




'''simple docstring'''


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       A   (		_lowerCamelCase					,_lowerCamelCase					,unittest.TestCase   ):
       lowercase_          =    IFInpaintingPipeline
       lowercase_          =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
       lowercase_          =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       lowercase_          =    PipelineTesterMixin.required_optional_params - {'latents'}







       def        __lowerCAmelCase      (  self	:  str )      ->							int:


              """simple docstring"""


              return self._get_dummy_components()







       def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[int]=0 )      ->							str:


              """simple docstring"""


              if str(lowercase_ ).startswith('''mps''' ):
                     _a     					=    torch.manual_seed(lowercase_ )
              else:
                     _a     					=    torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )

              _a     					=    floats_tensor((1, 3, 32, 32)							,      rng=random.Random(lowercase_ ) ).to(lowercase_ )
              _a     					=    floats_tensor((1, 3, 32, 32)							,      rng=random.Random(lowercase_ ) ).to(lowercase_ )

              _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	:  Optional[Any] )      ->							int:


              """simple docstring"""


              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )







       def        __lowerCAmelCase      (  self	:  Any )      ->							Any:


              """simple docstring"""


              self._test_save_load_optional_components()







       @unittest.skipIf(torch_device != '''cuda'''							,      reason='''float16 requires CUDA''' )
       def        __lowerCAmelCase      (  self	:  Dict )      ->							int:


              """simple docstring"""


              super().test_save_load_floataa(expected_max_diff=1e-1 )







       def        __lowerCAmelCase      (  self	:  Dict )      ->							int:


              """simple docstring"""


              self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )







       def        __lowerCAmelCase      (  self	:  Any )      ->							Tuple:


              """simple docstring"""


              self._test_save_load_local()







       def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Dict:


              """simple docstring"""


              self._test_inference_batch_single_identical(
                  expected_max_diff=1e-2							,      )







'''simple docstring'''


def 		snake_case_   (UpperCamelCase	:  str			,						UpperCamelCase	:  Any				):





							'''simple docstring'''


							return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  str=0				):





							'''simple docstring'''


							return sorted(UpperCamelCase			,						key=lambda UpperCamelCase				: x[column]				)
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Any			,						UpperCamelCase	:  Union[str, Any]=float('''inf'''				)				):





							'''simple docstring'''


							for i in range(points_counts - 1				):
														for j in range(i + 1			,						UpperCamelCase				):
																					_a     					=    euclidean_distance_sqr(points[i]			,						points[j]				)
																					if current_dis < min_dis:
																												_a     					=    current_dis
							return min_dis
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  Tuple			,						UpperCamelCase	:  List[str]=float('''inf'''				)				):





							'''simple docstring'''


							for i in range(min(6			,						points_counts - 1				)			,						UpperCamelCase				):
														for j in range(max(0			,						i - 6				)			,						UpperCamelCase				):
																					_a     					=    euclidean_distance_sqr(points[i]			,						points[j]				)
																					if current_dis < min_dis:
																												_a     					=    current_dis
							return min_dis
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  int				):





							'''simple docstring'''


							if points_counts <= 3:
														return dis_between_closest_pair(UpperCamelCase			,						UpperCamelCase				)

							# recursion
							_a     					=    points_counts // 2
							_a     					=    closest_pair_of_points_sqr(
							    UpperCamelCase			,						points_sorted_on_y[:mid]			,						UpperCamelCase				)
							_a     					=    closest_pair_of_points_sqr(
							    UpperCamelCase			,						points_sorted_on_y[mid:]			,						points_counts - mid				)
							_a     					=    min(UpperCamelCase			,						UpperCamelCase				)
							_a     					=    []
							for point in points_sorted_on_x:
														if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
																					cross_strip.append(UpperCamelCase				)

							_a     					=    dis_between_closest_in_strip(
							    UpperCamelCase			,						len(UpperCamelCase				)			,						UpperCamelCase				)
							return min(UpperCamelCase			,						UpperCamelCase				)
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  List[str]				):





							'''simple docstring'''


							_a     					=    column_based_sort(UpperCamelCase			,						column=0				)
							_a     					=    column_based_sort(UpperCamelCase			,						column=1				)
							return (
							    closest_pair_of_points_sqr(
							        UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				)
							) ** 0.5


if __name__ == "__main__":
				_snake_case				:				int       							=		[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
				print('Distance:', closest_pair_of_points(points, len(points)))

