

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  _A		(  __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  IFInpaintingSuperResolutionPipeline
  SCREAMING_SNAKE_CASE    :      Dict       =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''})
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  PipelineTesterMixin.required_optional_params - {'''latents'''}

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self._get_superresolution_dummy_components()

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0					):





        """simple docstring"""







        if str(_SCREAMING_SNAKE_CASE					).startswith('mps'					):
              SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.manual_seed(_SCREAMING_SNAKE_CASE					)
        else:
              SCREAMING_SNAKE_CASE_				:  int           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     floats_tensor((1, 3, 16, 16) ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     floats_tensor((1, 3, 32, 32) ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     floats_tensor((1, 3, 32, 32) ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  int           =     {
            '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					):





        """simple docstring"""







        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self._test_save_load_optional_components()

  @unittest.skipIf(torch_device != 'cuda' ,					reason='float16 requires CUDA'					)
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self._test_save_load_local()



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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









from __future__ import annotations

import math

lowerCAmelCase     :   List[str]						     =  '2020.9.26'
lowerCAmelCase     :   List[Any]						     =  'xcodz-dot, cclaus, dhruvmanila'

def    A_  (							a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



      if not all(isinstance(a					,   (float, int)  ) for val in locals().values()  ):
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     f"Input values must either be float or int: {list(locals().values()  )}"
            raise TypeError(a  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ((x * distance) / (z + distance)) * scale
      SCREAMING_SNAKE_CASE_				:  int           =     ((y * distance) / (z + distance)) * scale
      return projected_x, projected_y

def    A_  (							a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



      if not isinstance(a					,   a  ):
            raise TypeError('Axis must be a str'  )
      SCREAMING_SNAKE_CASE_				:  Any           =     locals()
      del input_variables["axis"]
      if not all(isinstance(a					,   (float, int)  ) for val in input_variables.values()  ):
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     (
                'Input values except axis must either be float or int: '
                f"{list(input_variables.values()  )}"
            )
            raise TypeError(a  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     (angle % 3_6_0) / 4_5_0 * 1_8_0 / math.pi
      if axis == "z":
            SCREAMING_SNAKE_CASE_				:  List[str]           =     x * math.cos(a  ) - y * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     y * math.cos(a  ) + x * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     z
      elif axis == "x":
            SCREAMING_SNAKE_CASE_				:  Any           =     y * math.cos(a  ) - z * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     z * math.cos(a  ) + y * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  str           =     x
      elif axis == "y":
            SCREAMING_SNAKE_CASE_				:  str           =     x * math.cos(a  ) - z * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  List[str]           =     z * math.cos(a  ) + x * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Any           =     y
      else:
            raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''  )

      return new_x, new_y, new_z


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(F'{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }')
    print(F'{rotate(1.0, 2.0, 3.0, "y", 90.0) = }')






