
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 __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    # TODO: is there an appropriate internal test set?
    _a		:						List[str]=				"ssube/stable-diffusion-x4-upscaler-onnx"
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=0      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      floats_tensor((1, 3, 128, 128)		,rng=random.Random(snake_case      )      )
           lowercase							:     str       	=      torch.manual_seed(snake_case      )
           lowercase							:     Dict       	=      {
               """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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Tuple       	=      self.get_dummy_inputs()
           lowercase							:     List[str]       	=      pipe(**snake_case      ).images
           lowercase							:     List[str]       	=      image[0, -3:, -3:, -1].flatten()

           # started as 128, should now be 512
           assert image.shape == (1, 512, 512, 3)
           lowercase							:     List[Any]       	=      np.array(
               [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]      )
           assert np.abs(image_slice - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     Optional[int]       	=      PNDMScheduler.from_config(pipe.scheduler.config		,skip_prk_steps=snake_case      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Union[str, Any]       	=      self.get_dummy_inputs()
           lowercase							:     Tuple       	=      pipe(**snake_case      ).images
           lowercase							:     List[Any]       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     List[str]       	=      np.array(
               [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]      )
           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     Dict       	=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     int       	=      self.get_dummy_inputs()
           lowercase							:     str       	=      pipe(**snake_case      ).images
           lowercase							:     int       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     Any       	=      np.array(
               [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     str       	=      EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     List[Any]       	=      self.get_dummy_inputs()
           lowercase							:     Dict       	=      pipe(**snake_case      ).images
           lowercase							:     Any       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     Optional[int]       	=      np.array(
               [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]      )
           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     List[Any]       	=      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Tuple       	=      self.get_dummy_inputs()
           lowercase							:     Optional[int]       	=      pipe(**snake_case      ).images
           lowercase							:     Dict       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     str       	=      np.array(
               [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]      )

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






@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case		(      unittest.TestCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     Dict       	=      ort.SessionOptions()
           lowercase							:     Tuple       	=      False
           return options
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

           lowercase							:     List[str]       	=      """A fantasy landscape, trending on artstation"""

           lowercase							:     str       	=      torch.manual_seed(0      )
           lowercase							:     Any       	=      pipe(
               prompt=snake_case		,image=snake_case		,guidance_scale=7.5		,num_inference_steps=10		,generator=snake_case		,output_type="""np"""		,)
           lowercase							:     Optional[Any]       	=      output.images
           lowercase							:     str       	=      images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           lowercase							:     Optional[int]       	=      np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]      )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2e-2
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           lowercase							:     Dict       	=      init_image.resize((128, 128)      )
           lowercase							:     Union[str, Any]       	=      LMSDiscreteScheduler.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,subfolder="""scheduler"""      )
           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,scheduler=snake_case		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     int       	=      """A fantasy landscape, trending on artstation"""

           lowercase							:     Tuple       	=      torch.manual_seed(0      )
           lowercase							:     Any       	=      pipe(
               prompt=snake_case		,image=snake_case		,guidance_scale=7.5		,num_inference_steps=20		,generator=snake_case		,output_type="""np"""		,)
           lowercase							:     str       	=      output.images
           lowercase							:     int       	=      images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           lowercase							:     Tuple       	=      np.array(
               [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]      )
           # 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(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       if index == r:
              for j in range(SCREAMING_SNAKE_CASE__				):
                     print(data[j]					,	end=""" """				)
              print(""" """				)
              return
       #  When no more elements are there to put in data[]
       if i >= n:
              return
       # current is included, put next at next location
       lowercase							:     Tuple       	=      arr[i]
       combination_util(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	index + 1					,	SCREAMING_SNAKE_CASE__					,	i + 1				)
       # current is excluded, replace it with
       # next (Note that i+1 is passed, but
       # index is not changed)
       combination_util(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	i + 1				)
       # The main function that prints all combinations
       # of size r in arr[] of size n. This function
       # mainly uses combinationUtil()







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       # A temporary array to store all combination one by one
       lowercase							:     Optional[int]       	=      [0] * r
       # Print all combination using temporary array 'data[]'
       combination_util(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	0					,	SCREAMING_SNAKE_CASE__					,	0				)


if __name__ == "__main__":
  # Driver code to check the function above
  lowercase  :					int						    =							[10, 20, 30, 40, 50]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu
