



'''simple docstring'''





from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase = False      ):



    """simple docstring"""





    if radian_mode:
        return [magnitude * cos(UpperCamelCase      ), magnitude * sin(UpperCamelCase      )]
    return [magnitude * cos(radians(UpperCamelCase      )      ), magnitude * sin(radians(UpperCamelCase      )      )]
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase = 10**-1      ):



    """simple docstring"""





    lowerCAmelCase__			:  NDArray[floataa]											= cross(UpperCamelCase							,   UpperCamelCase      )
    lowerCAmelCase__			:  float											= sum(UpperCamelCase      )
    return abs(UpperCamelCase      ) < eps


if __name__ == "__main__":
 # Test to check if it works
 _lowerCAmelCase										=     array(
     [
         polar_force(718.4, 180 - 30),
         polar_force(879.54, 45),
         polar_force(100, -90),
     ]
 )

 _lowerCAmelCase										=     array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 _lowerCAmelCase										=     array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(215, 180 - 45),
         polar_force(264, 90 - 30),
     ]
 )

 _lowerCAmelCase										=     array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 _lowerCAmelCase										=     array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

 _lowerCAmelCase										=     array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()







'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				IFInpaintingSuperResolutionPipeline
  __lowercase							:				int           =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  __lowercase							:				List[str]           =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
  __lowercase							:				Optional[Any]           =				PipelineTesterMixin.required_optional_params - {'''latents'''}




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """original_image""": original_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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self._test_save_load_optional_components()




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




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)


