





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 			A__   (							_a			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Optional[int]	  =  """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

   def 							__lowercase		( self ,  lowercase=0)      ->						int:







          '''simple docstring'''

          a__						:     List[Any]			=    floats_tensor((1, 3, 128, 128) ,  rng=random.Random(lowercase))
          a__						:     Dict			=    np.random.RandomState(lowercase)
          a__						:     List[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 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,  provider='CPUExecutionProvider')
          pipe.set_progress_bar_config(disable=lowercase)

          a__						:     int			=    self.get_dummy_inputs()
          a__						:     Optional[Any]			=    pipe(**lowercase).images
          a__						:     List[Any]			=    image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 128, 128, 3)
          a__						:     Optional[int]			=    np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87])
          assert np.abs(image_slice - expected_slice).max() < 1e-1

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,  provider='CPUExecutionProvider')
          a__						:     Any			=    PNDMScheduler.from_config(pipe.scheduler.config ,  skip_prk_steps=lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

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

          assert image.shape == (1, 128, 128, 3)
          a__						:     Dict			=    np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54])

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

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     str			=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,  provider='CPUExecutionProvider')
          a__						:     Optional[Any]			=    LMSDiscreteScheduler.from_config(pipe.scheduler.config)
          pipe.set_progress_bar_config(disable=lowercase)

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

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

          assert image.shape == (1, 128, 128, 3)
          a__						:     Tuple			=    np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03])

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

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

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

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

          assert image.shape == (1, 128, 128, 3)
          a__						:     Dict			=    np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04])

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

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Optional[Any]			=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,  provider='CPUExecutionProvider')
          a__						:     str			=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
          pipe.set_progress_bar_config(disable=lowercase)

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

          assert image.shape == (1, 128, 128, 3)
          a__						:     List[Any]			=    np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04])

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

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Dict			=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,  provider='CPUExecutionProvider')
          a__						:     int			=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
          pipe.set_progress_bar_config(disable=lowercase)

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

          assert image.shape == (1, 128, 128, 3)
          a__						:     Optional[Any]			=    np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52])

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







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




   """simple docstring"""



   @property
   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

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

   @property
   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     List[Any]			=    ort.SessionOptions()
          a__						:     str			=    False
          return options

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

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

          a__						:     Tuple			=    'A fantasy landscape, trending on artstation'

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

          assert images.shape == (1, 512, 768, 3)
          a__						:     Optional[Any]			=    np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19])
          # 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)      ->						Optional[Any]:







          '''simple docstring'''

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

          a__						:     Dict			=    'A fantasy landscape, trending on artstation'

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

          assert images.shape == (1, 512, 768, 3)
          a__						:     Optional[Any]			=    np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31])
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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









import re

from filelock import FileLock


try:
    import nltk

    lowercase				:	List[str]      						=  True
except (ImportError, ModuleNotFoundError):
    lowercase				:	Tuple      						=  False

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



def A_     (		A__    )  ->		str:
       re.sub('<n>'		,				''		,				A__    )  # 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(A__    )    )


