






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 	_UpperCAmelCase  (  _A				,							_A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    IFInpaintingPipeline
      A          =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
      A          =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      A          =    PipelineTesterMixin.required_optional_params - {'''latents'''}
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self._get_dummy_components()
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=0		):



          '''simple docstring'''
          if str(_lowerCAmelCase		).startswith("mps"		):
              lowerCAmelCase__   :Optional[Any]      							=				torch.manual_seed(_lowerCAmelCase		)
          else:
              lowerCAmelCase__   :Dict      							=				torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				{
              "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 			snake_case_      (   self		):



          '''simple docstring'''
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != "cuda"    ,	reason="float16 requires CUDA"		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # 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 			snake_case_      (   self		):



          '''simple docstring'''
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self._test_save_load_local()



      def 			snake_case_      (   self		):



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











def 				snake_case__							(							UpperCAmelCase							:  float    ,			UpperCAmelCase							:  list[float]       ):
    if discount_rate < 0:
        raise ValueError("Discount rate cannot be negative"       )
    if not cash_flows:
        raise ValueError("Cash flows list cannot be empty"       )
    lowerCAmelCase__   :List[Any]      							=				sum(
        cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(UpperCAmelCase       )       )
    return round(UpperCAmelCase    ,			ndigits=2       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



