


'''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      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  IFInpaintingSuperResolutionPipeline
  _lowerCamelCase		    =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
  _lowerCamelCase		    =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}			)
  _lowerCamelCase		    =  PipelineTesterMixin.required_optional_params - {"latents"}






  def   snake_case		(    self     ):







   """simple docstring"""
   return self._get_superresolution_dummy_components()






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=0     ):







   """simple docstring"""
   if str(UpperCamelCase     ).startswith("mps"     ):
    lowerCamelCase_      =  torch.manual_seed(UpperCamelCase     )
   else:
    lowerCamelCase_      =  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )

   lowerCamelCase_      =  floats_tensor((1, 3, 16, 16)			,  rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
   lowerCamelCase_      =  floats_tensor((1, 3, 32, 32)			,  rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
   lowerCamelCase_      =  floats_tensor((1, 3, 32, 32)			,  rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )

   lowerCamelCase_      =  {
       "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   snake_case		(    self     ):







   """simple docstring"""
   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self._test_save_load_optional_components()






  @unittest.skipIf(torch_device != "cuda"			,  reason="float16 requires CUDA"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   # 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   snake_case		(    self     ):







   """simple docstring"""
   self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self._test_save_load_local()







  def   snake_case		(    self     ):







   """simple docstring"""
   self._test_inference_batch_single_identical(
       expected_max_diff=1e-2			,  )







'''simple docstring'''

import math





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  math.loga(math.sqrt(4 * positive_integer + 1		) / 2 + 1 / 2		)

 return exponent == int(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						float = 1 / 12345		):
 lowerCamelCase_      =  0
 lowerCamelCase_      =  0

 lowerCamelCase_      =  3
 while True:
  lowerCamelCase_      =  (integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(UpperCAmelCase_		):
   lowerCamelCase_      =  int(UpperCAmelCase_		)
   total_partitions += 1
   if check_partition_perfect(UpperCAmelCase_		):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(UpperCAmelCase_		)
  integer += 1


if __name__ == "__main__":
     print(f'''{solution() = }''')



