import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     int	      = """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[Any]=0				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			floats_tensor((1, 3, 128, 128)		,	rng=random.Random(__UpperCAmelCase				)				)
    _UpperCamelCase:  List[str]    						=			np.random.RandomState(__UpperCAmelCase				)
    _UpperCamelCase:  List[str]    						=			{
        'prompt': 'A painting of a squirrel eating a burger',
        'image': image,
        'generator': generator,
        'num_inference_steps': 3,
        'strength': 0.75,
        'guidance_scale': 7.5,
        'output_type': 'numpy',
    }
    return inputs



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    _UpperCamelCase:  Union[str, Any]    						=			self.get_dummy_inputs()
    _UpperCamelCase:  Optional[int]    						=			pipe(**__UpperCAmelCase				).images
    _UpperCamelCase:  Union[str, Any]    						=			image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 128, 128, 3)
    _UpperCamelCase:  Union[str, Any]    						=			np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]				)
    assert np.abs(image_slice - expected_slice				).max() < 1E-1



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''				)
    _UpperCamelCase:  str    						=			PNDMScheduler.from_config(pipe.scheduler.config		,	skip_prk_steps=__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    _UpperCamelCase:  Tuple    						=			self.get_dummy_inputs()
    _UpperCamelCase:  Optional[Any]    						=			pipe(**__UpperCAmelCase				).images
    _UpperCamelCase:  Any    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    _UpperCamelCase:  int    						=			np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]				)

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



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''				)
    _UpperCamelCase:  List[str]    						=			LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    # warmup pass to apply optimizations
    _UpperCamelCase:  List[Any]    						=			pipe(**self.get_dummy_inputs()				)

    _UpperCamelCase:  Tuple    						=			self.get_dummy_inputs()
    _UpperCamelCase:  int    						=			pipe(**__UpperCAmelCase				).images
    _UpperCamelCase:  Any    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    _UpperCamelCase:  Union[str, Any]    						=			np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]				)

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



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''				)
    _UpperCamelCase:  Dict    						=			EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    _UpperCamelCase:  Any    						=			self.get_dummy_inputs()
    _UpperCamelCase:  List[str]    						=			pipe(**__UpperCAmelCase				).images
    _UpperCamelCase:  List[str]    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    _UpperCamelCase:  Optional[int]    						=			np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]				)

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



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''				)
    _UpperCamelCase:  str    						=			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    _UpperCamelCase:  Tuple    						=			self.get_dummy_inputs()
    _UpperCamelCase:  Any    						=			pipe(**__UpperCAmelCase				).images
    _UpperCamelCase:  Optional[int]    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    _UpperCamelCase:  int    						=			np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]				)

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



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''				)
    _UpperCamelCase:  List[Any]    						=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    _UpperCamelCase:  Any    						=			self.get_dummy_inputs()
    _UpperCamelCase:  List[Any]    						=			pipe(**__UpperCAmelCase				).images
    _UpperCamelCase:  int    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    _UpperCamelCase:  Optional[Any]    						=			np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]				)

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




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





   """simple docstring"""




   @property
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



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



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



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			ort.SessionOptions()
    _UpperCamelCase:  Optional[int]    						=			False
    return options



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/img2img/sketch-mountains-input.jpg'''				)
    _UpperCamelCase:  Any    						=			init_image.resize((768, 512)				)
    # using the PNDM scheduler by default
    _UpperCamelCase:  Optional[int]    						=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
        '''CompVis/stable-diffusion-v1-4'''		,	revision='''onnx'''		,	safety_checker=__UpperCAmelCase		,	feature_extractor=__UpperCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    _UpperCamelCase:  List[Any]    						=			'A fantasy landscape, trending on artstation'

    _UpperCamelCase:  Optional[Any]    						=			np.random.RandomState(0				)
    _UpperCamelCase:  List[str]    						=			pipe(
        prompt=__UpperCAmelCase		,	image=__UpperCAmelCase		,	strength=0.75		,	guidance_scale=7.5		,	num_inference_steps=10		,	generator=__UpperCAmelCase		,	output_type='''np'''		,	)
    _UpperCamelCase:  Any    						=			output.images
    _UpperCamelCase:  List[str]    						=			images[0, 255:258, 383:386, -1]

    assert images.shape == (1, 512, 768, 3)
    _UpperCamelCase:  List[Any]    						=			np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]				)
    # 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       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/img2img/sketch-mountains-input.jpg'''				)
    _UpperCamelCase:  Optional[Any]    						=			init_image.resize((768, 512)				)
    _UpperCamelCase:  List[Any]    						=			LMSDiscreteScheduler.from_pretrained(
        '''runwayml/stable-diffusion-v1-5'''		,	subfolder='''scheduler'''		,	revision='''onnx'''				)
    _UpperCamelCase:  Optional[Any]    						=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
        '''runwayml/stable-diffusion-v1-5'''		,	revision='''onnx'''		,	scheduler=__UpperCAmelCase		,	safety_checker=__UpperCAmelCase		,	feature_extractor=__UpperCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    _UpperCamelCase:  List[Any]    						=			'A fantasy landscape, trending on artstation'

    _UpperCamelCase:  List[Any]    						=			np.random.RandomState(0				)
    _UpperCamelCase:  List[Any]    						=			pipe(
        prompt=__UpperCAmelCase		,	image=__UpperCAmelCase		,	strength=0.75		,	guidance_scale=7.5		,	num_inference_steps=20		,	generator=__UpperCAmelCase		,	output_type='''np'''		,	)
    _UpperCamelCase:  Optional[Any]    						=			output.images
    _UpperCamelCase:  int    						=			images[0, 255:258, 383:386, -1]

    assert images.shape == (1, 512, 768, 3)
    _UpperCamelCase:  List[Any]    						=			np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]				)
    # 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"""





def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list[str]:





  """simple docstring"""


  return [sentence[i : i + ngram_size] for i in range(len(_SCREAMING_SNAKE_CASE     ) - ngram_size + 1     )]


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

  testmod()
