

'''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 	lowerCAmelCase  (   A ,      unittest.TestCase ):
       # TODO: is there an appropriate internal test set?
       lowerCAmelCase_												=			"ssube/stable-diffusion-x4-upscaler-onnx"







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			int=0   ):







            """simple docstring"""







            __lowercase      		=floats_tensor((1, 3, 128, 128)    ,   rng=random.Random(__lowercase   )   )
            __lowercase      		=torch.manual_seed(__lowercase   )
            __lowercase      		={
                '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       snake_case (				self      :			List[str]   ):







            """simple docstring"""







            __lowercase      		=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'   )
            pipe.set_progress_bar_config(disable=__lowercase   )

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

            # started as 128, should now be 512
            assert image.shape == (1, 512, 512, 3)
            __lowercase      		=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       snake_case (				self      :			List[str]   ):







            """simple docstring"""







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

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

            assert image.shape == (1, 512, 512, 3)
            __lowercase      		=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       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'   )
            __lowercase      		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
            pipe.set_progress_bar_config(disable=__lowercase   )

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

            assert image.shape == (1, 512, 512, 3)
            __lowercase      		=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       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'   )
            __lowercase      		=EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
            pipe.set_progress_bar_config(disable=__lowercase   )

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

            assert image.shape == (1, 512, 512, 3)
            __lowercase      		=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       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'   )
            __lowercase      		=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
            pipe.set_progress_bar_config(disable=__lowercase   )

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

            assert image.shape == (1, 512, 512, 3)
            __lowercase      		=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 	lowerCAmelCase  (   unittest.TestCase ):







       @property
       def       snake_case (				self      :			List[str]   ):







            """simple docstring"""







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







       @property
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=ort.SessionOptions()
            __lowercase      		=False
            return options







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







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

            __lowercase      		='A fantasy landscape, trending on artstation'

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

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







            """simple docstring"""







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

            __lowercase      		='A fantasy landscape, trending on artstation'

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

            assert images.shape == (1, 512, 512, 3)
            __lowercase      		=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'''

# Function to print upper half of diamond (pyramid)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     for i in range(0,       lowercase__			):
          for _ in range(0,       n - i - 1			):  # printing spaces
               print(' ',       end=''			)
          for _ in range(0,       i + 1			):  # printing stars
               print('* ',       end=''			)
          print()

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     for i in range(lowercase__,       0,       -1			):
          for _ in range(lowercase__,       0,       -1			):  # printing stars
               print('* ',       end=''			)
          print()
          for _ in range(n - i + 1,       0,       -1			):  # printing spaces
               print(' ',       end=''			)

def 					__UpperCamelCase				(						lowercase__    :	Any			):






     '''simple docstring'''





     if n <= 0:
          print('       ...       ....        nothing printing :('			)
          return
     floyd(lowercase__			)  # upper half
     reverse_floyd(lowercase__			)  # lower half


if __name__ == "__main__":
       print(r'''| /\ | |- |  |-  |--| |\  /| |-''')
       print(r'''|/  \| |- |_ |_  |__| | \/ | |_''')
       UpperCAmelCase					 =	1
       while K:
              UpperCAmelCase					 =	int(input('''enter the number and , and see the magic : '''))
              print()
              pretty_print(user_number)
              UpperCAmelCase					 =	int(input('''press 0 to exit... and 1 to continue...'''))

       print('''Good Bye...''')


