'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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'''






  _UpperCamelCase						= IFSuperResolutionPipeline
  _UpperCamelCase						= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
  _UpperCamelCase						= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _UpperCamelCase						= PipelineTesterMixin.required_optional_params - {'latents'}

  def 		_lowerCamelCase	(self  : Tuple    )   ->       Dict:
        return self._get_superresolution_dummy_components()

  def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : str=0    )   ->       Dict:
        if str(lowerCAmelCase_    ).startswith("mps"    ):
              snake_case_						       =							torch.manual_seed(lowerCAmelCase_    )
        else:
              snake_case_						       =							torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )

        snake_case_						       =							floats_tensor((1, 3, 32, 32)	, rng=random.Random(lowerCAmelCase_    )    ).to(lowerCAmelCase_    )

        snake_case_						       =							{
            "prompt": "A painting of a squirrel eating a burger",
            "image": 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 		_lowerCamelCase	(self  : List[Any]    )   ->       int:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )

  def 		_lowerCamelCase	(self  : int    )   ->       str:
        self._test_save_load_optional_components()

  @unittest.skipIf(torch_device != "cuda"	, reason="float16 requires CUDA"    )
  def 		_lowerCamelCase	(self  : str    )   ->       List[str]:
        # 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 		_lowerCamelCase	(self  : Dict    )   ->       Optional[Any]:
        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2    )

  def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       List[Any]:
        self._test_save_load_local()


  def 		_lowerCamelCase	(self  : List[Any]    )   ->       List[Any]:
        self._test_inference_batch_single_identical(
            expected_max_diff=1E-2	, )



'''simple docstring'''





from __future__ import annotations

import random



class  lowerCAmelCase__   :



  '''simple docstring'''






  _UpperCamelCase						= 6

  def __init__(self  : List[Any]    )   ->       Optional[Any]:
        snake_case_						       =							list(range(1	, Dice.NUM_SIDES + 1    )    )


  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       int:
        return random.choice(self.sides    )

def   snake_case_       (							_a     :							int ,					_a     :							int = 2			):
      snake_case_						       =							[Dice() for i in range(_a			)]
      snake_case_						       =							[0] * (len(_a			) * Dice.NUM_SIDES + 1)
      for _ in range(_a			):
            count_of_sum[sum(dice.roll() for dice in dices			)] += 1
      snake_case_						       =							[round((count * 100) / num_throws ,					2			) for count in count_of_sum]
      return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
     import doctest

     doctest.testmod()

