'''simple docstring'''
from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  list(_UpperCamelCase					)
      UpperCAmelCase_            =  list(_UpperCamelCase					)
      UpperCAmelCase_            =  0
      for i in range(len(_UpperCamelCase					)					):
            if lista[i] != lista[i]:
                  count += 1
                  UpperCAmelCase_            =  '''_'''
      if count > 1:
            return False
      else:
            return "".join(_UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      while True:
            UpperCAmelCase_            =  ['''$'''] * len(_UpperCamelCase					)
            UpperCAmelCase_            =  []
            for i in range(len(_UpperCamelCase					)					):
                  for j in range(i + 1							,    len(_UpperCamelCase					)					):
                        UpperCAmelCase_            =  compare_string(binary[i]							,    binary[j]					)
                        if k is False:
                              UpperCAmelCase_            =  '''*'''
                              UpperCAmelCase_            =  '''*'''
                              temp.append('''X'''					)
            for i in range(len(_UpperCamelCase					)					):
                  if checka[i] == "$":
                        pi.append(binary[i]					)
            if len(_UpperCamelCase					) == 0:
                  return pi
            UpperCAmelCase_            =  list(set(_UpperCamelCase					)					)



def  __lowerCamelCase       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  Sequence[float]					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      for minterm in minterms:
            UpperCAmelCase_            =  ''''''
            for _ in range(_UpperCamelCase					):
                  UpperCAmelCase_            =  str(minterm % 2					) + string
                  minterm //= 2
            temp.append(_UpperCamelCase					)
      return temp



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  list(_UpperCamelCase					)
      UpperCAmelCase_            =  list(_UpperCamelCase					)
      UpperCAmelCase_            =  0
      for i in range(len(_UpperCamelCase					)					):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]							,    _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      UpperCAmelCase_            =  [0] * len(_UpperCamelCase					)
      for i in range(len(chart[0]					)					):
            UpperCAmelCase_            =  0
            UpperCAmelCase_            =  -1
            for j in range(len(_UpperCamelCase					)					):
                  if chart[j][i] == 1:
                        count += 1
                        UpperCAmelCase_            =  j
            if count == 1:
                  UpperCAmelCase_            =  1
      for i in range(len(_UpperCamelCase					)					):
            if select[i] == 1:
                  for j in range(len(chart[0]					)					):
                        if chart[i][j] == 1:
                              for k in range(len(_UpperCamelCase					)					):
                                    UpperCAmelCase_            =  0
                  temp.append(prime_implicants[i]					)
      while True:
            UpperCAmelCase_            =  0
            UpperCAmelCase_            =  -1
            UpperCAmelCase_            =  0
            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_            =  chart[i].count(1					)
                  if count_n > max_n:
                        UpperCAmelCase_            =  count_n
                        UpperCAmelCase_            =  i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]					)

            for i in range(len(chart[0]					)					):
                  if chart[rem][i] == 1:
                        for j in range(len(_UpperCamelCase					)					):
                              UpperCAmelCase_            =  0



def  __lowerCamelCase       (      _UpperCamelCase  :  list[str]							,    _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  [[0 for x in range(len(_UpperCamelCase					)					)] for x in range(len(_UpperCamelCase					)					)]
      for i in range(len(_UpperCamelCase					)					):
            UpperCAmelCase_            =  prime_implicants[i].count('''_'''					)
            for j in range(len(_UpperCamelCase					)					):
                  if is_for_table(prime_implicants[i]							,    binary[j]							,    _UpperCamelCase					):
                        UpperCAmelCase_            =  1

      return chart



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  int(input('''Enter the no. of variables\n'''					)					)
      UpperCAmelCase_            =  [
          float(_UpperCamelCase					)
          for x in input(
              '''Enter the decimal representation of Minterms \'Spaces Separated\'\n'''					).split()
      ]
      UpperCAmelCase_            =  decimal_to_binary(_UpperCamelCase							,    _UpperCamelCase					)

      UpperCAmelCase_            =  check(_UpperCamelCase					)
      print('''Prime Implicants are:'''					)
      print(_UpperCamelCase					)
      UpperCAmelCase_            =  prime_implicant_chart(_UpperCamelCase							,    _UpperCamelCase					)

      UpperCAmelCase_            =  selection(_UpperCamelCase							,    _UpperCamelCase					)
      print('''Essential Prime Implicants are:'''					)
      print(_UpperCamelCase					)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()






'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







@slow
@require_torch_gpu
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def  lowerCAmelCase__ (      self			:       str  )				->List[str]:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_euler'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe([prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=9.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]  )

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






  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_euler'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe([prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=9.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-1






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Optional[int]:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_dpmpp_2m'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe(
            [prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							num_inference_steps=15  ,							output_type='''np'''  ,							use_karras_sigmas=UpperCAmelCase__  ,							)

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array(
            [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]  )

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





