
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 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  IFInpaintingPipeline
     SCREAMING_SNAKE_CASE_		   =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     SCREAMING_SNAKE_CASE_		   =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     SCREAMING_SNAKE_CASE_		   =  PipelineTesterMixin.required_optional_params - {'''latents'''}






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           return self._get_dummy_components()






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=0  )  ->   Dict:




           """simple docstring"""







           if str(__SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
                 UpperCamelCase__    :   List[str]					=				torch.manual_seed(__SCREAMING_SNAKE_CASE  )
           else:
                 UpperCamelCase__    :   Optional[Any]					=				torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   str					=				floats_tensor((1, 3, 3_2, 3_2)						,						rng=random.Random(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   str					=				floats_tensor((1, 3, 3_2, 3_2)						,						rng=random.Random(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Union[str, Any]					=				{
               '''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    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           self._test_save_load_optional_components()






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




           """simple docstring"""







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






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




           """simple docstring"""







           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2  )






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




           """simple docstring"""







           self._test_save_load_local()






     def    __SCREAMING_SNAKE_CASE			(							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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1_0_0 ,		):
      UpperCamelCase__    :   Union[str, Any]					=				x_start
      UpperCamelCase__    :   List[Any]					=				fnc(UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				0.0
      for _ in range(UpperCamelCase__		):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCamelCase__    :   str					=				(x_end - x_start) / steps + xa
            UpperCamelCase__    :   Dict					=				fnc(UpperCamelCase__		)
            area += abs(fxa + fxa		) * (xa - xa) / 2
            # Increment step
            UpperCamelCase__    :   Tuple					=				xa
            UpperCamelCase__    :   Union[str, Any]					=				fxa
      return area


if __name__ == "__main__":


    def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
          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:")
    lowerCamelCase      =1_0
    while i <= 1_0_0_0_0_0:
        print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
        i *= 1_0


