelCase					       =  40_0366_0346
           UpperCamelCase					       =  7

           # without safety guidance (sld_guidance_scale = 0)
           UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=50   ,						output_type='np'   ,						width=512   ,						height=512   ,						sld_guidance_scale=0   ,						)

           UpperCamelCase					       =  output.images
           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

           # without safety guidance (strong configuration)
           UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=50   ,						output_type='np'   ,						width=512   ,						height=512   ,						sld_guidance_scale=2000   ,						sld_warmup_steps=7   ,						sld_threshold=0.0_2_5   ,						sld_momentum_scale=0.5   ,						sld_mom_beta=0.7   ,						)

           UpperCamelCase					       =  output.images
           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'   ,						safety_checker=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  'padme amidala taking a bath artwork, safe for work, no nudity'
           UpperCamelCase					       =  27_3497_1755
           UpperCamelCase					       =  7

           UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=50   ,						output_type='np'   ,						width=512   ,						height=512   ,						sld_guidance_scale=0   ,						)

           UpperCamelCase					       =  output.images
           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

           UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=50   ,						output_type='np'   ,						width=512   ,						height=512   ,						sld_guidance_scale=2000   ,						sld_warmup_steps=7   ,						sld_threshold=0.0_2_5   ,						sld_momentum_scale=0.5   ,						sld_mom_beta=0.7   ,						)

           UpperCamelCase					       =  output.images
           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' )
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  (
               'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
               ' leyendecker'
           )
           UpperCamelCase					       =  10_4435_5234
           UpperCamelCase					       =  12

           UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=50   ,						output_type='np'   ,						width=512   ,						height=512   ,						sld_guidance_scale=0   ,						)

           UpperCamelCase					       =  output.images
           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7

           UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe(
               [prompt]   ,						generator=_SCREAMING_SNAKE_CASE   ,						guidance_scale=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=50   ,						output_type='np'   ,						width=512   ,						height=512   ,						sld_guidance_scale=2000   ,						sld_warmup_steps=7   ,						sld_threshold=0.0_2_5   ,						sld_momentum_scale=0.5   ,						sld_mom_beta=0.7   ,						)

           UpperCamelCase					       =  output.images
           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           UpperCamelCase					       =  np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1] )
           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2











def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  {
          '^': 3,
          '*': 2,
          '/': 2,
          '%': 2,
          '+': 1,
          '-': 1,
      }  # Priority of each operator
      UpperCamelCase					       =  len(_UpperCamelCase) if (len(_UpperCamelCase) > 7) else 7

      # Print table header for output
      print(
          'Symbol'.center(8)       ,							'Stack'.center(_UpperCamelCase)       ,							'Postfix'.center(_UpperCamelCase)       ,							sep=' | '       ,							)
      print('-' * (print_width * 3 + 7))

      for x in infix:
            if x.isalpha() or x.isdigit():
                  post_fix.append(_UpperCamelCase)  # if x is Alphabet / Digit, add it to Postfix
            elif x == "(":
                  stack.append(_UpperCamelCase)  # if x is "(" push to Stack
            elif x == ")":  # if x is ")" pop stack until "(" is encountered
                  while stack[-1] != "(":
                        post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
                  stack.pop()
            else:
                  if len(_UpperCamelCase) == 0:
                        stack.append(_UpperCamelCase)  # If stack is empty, push x to stack
                  else:  # while priority of x is not > priority of element in the stack
                        while len(_UpperCamelCase) > 0 and priority[x] <= priority[stack[-1]]:
                              post_fix.append(stack.pop())  # pop stack & add to Postfix
                        stack.append(_UpperCamelCase)  # push x to stack

            print(
                x.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      while len(_UpperCamelCase) > 0:  # while stack is not empty
            post_fix.append(stack.pop())  # pop stack & add to Postfix
            print(
                ' '.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      return "".join(_UpperCamelCase)  # return Postfix as str
def 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  list(infix[::-1])  # reverse the infix equation

      for i in range(len(_UpperCamelCase)):
            if infix[i] == "(":
                  UpperCamelCase					       =  ')'  # change "(" to ")"
            elif infix[i] == ")":
                  UpperCamelCase					       =  '('  # change ")" to "("

      return (infix_2_postfix(''.join(_UpperCamelCase)))[
          ::-1
      ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
       __magic_name__   :  int        =							input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
       __magic_name__   :  Optional[Any]        =							''''''.join(Infix.split())  # Remove spaces from the input
       print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



