

"""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'''







  SCREAMING_SNAKE_CASE					      =  'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'



  def  _a      (self							,       _lowerCamelCase=0	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    floats_tensor((1, 3, 128, 128)							,       rng=random.Random(_lowerCamelCase	)	)
        UpperCAmelCase__			:							Optional[Any]							=    np.random.RandomState(_lowerCamelCase	)
        UpperCAmelCase__			:							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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider="""CPUExecutionProvider"""	)
        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

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

        assert image.shape == (1, 128, 128, 3)
        UpperCAmelCase__			:							int							=    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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider="""CPUExecutionProvider"""	)
        UpperCAmelCase__			:							Dict							=    PNDMScheduler.from_config(pipe.scheduler.config							,       skip_prk_steps=_lowerCamelCase	)
        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							Tuple							=    self.get_dummy_inputs()
        UpperCAmelCase__			:							Tuple							=    pipe(**_lowerCamelCase	).images
        UpperCAmelCase__			:							Tuple							=    image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        UpperCAmelCase__			:							Dict							=    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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider="""CPUExecutionProvider"""	)
        UpperCAmelCase__			:							Union[str, Any]							=    LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

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

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

        assert image.shape == (1, 128, 128, 3)
        UpperCAmelCase__			:							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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider="""CPUExecutionProvider"""	)
        UpperCAmelCase__			:							List[str]							=    EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

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

        assert image.shape == (1, 128, 128, 3)
        UpperCAmelCase__			:							Tuple							=    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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider="""CPUExecutionProvider"""	)
        UpperCAmelCase__			:							int							=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

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

        assert image.shape == (1, 128, 128, 3)
        UpperCAmelCase__			:							Union[str, Any]							=    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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider="""CPUExecutionProvider"""	)
        UpperCAmelCase__			:							List[Any]							=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							Optional[Any]							=    self.get_dummy_inputs()
        UpperCAmelCase__			:							List[str]							=    pipe(**_lowerCamelCase	).images
        UpperCAmelCase__			:							Tuple							=    image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        UpperCAmelCase__			:							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       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @property
  def  _a      (self	):


        """simple docstring"""

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



  @property
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    ort.SessionOptions()
        UpperCAmelCase__			:							Union[str, Any]							=    False
        return options



  def  _a      (self	):


        """simple docstring"""

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

        UpperCAmelCase__			:							Dict							=    """A fantasy landscape, trending on artstation"""

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

        assert images.shape == (1, 512, 768, 3)
        UpperCAmelCase__			:							str							=    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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    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)	)
        UpperCAmelCase__			:							Dict							=    LMSDiscreteScheduler.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""							,       subfolder="""scheduler"""							,       revision="""onnx"""	)
        UpperCAmelCase__			:							Any							=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""							,       revision="""onnx"""							,       scheduler=_lowerCamelCase							,       safety_checker=_lowerCamelCase							,       feature_extractor=_lowerCamelCase							,       provider=self.gpu_provider							,       sess_options=self.gpu_options							,       )
        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

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

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

        assert images.shape == (1, 512, 768, 3)
        UpperCAmelCase__			:							Tuple							=    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 requests




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> None:
      UpperCAmelCase__			:							List[str]							=    {"""Content-Type""": """application/json"""}
      UpperCAmelCase__			:							List[str]							=    requests.post(lowerCAmelCase			,	json={"""text""": message_body}			,	headers=lowerCAmelCase    )
      if response.status_code != 2_00:
            UpperCAmelCase__			:							str							=    (
                """Request to slack returned an error """
                F"""{response.status_code}, the response is:\n{response.text}"""
            )
            raise ValueError(lowerCAmelCase    )


if __name__ == "__main__":
    # Set the slack url to the one provided by Slack when you create the webhook at
    # https://my.slack.com/services/new/incoming-webhook/
    send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")

