



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 			__snake_case   ( SCREAMING_SNAKE_CASE      ,SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   int       =       IFImgaImgPipeline
							UpperCamelCase__					:   Optional[int]       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
							UpperCamelCase__					:   int       =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							UpperCamelCase__					:   Tuple       =       PipelineTesterMixin.required_optional_params - {"""latents"""}


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


							def _a   (				self	,	a_	,	a_=0		):
											if str(a_		).startswith("""mps"""		):
															a__					   =		torch.manual_seed(a_		)
											else:
															a__					   =		torch.Generator(device=a_		).manual_seed(a_		)

											a__					   =		floats_tensor((1, 3, 32, 32)	,	rng=random.Random(a_		)		).to(a_		)

											a__					   =		{
											    """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		):
											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		):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)


							@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		)


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


'''simple docstring'''





from itertools import count
from math import asin, pi, sqrt






def SCREAMING_SNAKE_CASE__  (				snake_case       :				float  )   ->				float:







     """simple docstring"""
     return (
         (1 - 2 * point) * sqrt(point - point**2  ) + 2 * point + asin(sqrt(1 - point  )  )
     ) / 4






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int  )   ->				float:







     """simple docstring"""
     a					:							List[str]							= (circles_number + 1 - sqrt(2 * circles_number  )) / (
         2 * (circles_number**2 + 1)
     )
     a					:							int							= circles_number * intersection_y

     a					:							List[Any]							= intersection_x * intersection_y / 2
     a					:							List[str]							= circle_bottom_arc_integral(
         1 / 2  ) - circle_bottom_arc_integral(snake_case  )

     return triangle_area + concave_region_area






def SCREAMING_SNAKE_CASE__  (				snake_case       :				float = 1 / 1_000  )   ->				int:







     """simple docstring"""
     a					:							str							= (1 - pi / 4) / 4

     for n in count(1  ):
          if concave_triangle_area(snake_case  ) / l_section_area < fraction:
               return n

     return -1


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

