


'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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				( snake_case__       ,      snake_case__       ,      unittest.TestCase ):
    """simple docstring"""
    a									=					IFImgaImgPipeline
    a									=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    a									=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a									=					PipelineTesterMixin.required_optional_params - {"latents"}


    def      A					(						self    :							Optional[Any]):
           return self._get_dummy_components()


    def      A					(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							int=0):
           if str(__snake_case).startswith('mps'):
                  _A  :			int											=      torch.manual_seed(__snake_case)
           else:
                  _A  :			str											=      torch.Generator(device=__snake_case).manual_seed(__snake_case)

           _A  :			Any											=      floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(__snake_case)).to(__snake_case)

           _A  :			List[Any]											=      {
               'prompt': 'A painting of a squirrel eating a burger',
               'image': image,
               'generator': generator,
               'num_inference_steps': 2,
               'output_type': 'numpy',
           }

           return inputs


    def      A					(						self    :							Tuple):
           self._test_save_load_optional_components()


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()   ,							reason='XFormers attention is only available with CUDA and `xformers` installed'   ,							)
    def      A					(						self    :							List[str]):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)


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


    @unittest.skipIf(torch_device != 'cuda'   ,							reason='float16 requires CUDA')
    def      A					(						self    :							Optional[Any]):
           super().test_floataa_inference(expected_max_diff=1e-1)


    def      A					(						self    :							List[str]):
           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


    def      A					(						self    :							List[str]):
           self._test_save_load_local()


    def      A					(						self    :							List[str]):
           self._test_inference_batch_single_identical(
               expected_max_diff=1e-2   ,							)










'''simple docstring'''







import itertools






def    lowerCAmelCase__ (       lowerCamelCase  :    int = 1000000     ):
       _A  :			str											=      [1]

       for i in itertools.count(len(lowerCamelCase     )     ):
              _A  :			Optional[Any]											=      0
              for j in itertools.count(1     ):
                     _A  :			int											=      -1 if j % 2 == 0 else +1
                     _A  :			Optional[Any]											=      (j * j * 3 - j) // 2
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number
                     index += j
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number

              if item == 0:
                     return i
              partitions.append(lowerCamelCase     )

       return 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






