





'''simple docstring'''




import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 UpperCAmelCase							(		lowercase__					,			unittest.TestCase							):
				_A :			str						   =       """ssube/stable-diffusion-x4-upscaler-onnx"""



				def 					__lowerCamelCase						(  self     ,			__A=0					):
					__UpperCAmelCase					=   floats_tensor((1, 3, 128, 128)     ,			rng=random.Random(__lowerCamelCase					)					)
					__UpperCAmelCase					=   torch.manual_seed(__lowerCamelCase					)
					__UpperCAmelCase					=   {
					    "prompt": "A painting of a squirrel eating a burger",
					    "image": image,
					    "generator": generator,
					    "num_inference_steps": 3,
					    "guidance_scale": 7.5,
					    "output_type": "numpy",
					}
					return inputs



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,			provider='CPUExecutionProvider'					)
					pipe.set_progress_bar_config(disable=__lowerCamelCase					)

					__UpperCAmelCase					=   self.get_dummy_inputs()
					__UpperCAmelCase					=   pipe(**__lowerCamelCase					).images
					__UpperCAmelCase					=   image[0, -3:, -3:, -1].flatten()

					# started as 128, should now be 512
					assert image.shape == (1, 512, 512, 3)
					__UpperCAmelCase					=   np.array(
					    [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]					)
					assert np.abs(image_slice - expected_slice					).max() < 1E-1



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,			provider='CPUExecutionProvider'					)
					__UpperCAmelCase					=   PNDMScheduler.from_config(pipe.scheduler.config     ,			skip_prk_steps=__lowerCamelCase					)
					pipe.set_progress_bar_config(disable=__lowerCamelCase					)

					__UpperCAmelCase					=   self.get_dummy_inputs()
					__UpperCAmelCase					=   pipe(**__lowerCamelCase					).images
					__UpperCAmelCase					=   image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					__UpperCAmelCase					=   np.array(
					    [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4]					)
					assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-1



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,			provider='CPUExecutionProvider'					)
					__UpperCAmelCase					=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
					pipe.set_progress_bar_config(disable=__lowerCamelCase					)

					__UpperCAmelCase					=   self.get_dummy_inputs()
					__UpperCAmelCase					=   pipe(**__lowerCamelCase					).images
					__UpperCAmelCase					=   image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					__UpperCAmelCase					=   np.array(
					    [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5]					)

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



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,			provider='CPUExecutionProvider'					)
					__UpperCAmelCase					=   EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
					pipe.set_progress_bar_config(disable=__lowerCamelCase					)

					__UpperCAmelCase					=   self.get_dummy_inputs()
					__UpperCAmelCase					=   pipe(**__lowerCamelCase					).images
					__UpperCAmelCase					=   image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					__UpperCAmelCase					=   np.array(
					    [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]					)
					assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-1




				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,			provider='CPUExecutionProvider'					)
					__UpperCAmelCase					=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
					pipe.set_progress_bar_config(disable=__lowerCamelCase					)

					__UpperCAmelCase					=   self.get_dummy_inputs()
					__UpperCAmelCase					=   pipe(**__lowerCamelCase					).images
					__UpperCAmelCase					=   image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					__UpperCAmelCase					=   np.array(
					    [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3]					)

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






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



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



				@property
				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   ort.SessionOptions()
					__UpperCAmelCase					=   False
					return options



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/img2img/sketch-mountains-input.jpg'					)
					__UpperCAmelCase					=   init_image.resize((128, 128)					)
					# using the PNDM scheduler by default
					__UpperCAmelCase					=   OnnxStableDiffusionUpscalePipeline.from_pretrained(
					    'ssube/stable-diffusion-x4-upscaler-onnx'     ,			provider=self.gpu_provider     ,			sess_options=self.gpu_options     ,			)
					pipe.set_progress_bar_config(disable=__lowerCamelCase					)

					__UpperCAmelCase					=   "A fantasy landscape, trending on artstation"

					__UpperCAmelCase					=   torch.manual_seed(0					)
					__UpperCAmelCase					=   pipe(
					    prompt=__lowerCamelCase     ,			image=__lowerCamelCase     ,			guidance_scale=7.5     ,			num_inference_steps=10     ,			generator=__lowerCamelCase     ,			output_type='np'     ,			)
					__UpperCAmelCase					=   output.images
					__UpperCAmelCase					=   images[0, 255:258, 383:386, -1]

					assert images.shape == (1, 512, 512, 3)
					__UpperCAmelCase					=   np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2]					)
					# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

					assert np.abs(image_slice.flatten() - expected_slice					).max() < 2E-2




				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/img2img/sketch-mountains-input.jpg'					)
					__UpperCAmelCase					=   init_image.resize((128, 128)					)
					__UpperCAmelCase					=   LMSDiscreteScheduler.from_pretrained(
					    'ssube/stable-diffusion-x4-upscaler-onnx'     ,			subfolder='scheduler'					)
					__UpperCAmelCase					=   OnnxStableDiffusionUpscalePipeline.from_pretrained(
					    'ssube/stable-diffusion-x4-upscaler-onnx'     ,			scheduler=__lowerCamelCase     ,			provider=self.gpu_provider     ,			sess_options=self.gpu_options     ,			)
					pipe.set_progress_bar_config(disable=__lowerCamelCase					)

					__UpperCAmelCase					=   "A fantasy landscape, trending on artstation"

					__UpperCAmelCase					=   torch.manual_seed(0					)
					__UpperCAmelCase					=   pipe(
					    prompt=__lowerCamelCase     ,			image=__lowerCamelCase     ,			guidance_scale=7.5     ,			num_inference_steps=20     ,			generator=__lowerCamelCase     ,			output_type='np'     ,			)
					__UpperCAmelCase					=   output.images
					__UpperCAmelCase					=   images[0, 255:258, 383:386, -1]

					assert images.shape == (1, 512, 512, 3)
					__UpperCAmelCase					=   np.array(
					    [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6]					)
					# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

					assert np.abs(image_slice.flatten() - expected_slice					).max() < 2E-2












'''simple docstring'''




from collections.abc import Sequence







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     float:
 return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     float:
 __UpperCAmelCase					=   0.0
 for coeff in reversed(_lowerCAmelCase    ):
  __UpperCAmelCase					=   result * x + coeff
 return result


if __name__ == "__main__":
    _A:					str							=				(0.0, 0.0, 5.0, 9.3, 7.0)
    _A:					Dict							=				10.0
    print(evaluate_poly(poly, x))
    print(horner(poly, x))





