

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 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Union[str, Any]														=					"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""





    def 				snake_case_    (				self						,       a=0):
         lowercase__   :       Union[str, Any]				=	floats_tensor((1, 3, 128, 128)						,       rng=random.Random(a))
         lowercase__   :       List[Any]				=	np.random.RandomState(a)
         lowercase__   :       Optional[Any]				=	{
             '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 				snake_case_    (				self):
         lowercase__   :       Tuple				=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,       provider='CPUExecutionProvider')
         pipe.set_progress_bar_config(disable=a)

         lowercase__   :       Any				=	self.get_dummy_inputs()
         lowercase__   :       Dict				=	pipe(**a).images
         lowercase__   :       Optional[Any]				=	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 128, 128, 3)
         lowercase__   :       List[str]				=	np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087])
         assert np.abs(image_slice - expected_slice).max() < 1e-1





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,       provider='CPUExecutionProvider')
         lowercase__   :       Union[str, Any]				=	PNDMScheduler.from_config(pipe.scheduler.config						,       skip_prk_steps=a)
         pipe.set_progress_bar_config(disable=a)

         lowercase__   :       Optional[int]				=	self.get_dummy_inputs()
         lowercase__   :       Dict				=	pipe(**a).images
         lowercase__   :       List[Any]				=	image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         lowercase__   :       List[Any]				=	np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154])

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





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,       provider='CPUExecutionProvider')
         lowercase__   :       Optional[Any]				=	LMSDiscreteScheduler.from_config(pipe.scheduler.config)
         pipe.set_progress_bar_config(disable=a)

         # warmup pass to apply optimizations
         lowercase__   :       str				=	pipe(**self.get_dummy_inputs())

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

         assert image.shape == (1, 128, 128, 3)
         lowercase__   :       Any				=	np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203])

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





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,       provider='CPUExecutionProvider')
         lowercase__   :       Tuple				=	EulerDiscreteScheduler.from_config(pipe.scheduler.config)
         pipe.set_progress_bar_config(disable=a)

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

         assert image.shape == (1, 128, 128, 3)
         lowercase__   :       str				=	np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304])

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





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,       provider='CPUExecutionProvider')
         lowercase__   :       int				=	EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
         pipe.set_progress_bar_config(disable=a)

         lowercase__   :       Dict				=	self.get_dummy_inputs()
         lowercase__   :       Union[str, Any]				=	pipe(**a).images
         lowercase__   :       Optional[Any]				=	image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         lowercase__   :       str				=	np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304])

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





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,       provider='CPUExecutionProvider')
         lowercase__   :       str				=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
         pipe.set_progress_bar_config(disable=a)

         lowercase__   :       Dict				=	self.get_dummy_inputs()
         lowercase__   :       Any				=	pipe(**a).images
         lowercase__   :       Tuple				=	image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         lowercase__   :       List[str]				=	np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552])

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



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





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





    @property
    def 				snake_case_    (				self):
         lowercase__   :       str				=	ort.SessionOptions()
         lowercase__   :       Optional[int]				=	False
         return options





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

         lowercase__   :       Any				=	'A fantasy landscape, trending on artstation'

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

         assert images.shape == (1, 512, 768, 3)
         lowercase__   :       int				=	np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019])
         # 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):
         lowercase__   :       Dict				=	load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg')
         lowercase__   :       Union[str, Any]				=	init_image.resize((768, 512))
         lowercase__   :       Optional[Any]				=	LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-v1-5'						,       subfolder='scheduler'						,       revision='onnx')
         lowercase__   :       Union[str, Any]				=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'						,       revision='onnx'						,       scheduler=a						,       safety_checker=a						,       feature_extractor=a						,       provider=self.gpu_provider						,       sess_options=self.gpu_options						,       )
         pipe.set_progress_bar_config(disable=a)

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

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

         assert images.shape == (1, 512, 768, 3)
         lowercase__   :       List[Any]				=	np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431])
         # 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_		:   float      ):



     '''simple docstring'''





     if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          raise ValueError('Length must be a positive.'      )
     return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   float      ):



     '''simple docstring'''





     if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          raise ValueError('Length must be a positive.'      )
     return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

