



'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				IFInpaintingSuperResolutionPipeline
  __lowercase							:				int           =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  __lowercase							:				List[str]           =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
  __lowercase							:				Optional[Any]           =				PipelineTesterMixin.required_optional_params - {'''latents'''}




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """original_image""": original_image,
          """mask_image""": mask_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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self._test_save_load_optional_components()




  @unittest.skipIf(torch_device != """cuda"""  ,reason="""float16 requires CUDA"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)







'''simple docstring'''





import os
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= len(grid[0]      )
    lowerCAmelCase__			:  int											= len(UpperCamelCase      )

    lowerCAmelCase__			:  Optional[int]											= 0
    lowerCAmelCase__			:  Tuple											= 0
    lowerCAmelCase__			:  Optional[Any]											= 0

    # Check vertically, horizontally, diagonally at the same time (only works
    # for nxn grid)
    for i in range(UpperCamelCase      ):
        for j in range(n_rows - 3      ):
            lowerCAmelCase__			:  str											= grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
            lowerCAmelCase__			:  Optional[int]											= grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

            # Left-to-right diagonal (\) product
            if i < n_columns - 3:
                lowerCAmelCase__			:  Optional[int]											= (
                    grid[i][j]
                    * grid[i + 1][j + 1]
                    * grid[i + 2][j + 2]
                    * grid[i + 3][j + 3]
                )

            # Right-to-left diagonal(/) product
            if i > 2:
                lowerCAmelCase__			:  Tuple											= (
                    grid[i][j]
                    * grid[i - 1][j + 1]
                    * grid[i - 2][j + 2]
                    * grid[i - 3][j + 3]
                )

            lowerCAmelCase__			:  Dict											= max(
                UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
            if max_product > largest:
                lowerCAmelCase__			:  Any											= max_product

    return largest
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= []
    with open(os.path.dirname(UpperCamelCase      ) + """/grid.txt"""      ) as file:
        for line in file:
            grid.append(line.strip("""\n"""      ).split(""" """      )      )

    lowerCAmelCase__			:  Dict											= [[int(UpperCamelCase      ) for i in grid[j]] for j in range(len(UpperCamelCase      )      )]

    return largest_product(UpperCamelCase      )


if __name__ == "__main__":
 print(solution())


