



from __future__ import annotations

from collections.abc import Sequence
from typing import Literal






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Union[str, Any]:
      __lowercase					=				list(SCREAMING_SNAKE_CASE__   )
      __lowercase					=				list(SCREAMING_SNAKE_CASE__   )
      __lowercase					=				0
      for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
            if lista[i] != lista[i]:
                  count += 1
                  __lowercase					=				"""_"""
      if count > 1:
            return False
      else:
            return "".join(SCREAMING_SNAKE_CASE__   )






def  UpperCAmelCase__    (    lowercase__   ) ->      Any:
      __lowercase					=				[]
      while True:
            __lowercase					=				["""$"""] * len(SCREAMING_SNAKE_CASE__   )
            __lowercase					=				[]
            for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
                  for j in range(i + 1						,       len(SCREAMING_SNAKE_CASE__   )   ):
                        __lowercase					=				compare_string(binary[i]						,       binary[j]   )
                        if k is False:
                              __lowercase					=				"""*"""
                              __lowercase					=				"""*"""
                              temp.append("""X"""   )
            for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
                  if checka[i] == "$":
                        pi.append(binary[i]   )
            if len(SCREAMING_SNAKE_CASE__   ) == 0:
                  return pi
            __lowercase					=				list(set(SCREAMING_SNAKE_CASE__   )   )






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      List[Any]:
      __lowercase					=				[]
      for minterm in minterms:
            __lowercase					=				""""""
            for _ in range(SCREAMING_SNAKE_CASE__   ):
                  __lowercase					=				str(minterm % 2   ) + string
                  minterm //= 2
            temp.append(SCREAMING_SNAKE_CASE__   )
      return temp






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      List[str]:
      __lowercase					=				list(SCREAMING_SNAKE_CASE__   )
      __lowercase					=				list(SCREAMING_SNAKE_CASE__   )
      __lowercase					=				0
      for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      str:
      __lowercase					=				[]
      __lowercase					=				[0] * len(SCREAMING_SNAKE_CASE__   )
      for i in range(len(chart[0]   )   ):
            __lowercase					=				0
            __lowercase					=				-1
            for j in range(len(SCREAMING_SNAKE_CASE__   )   ):
                  if chart[j][i] == 1:
                        count += 1
                        __lowercase					=				j
            if count == 1:
                  __lowercase					=				1
      for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
            if select[i] == 1:
                  for j in range(len(chart[0]   )   ):
                        if chart[i][j] == 1:
                              for k in range(len(SCREAMING_SNAKE_CASE__   )   ):
                                    __lowercase					=				0
                  temp.append(prime_implicants[i]   )
      while True:
            __lowercase					=				0
            __lowercase					=				-1
            __lowercase					=				0
            for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
                  __lowercase					=				chart[i].count(1   )
                  if count_n > max_n:
                        __lowercase					=				count_n
                        __lowercase					=				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(SCREAMING_SNAKE_CASE__   )   ):
                              __lowercase					=				0






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      List[str]:
      __lowercase					=				[[0 for x in range(len(SCREAMING_SNAKE_CASE__   )   )] for x in range(len(SCREAMING_SNAKE_CASE__   )   )]
      for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
            __lowercase					=				prime_implicants[i].count("""_"""   )
            for j in range(len(SCREAMING_SNAKE_CASE__   )   ):
                  if is_for_table(prime_implicants[i]						,       binary[j]						,       SCREAMING_SNAKE_CASE__   ):
                        __lowercase					=				1

      return chart






def  UpperCAmelCase__    (    ) ->      Optional[int]:
      __lowercase					=				int(input("""Enter the no. of variables\n"""   )   )
      __lowercase					=				[
          float(SCREAMING_SNAKE_CASE__   )
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""   ).split()
      ]
      __lowercase					=				decimal_to_binary(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__   )

      __lowercase					=				check(SCREAMING_SNAKE_CASE__   )
      print("""Prime Implicants are:"""   )
      print(SCREAMING_SNAKE_CASE__   )
      __lowercase					=				prime_implicant_chart(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__   )

      __lowercase					=				selection(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__   )
      print("""Essential Prime Implicants are:"""   )
      print(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()










import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			IFImgaImgSuperResolutionPipeline
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}			)
			lowercase__      :  Tuple      =			PipelineTesterMixin.required_optional_params - {"""latents"""}





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									return self._get_superresolution_dummy_components()





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]=0   )						->					Union[str, Any]:
									"""simple docstring"""







									if str(lowercase   ).startswith("""mps"""   ):
															__lowercase					=				torch.manual_seed(lowercase   )
									else:
															__lowercase					=				torch.Generator(device=lowercase   ).manual_seed(lowercase   )

									__lowercase					=				floats_tensor((1, 3, 32, 32)			,     rng=random.Random(lowercase   )   ).to(lowercase   )
									__lowercase					=				floats_tensor((1, 3, 16, 16)			,     rng=random.Random(lowercase   )   ).to(lowercase   )

									__lowercase					=				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """original_image""": original_image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}

									return inputs





			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()			,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,     )
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )





			def 		snake_case__			(				self	:       List[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									self._test_save_load_optional_components()





			@unittest.skipIf(torch_device != """cuda"""			,     reason="""float16 requires CUDA"""   )
			def 		snake_case__			(				self	:       List[Any]   )						->					Dict:
									"""simple docstring"""







									super().test_save_load_floataa(expected_max_diff=1E-1   )





			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									self._test_save_load_local()







			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2			,     )





