
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        SCREAMING_SNAKE_CASE__					(     a__							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Optional[int]          = """ssube/stable-diffusion-x4-upscaler-onnx"""



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   List[Any]=0     ):


				'''simple docstring'''


				_snake_case          =      floats_tensor((1, 3, 128, 128)      ,  rng=random.Random(lowerCAmelCase__     )     )
				_snake_case          =      torch.manual_seed(lowerCAmelCase__     )
				_snake_case          =      {
				    "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 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


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

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

				# started as 128, should now be 512
				assert image.shape == (1, 512, 512, 3)
				_snake_case          =      np.array(
				    [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]     )
				assert np.abs(image_slice - expected_slice     ).max() < 1E-1



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


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

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

				assert image.shape == (1, 512, 512, 3)
				_snake_case          =      np.array(
				    [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]     )
				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


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

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

				assert image.shape == (1, 512, 512, 3)
				_snake_case          =      np.array(
				    [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]     )

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



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


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

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

				assert image.shape == (1, 512, 512, 3)
				_snake_case          =      np.array(
				    [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]     )
				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1





	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


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

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

				assert image.shape == (1, 512, 512, 3)
				_snake_case          =      np.array(
				    [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]     )

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




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





	'''simple docstring'''



	@property
	def 			A							(  self     :   str     ):


				'''simple docstring'''


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



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


				'''simple docstring'''


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



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/img2img/sketch-mountains-input.jpg'     )
				_snake_case          =      init_image.resize((128, 128)     )
				# using the PNDM scheduler by default
				_snake_case          =      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__     )

				_snake_case          =      "A fantasy landscape, trending on artstation"

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

				assert images.shape == (1, 512, 512, 3)
				_snake_case          =      np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]     )
				# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/img2img/sketch-mountains-input.jpg'     )
				_snake_case          =      init_image.resize((128, 128)     )
				_snake_case          =      LMSDiscreteScheduler.from_pretrained(
				    'ssube/stable-diffusion-x4-upscaler-onnx'      ,  subfolder='scheduler'     )
				_snake_case          =      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__     )

				_snake_case          =      "A fantasy landscape, trending on artstation"

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

				assert images.shape == (1, 512, 512, 3)
				_snake_case          =      np.array(
				    [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]     )
				# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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

from __future__ import annotations

_lowerCamelCase	:    Optional[Any]          =      [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase	:    Optional[int]          =      [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			_snake_case          =      len(__lowercase			)

			for i in range(__lowercase			):
						_snake_case          =      -1
						for j in range(i + 1	, __lowercase			):
									if arr[i] < arr[j]:
												_snake_case          =      arr[j]
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			for i, outer in enumerate(__lowercase			):
						_snake_case          =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												_snake_case          =      inner
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      len(__lowercase			)
			_snake_case          =      []
			_snake_case          =      [-1] * arr_size

			for index in reversed(range(__lowercase			)			):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									_snake_case          =      stack[-1]
						stack.append(arr[index]			)
			return result


if __name__ == "__main__":
			from doctest import testmod
			from timeit import timeit

			testmod()
			print(next_greatest_element_slow(arr))
			print(next_greatest_element_fast(arr))
			print(next_greatest_element(arr))

			_lowerCamelCase	:    Union[str, Any]          =      (
			    '''from __main__ import arr, next_greatest_element_slow, '''
			    '''next_greatest_element_fast, next_greatest_element'''
			)
			print(
			    '''next_greatest_element_slow():''',
			    timeit('''next_greatest_element_slow(arr)''', setup=setup),
			)
			print(
			    '''next_greatest_element_fast():''',
			    timeit('''next_greatest_element_fast(arr)''', setup=setup),
			)
			print(
			    '''     next_greatest_element():''',
			    timeit('''next_greatest_element(arr)''', setup=setup),
			)