

"""simple docstring"""




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  # FIXME: add fast tests
  pass







@nightly
@require_onnxruntime
@require_torch_gpu
class   __lowercase      (     unittest.TestCase       ):

  @property
  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )

  @property
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       ort.SessionOptions()
   UpperCAmelCase__       :	int        =       False
   return options

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""     )
   UpperCAmelCase__       :	Union[str, Any]        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""     )
   UpperCAmelCase__       :	int        =       OnnxStableDiffusionInpaintPipeline.from_pretrained(
       """runwayml/stable-diffusion-inpainting"""       ,revision="""onnx"""       ,safety_checker=A       ,feature_extractor=A       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
   pipe.set_progress_bar_config(disable=A     )

   UpperCAmelCase__       :	Optional[int]        =       """A red cat sitting on a park bench"""

   UpperCAmelCase__       :	str        =       np.random.RandomState(0     )
   UpperCAmelCase__       :	Dict        =       pipe(
       prompt=A       ,image=A       ,mask_image=A       ,guidance_scale=7.5       ,num_inference_steps=10       ,generator=A       ,output_type="""np"""       ,)
   UpperCAmelCase__       :	Any        =       output.images
   UpperCAmelCase__       :	List[str]        =       images[0, 255:258, 255:258, -1]

   assert images.shape == (1, 512, 512, 3)
   UpperCAmelCase__       :	str        =       np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]     )

   assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3




  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""     )
   UpperCAmelCase__       :	Optional[Any]        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""     )
   UpperCAmelCase__       :	List[str]        =       LMSDiscreteScheduler.from_pretrained(
       """runwayml/stable-diffusion-inpainting"""       ,subfolder="""scheduler"""       ,revision="""onnx"""     )
   UpperCAmelCase__       :	Optional[Any]        =       OnnxStableDiffusionInpaintPipeline.from_pretrained(
       """runwayml/stable-diffusion-inpainting"""       ,revision="""onnx"""       ,scheduler=A       ,safety_checker=A       ,feature_extractor=A       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
   pipe.set_progress_bar_config(disable=A     )

   UpperCAmelCase__       :	str        =       """A red cat sitting on a park bench"""

   UpperCAmelCase__       :	Tuple        =       np.random.RandomState(0     )
   UpperCAmelCase__       :	Dict        =       pipe(
       prompt=A       ,image=A       ,mask_image=A       ,guidance_scale=7.5       ,num_inference_steps=20       ,generator=A       ,output_type="""np"""       ,)
   UpperCAmelCase__       :	Dict        =       output.images
   UpperCAmelCase__       :	Optional[Any]        =       images[0, 255:258, 255:258, -1]

   assert images.shape == (1, 512, 512, 3)
   UpperCAmelCase__       :	Tuple        =       np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]     )

   assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3












'''simple docstring'''




from __future__ import annotations





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float		)		->  float:
   if days_between_payments <= 0:
      raise ValueError("""days_between_payments must be > 0"""		)
   if daily_interest_rate < 0:
      raise ValueError("""daily_interest_rate must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)
   return principal * daily_interest_rate * days_between_payments





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  float:
   if number_of_compounding_periods <= 0:
      raise ValueError("""number_of_compounding_periods must be > 0"""		)
   if nominal_annual_interest_rate_percentage < 0:
      raise ValueError("""nominal_annual_interest_rate_percentage must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)

   return principal * (
       (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
       - 1
   )





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  float:
   if number_of_years <= 0:
      raise ValueError("""number_of_years must be > 0"""		)
   if nominal_annual_percentage_rate < 0:
      raise ValueError("""nominal_annual_percentage_rate must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)

   return compound_interest(
       __lowerCAmelCase      ,    nominal_annual_percentage_rate / 3_65      ,    number_of_years * 3_65		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

