





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__   (							__UpperCAmelCase			,  __UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Union[str, Any]	  =  IFInpaintingSuperResolutionPipeline
   __A       :    Dict	  =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
   __A       :    Any	  =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}     )
   __A       :    Union[str, Any]	  =  PipelineTesterMixin.required_optional_params - {'''latents'''}

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          return self._get_superresolution_dummy_components()

   def 							__lowercase		( self ,  lowercase ,  lowercase=0)      ->						str:







          '''simple docstring'''

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

          a__						:     str			=    floats_tensor((1, 3, 16, 16) ,  rng=random.Random(_a)).to(_a)
          a__						:     Dict			=    floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_a)).to(_a)
          a__						:     List[str]			=    floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_a)).to(_a)

          a__						:     List[Any]			=    {
              '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 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          self._test_save_load_optional_components()

   @unittest.skipIf(torch_device != 'cuda' ,  reason='float16 requires CUDA')
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

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

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          self._test_save_load_local()

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

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









from __future__ import annotations

from collections.abc import Callable



def A_     (		A__		,				A__		,				A__		,				A__ = 100		,				)  ->		float:
       a__						:     Dict			=    x_start
       a__						:     Any			=    fnc(A__    )
       a__						:     Optional[int]			=    0.0
       for _ in range(A__    ):
              # Approximates small segments of curve as linear and solve
              # for trapezoidal area
              a__						:     Union[str, Any]			=    (x_end - x_start) / steps + xa
              a__						:     str			=    fnc(A__    )
              area += abs(fxa + fxa    ) * (xa - xa) / 2
              # Increment step
              a__						:     Optional[Any]			=    xa
              a__						:     Optional[int]			=    fxa
       return area


if __name__ == "__main__":



    def A_     (		A__    )  ->		List[str]:
           return x**3 + x**2

    print("""f(x) = x^3 + x^2""")
    print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
    lowercase				:	Union[str, Any]      						=  1_0
    while i <= 1_0_0_0_0_0:
        print(F"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
        i *= 1_0


