

"""simple docstring"""





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  _lowerCAmelCase  (     lowerCamelCase_					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Dict       		=     '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      List[Any]=0     ):
    __lowercase								=						floats_tensor((1, 3, 1_2_8, 1_2_8), rng=random.Random(__snake_case     )     )
    __lowercase								=						np.random.RandomState(__snake_case     )
    __lowercase								=						{
        "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  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider"     )
    pipe.set_progress_bar_config(disable=__snake_case     )

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

    assert image.shape == (1, 1_2_8, 1_2_8, 3)
    __lowercase								=						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  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider"     )
    __lowercase								=						PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=__snake_case     )
    pipe.set_progress_bar_config(disable=__snake_case     )

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

    assert image.shape == (1, 1_2_8, 1_2_8, 3)
    __lowercase								=						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  _lowercase			(      self							:      int     ):
    __lowercase								=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider"     )
    __lowercase								=						LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
    pipe.set_progress_bar_config(disable=__snake_case     )

    # warmup pass to apply optimizations
    __lowercase								=						pipe(**self.get_dummy_inputs()     )

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

    assert image.shape == (1, 1_2_8, 1_2_8, 3)
    __lowercase								=						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  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider"     )
    __lowercase								=						EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
    pipe.set_progress_bar_config(disable=__snake_case     )

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

    assert image.shape == (1, 1_2_8, 1_2_8, 3)
    __lowercase								=						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  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider"     )
    __lowercase								=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
    pipe.set_progress_bar_config(disable=__snake_case     )

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

    assert image.shape == (1, 1_2_8, 1_2_8, 3)
    __lowercase								=						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  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider"     )
    __lowercase								=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
    pipe.set_progress_bar_config(disable=__snake_case     )

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

    assert image.shape == (1, 1_2_8, 1_2_8, 3)
    __lowercase								=						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  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







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


 @property
 def  _lowercase			(      self							:      str     ):
    __lowercase								=						ort.SessionOptions()
    __lowercase								=						False
    return options


 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"     )
    __lowercase								=						init_image.resize((7_6_8, 5_1_2)     )
    # using the PNDM scheduler by default
    __lowercase								=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4", revision="onnx", safety_checker=__snake_case, feature_extractor=__snake_case, provider=self.gpu_provider, sess_options=self.gpu_options, )
    pipe.set_progress_bar_config(disable=__snake_case     )

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						np.random.RandomState(0     )
    __lowercase								=						pipe(
        prompt=__snake_case, image=__snake_case, strength=0.75, guidance_scale=7.5, num_inference_steps=1_0, generator=__snake_case, output_type="np", )
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 7_6_8, 3)
    __lowercase								=						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  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"     )
    __lowercase								=						init_image.resize((7_6_8, 5_1_2)     )
    __lowercase								=						LMSDiscreteScheduler.from_pretrained(
        "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"     )
    __lowercase								=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5", revision="onnx", scheduler=__snake_case, safety_checker=__snake_case, feature_extractor=__snake_case, provider=self.gpu_provider, sess_options=self.gpu_options, )
    pipe.set_progress_bar_config(disable=__snake_case     )

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						np.random.RandomState(0     )
    __lowercase								=						pipe(
        prompt=__snake_case, image=__snake_case, strength=0.75, guidance_scale=7.5, num_inference_steps=2_0, generator=__snake_case, output_type="np", )
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 7_6_8, 3)
    __lowercase								=						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







"""simple docstring"""





import re

from filelock import FileLock


try:
   import nltk

   _a							=							True
except (ImportError, ModuleNotFoundError):
   _a							=							False

if NLTK_AVAILABLE:
   with FileLock('.lock') as lock:
      nltk.download('punkt', quiet=True)




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   re.sub("<n>",      "",      UpperCamelCase_)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(UpperCamelCase_))




