


'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCamelCase												=						False




class 				UpperCAmelCase			(			unittest.TestCase						):
 pass




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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Any      =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''			,       torch_dtype=torch.floataa )
    pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:str      =      load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )

    UpperCAmelCase__	:str      =      torch.manual_seed(0 )
    UpperCAmelCase__	:int      =      pipe.dual_guided(
        prompt='''first prompt'''			,       image=__lowerCamelCase			,       text_to_image_strength=0.75			,       generator=__lowerCamelCase			,       guidance_scale=7.5			,       num_inference_steps=2			,       output_type='''numpy'''			,       ).images

    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(__lowerCamelCase )
       UpperCAmelCase__	:Any      =      VersatileDiffusionPipeline.from_pretrained(__lowerCamelCase			,       torch_dtype=torch.floataa )
    pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      generator.manual_seed(0 )
    UpperCAmelCase__	:Any      =      pipe.dual_guided(
        prompt='''first prompt'''			,       image=__lowerCamelCase			,       text_to_image_strength=0.75			,       generator=__lowerCamelCase			,       guidance_scale=7.5			,       num_inference_steps=2			,       output_type='''numpy'''			,       ).images

    assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[int]      =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''			,       torch_dtype=torch.floataa )
    pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:Optional[int]      =      '''cyberpunk 2077'''
    UpperCAmelCase__	:Any      =      load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
    UpperCAmelCase__	:Any      =      torch.manual_seed(0 )
    UpperCAmelCase__	:Optional[int]      =      pipe.dual_guided(
        prompt=__lowerCamelCase			,       image=__lowerCamelCase			,       text_to_image_strength=0.75			,       generator=__lowerCamelCase			,       guidance_scale=7.5			,       num_inference_steps=5_0			,       output_type='''numpy'''			,       ).images

    UpperCAmelCase__	:Tuple      =      image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    UpperCAmelCase__	:Optional[Any]      =      np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01] )

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

    UpperCAmelCase__	:Tuple      =      '''A painting of a squirrel eating a burger '''
    UpperCAmelCase__	:List[str]      =      torch.manual_seed(0 )
    UpperCAmelCase__	:Union[str, Any]      =      pipe.text_to_image(
        prompt=__lowerCamelCase			,       generator=__lowerCamelCase			,       guidance_scale=7.5			,       num_inference_steps=5_0			,       output_type='''numpy''' ).images

    UpperCAmelCase__	:Tuple      =      image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    UpperCAmelCase__	:Any      =      np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )

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

    UpperCAmelCase__	:Optional[int]      =      pipe.image_variation(__lowerCamelCase			,       generator=__lowerCamelCase			,       output_type='''numpy''' ).images

    UpperCAmelCase__	:List[Any]      =      image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    UpperCAmelCase__	:str      =      np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56] )

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







'''simple docstring'''





from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str						):
   UpperCAmelCase__	:Any      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      0
   for i in range(len(UpperCamelCase_						)						):
      if lista[i] != lista[i]:
         count += 1
         UpperCAmelCase__	:Union[str, Any]      =      '''_'''
   if count > 1:
      return False
   else:
      return "".join(UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:int      =      []
   while True:
      UpperCAmelCase__	:Dict      =      ['''$'''] * len(UpperCamelCase_						)
      UpperCAmelCase__	:List[str]      =      []
      for i in range(len(UpperCamelCase_						)						):
         for j in range(i + 1,   len(UpperCamelCase_						)						):
            UpperCAmelCase__	:Optional[Any]      =      compare_string(binary[i],   binary[j]						)
            if k is False:
               UpperCAmelCase__	:Any      =      '''*'''
               UpperCAmelCase__	:List[Any]      =      '''*'''
               temp.append('''X'''						)
      for i in range(len(UpperCamelCase_						)						):
         if checka[i] == "$":
            pi.append(binary[i]						)
      if len(UpperCamelCase_						) == 0:
         return pi
      UpperCAmelCase__	:Tuple      =      list(set(UpperCamelCase_						)						)


def        a__     (					UpperCamelCase_						:      int,   UpperCamelCase_						:      Sequence[float]						):
   UpperCAmelCase__	:int      =      []
   for minterm in minterms:
      UpperCAmelCase__	:int      =      ''''''
      for _ in range(UpperCamelCase_						):
         UpperCAmelCase__	:Optional[int]      =      str(minterm % 2						) + string
         minterm //= 2
      temp.append(UpperCamelCase_						)
   return temp


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str,   UpperCamelCase_						:      int						):
   UpperCAmelCase__	:Dict      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      0
   for i in range(len(UpperCamelCase_						)						):
      if lista[i] != lista[i]:
         count_n += 1
   return count_n == count


def        a__     (					UpperCamelCase_						:      list[list[int]],   UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:Optional[Any]      =      []
   UpperCAmelCase__	:List[Any]      =      [0] * len(UpperCamelCase_						)
   for i in range(len(chart[0]						)						):
      UpperCAmelCase__	:Optional[Any]      =      0
      UpperCAmelCase__	:Union[str, Any]      =      -1
      for j in range(len(UpperCamelCase_						)						):
         if chart[j][i] == 1:
            count += 1
            UpperCAmelCase__	:Any      =      j
      if count == 1:
         UpperCAmelCase__	:Any      =      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__	:int      =      0
         temp.append(prime_implicants[i]						)
   while True:
      UpperCAmelCase__	:Optional[int]      =      0
      UpperCAmelCase__	:Dict      =      -1
      UpperCAmelCase__	:Optional[Any]      =      0
      for i in range(len(UpperCamelCase_						)						):
         UpperCAmelCase__	:str      =      chart[i].count(1						)
         if count_n > max_n:
            UpperCAmelCase__	:Any      =      count_n
            UpperCAmelCase__	:List[Any]      =      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__	:Optional[int]      =      0


def        a__     (					UpperCamelCase_						:      list[str],   UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:List[str]      =      [[0 for x in range(len(UpperCamelCase_						)						)] for x in range(len(UpperCamelCase_						)						)]
   for i in range(len(UpperCamelCase_						)						):
      UpperCAmelCase__	:Tuple      =      prime_implicants[i].count('''_'''						)
      for j in range(len(UpperCamelCase_						)						):
         if is_for_table(prime_implicants[i],   binary[j],   UpperCamelCase_						):
            UpperCAmelCase__	:List[str]      =      1

   return chart


def        a__     (					):
   UpperCAmelCase__	:int      =      int(input('''Enter the no. of variables\n'''						)						)
   UpperCAmelCase__	:Tuple      =      [
       float(UpperCamelCase_						)
       for x in input(
           '''Enter the decimal representation of Minterms \'Spaces Separated\'\n'''						).split()
   ]
   UpperCAmelCase__	:Union[str, Any]      =      decimal_to_binary(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Optional[Any]      =      check(UpperCamelCase_						)
   print('''Prime Implicants are:'''						)
   print(UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      prime_implicant_chart(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Dict      =      selection(UpperCamelCase_,   UpperCamelCase_						)
   print('''Essential Prime Implicants are:'''						)
   print(UpperCamelCase_						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



