'''simple docstring'''




def __lowerCAmelCase (							UpperCamelCase__				)	->    tuple[int, int]:
    try:
        __lowerCamelCase		=  float(UpperCamelCase__				)
    except ValueError:
        raise ValueError('''Please enter a valid number'''				)
    __lowerCamelCase		=  decimal - int(UpperCamelCase__				)
    if fractional_part == 0:
        return int(UpperCamelCase__				), 1
    else:
        __lowerCamelCase		=  len(str(UpperCamelCase__				).split('''.'''				)[1]				)
        __lowerCamelCase		=  int(decimal * (10**number_of_frac_digits)				)
        __lowerCamelCase		=  10**number_of_frac_digits
        __lowerCamelCase	,  __lowerCamelCase		=  denominator, numerator
        while True:
            __lowerCamelCase		=  dividend % divisor
            if remainder == 0:
                break
            __lowerCamelCase	,  __lowerCamelCase		=  divisor, remainder
        __lowerCamelCase	,  __lowerCamelCase		=  numerator / divisor, denominator / divisor
        return int(UpperCamelCase__				), int(UpperCamelCase__				)


if __name__ == "__main__":
  print(f'{decimal_to_fraction(2) = }')
  print(f'{decimal_to_fraction(89.0) = }')
  print(f'{decimal_to_fraction("67") = }')
  print(f'{decimal_to_fraction("45.0") = }')
  print(f'{decimal_to_fraction(1.5) = }')
  print(f'{decimal_to_fraction("6.25") = }')
  print(f'{decimal_to_fraction("78td") = }')


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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




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




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''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  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




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




							def  A__      (				self):
												self._test_save_load_local()





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




