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   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
       # FIXME: add fast tests
       pass


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







       @property
       def 				snake_case  (				self     :    Any		):
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )







       @property
       def 				snake_case  (				self     :    str		):
          lowerCamelCase						:int						  =     ort.SessionOptions()
          lowerCamelCase						:Dict						  =     False
          return options







       def 				snake_case  (				self     :    Dict		):
          lowerCamelCase						:int						  =     load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
          lowerCamelCase						:List[Any]						  =     load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
          lowerCamelCase						:Optional[int]						  =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
              '''runwayml/stable-diffusion-inpainting'''	,					revision='''onnx'''	,					safety_checker=__snake_case	,					feature_extractor=__snake_case	,					provider=self.gpu_provider	,					sess_options=self.gpu_options	,					)
          pipe.set_progress_bar_config(disable=__snake_case		)

          lowerCamelCase						:Optional[int]						  =     '''A red cat sitting on a park bench'''

          lowerCamelCase						:Optional[int]						  =     np.random.RandomState(0		)
          lowerCamelCase						:int						  =     pipe(
              prompt=__snake_case	,					image=__snake_case	,					mask_image=__snake_case	,					guidance_scale=7.5	,					num_inference_steps=10	,					generator=__snake_case	,					output_type='''np'''	,					)
          lowerCamelCase						:str						  =     output.images
          lowerCamelCase						:Tuple						  =     images[0, 255:258, 255:258, -1]

          assert images.shape == (1, 512, 512, 3)
          lowerCamelCase						:Tuple						  =     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 				snake_case  (				self     :    List[Any]		):
          lowerCamelCase						:Any						  =     load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
          lowerCamelCase						:Optional[Any]						  =     load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
          lowerCamelCase						:int						  =     LMSDiscreteScheduler.from_pretrained(
              '''runwayml/stable-diffusion-inpainting'''	,					subfolder='''scheduler'''	,					revision='''onnx'''		)
          lowerCamelCase						:Any						  =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
              '''runwayml/stable-diffusion-inpainting'''	,					revision='''onnx'''	,					scheduler=__snake_case	,					safety_checker=__snake_case	,					feature_extractor=__snake_case	,					provider=self.gpu_provider	,					sess_options=self.gpu_options	,					)
          pipe.set_progress_bar_config(disable=__snake_case		)

          lowerCamelCase						:Any						  =     '''A red cat sitting on a park bench'''

          lowerCamelCase						:Optional[int]						  =     np.random.RandomState(0		)
          lowerCamelCase						:str						  =     pipe(
              prompt=__snake_case	,					image=__snake_case	,					mask_image=__snake_case	,					guidance_scale=7.5	,					num_inference_steps=20	,					generator=__snake_case	,					output_type='''np'''	,					)
          lowerCamelCase						:str						  =     output.images
          lowerCamelCase						:Any						  =     images[0, 255:258, 255:258, -1]

          assert images.shape == (1, 512, 512, 3)
          lowerCamelCase						:Optional[Any]						  =     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




import math





def  _lowerCamelCase					(   a_				: int):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5						,			int(math.sqrt(a_) + 1)						,			6):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True





def  _lowerCamelCase					(   a_				: float = 0.1):
			lowerCamelCase						:Dict						  =     3
			lowerCamelCase						:List[Any]						  =     3

			while primes / (2 * j - 1) >= ratio:
						for i in range(j * j + j + 1						,			(j + 2) * (j + 2)						,			j + 1):
									primes += is_prime(a_)
						j += 2
			return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()



