





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			IFInpaintingPipeline
     __magic_name__   =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
     __magic_name__   =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __magic_name__   =			PipelineTesterMixin.required_optional_params - {'latents'}




     def        lowerCAmelCase__		(    self							):
         return self._get_dummy_components()




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0							):
         if str(snake_case_							).startswith('mps'							):
             _A     =							torch.manual_seed(snake_case_							)
         else:
             _A     =							torch.Generator(device=snake_case_							).manual_seed(snake_case_							)

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

         _A     =							{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': 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        lowerCAmelCase__		(    self							):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)




     def        lowerCAmelCase__		(    self							):
         self._test_save_load_optional_components()




     @unittest.skipIf(torch_device != 'cuda'				,     reason='float16 requires CUDA'							)
     def        lowerCAmelCase__		(    self							):
         # 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							):
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)




     def        lowerCAmelCase__		(    self							):
         self._test_save_load_local()




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







'''simple docstring'''



from itertools import product


def A (__lowerCamelCase :int					,      __lowerCamelCase :int						):
						_lowerCAmelCase    		=					sides_number
						_lowerCAmelCase    		=					max_face_number * dice_number
						_lowerCAmelCase    		=					[0] * (max_total + 1)

						_lowerCAmelCase    		=					1
						_lowerCAmelCase    		=					range(__lowerCamelCase					,      max_face_number + 1						)
						for dice_numbers in product(__lowerCamelCase					,      repeat=__lowerCamelCase						):
												_lowerCAmelCase    		=					sum(__lowerCamelCase						)
												totals_frequencies[total] += 1

						return totals_frequencies


def A ():
						_lowerCAmelCase    		=					total_frequency_distribution(
						    sides_number=4					,      dice_number=9						)
						_lowerCAmelCase    		=					total_frequency_distribution(
						    sides_number=6					,      dice_number=6						)

						_lowerCAmelCase    		=					0
						_lowerCAmelCase    		=					9
						_lowerCAmelCase    		=					4 * 9
						_lowerCAmelCase    		=					6
						for peter_total in range(__lowerCamelCase					,      max_peter_total + 1						):
												peter_wins_count += peter_totals_frequencies[peter_total] * sum(
												    colin_totals_frequencies[min_colin_total:peter_total]						)

						_lowerCAmelCase    		=					(4**9) * (6**6)
						_lowerCAmelCase    		=					peter_wins_count / total_games_number

						_lowerCAmelCase    		=					round(__lowerCamelCase					,      ndigits=7						)

						return rounded_peter_win_probability


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