
"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 			UpperCamelCase      (							unittest.TestCase  ):
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       torch.manual_seed(0				)
       _SCREAMING_SNAKE_CASE   :				List[str]         = UNetaDModel(
           block_out_channels=(32, 64)  ,     layers_per_block=2  ,     sample_size=32  ,     in_channels=3  ,     out_channels=3  ,     down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,     up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,     )
       return model




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]         = self.dummy_uncond_unet
       _SCREAMING_SNAKE_CASE   :				List[Any]         = KarrasVeScheduler()

       _SCREAMING_SNAKE_CASE   :				Any         = KarrasVePipeline(unet=snake_case__  ,     scheduler=snake_case__				)
       pipe.to(snake_case__				)
       pipe.set_progress_bar_config(disable=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Dict         = torch.manual_seed(0				)
       _SCREAMING_SNAKE_CASE   :				Dict         = pipe(num_inference_steps=2  ,     generator=snake_case__  ,     output_type="numpy"				).images

       _SCREAMING_SNAKE_CASE   :				str         = torch.manual_seed(0				)
       _SCREAMING_SNAKE_CASE   :				int         = pipe(num_inference_steps=2  ,     generator=snake_case__  ,     output_type="numpy"  ,     return_dict=snake_case__				)[0]

       _SCREAMING_SNAKE_CASE   :				str         = image[0, -3:, -3:, -1]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]         = image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       _SCREAMING_SNAKE_CASE   :				int         = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

       assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
@slow
@require_torch
class 			UpperCamelCase      (							unittest.TestCase  ):
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]         = "google/ncsnpp-celebahq-256"
       _SCREAMING_SNAKE_CASE   :				List[Any]         = UNetaDModel.from_pretrained(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]         = KarrasVeScheduler()

       _SCREAMING_SNAKE_CASE   :				Optional[int]         = KarrasVePipeline(unet=snake_case__  ,     scheduler=snake_case__				)
       pipe.to(snake_case__				)
       pipe.set_progress_bar_config(disable=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]         = torch.manual_seed(0				)
       _SCREAMING_SNAKE_CASE   :				List[Any]         = pipe(num_inference_steps=20  ,     generator=snake_case__  ,     output_type="numpy"				).images

       _SCREAMING_SNAKE_CASE   :				List[Any]         = image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       _SCREAMING_SNAKE_CASE   :				Optional[int]         = np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]				)

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





"""simple docstring"""
import operator as op





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  _SCREAMING_SNAKE_CASE   :				str							    = lambda lowerCamelCase__,						lowerCamelCase__				: int(x / y				)  # noqa: E731 integer division operation
  _SCREAMING_SNAKE_CASE   :				Any							    = {
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8				),						"Action".center(1_2				),						"Stack",						sep=" | "				)
  print("-" * (3_0 + len(lowerCamelCase__				))				)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(lowerCamelCase__				)  # append x to stack
      # output in tabular format
      print(x.rjust(8				),						("push(" + x + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)
    else:
      _SCREAMING_SNAKE_CASE   :				Dict							    = stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8				),						("pop(" + b + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)

      _SCREAMING_SNAKE_CASE   :				Any							    = stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8				),						("pop(" + a + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)

      stack.append(
          str(opr[x](int(lowerCamelCase__				),						int(lowerCamelCase__				)				)				)				)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8				),						("push(" + a + x + b + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | ",						)

  return int(stack[0]				)


if __name__ == "__main__":
      lowercase_       :						int     					=	input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
      print('''\n\tResult = ''', solve(Postfix))



