


def  _a			(						__UpperCamelCase ):
     return "".join([hex(__UpperCamelCase )[2:].zfill(2 ).upper() for byte in list(__UpperCamelCase )] )



def  _a			(						__UpperCamelCase ):
     # Check data validity, following RFC3548
     # https://www.ietf.org/rfc/rfc3548.txt
     if (len(__UpperCamelCase ) % 2) != 0:
          raise ValueError(
              """Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
     # Check the character set - the standard base16 alphabet
     # is uppercase according to RFC3548 section 6
     if not set(__UpperCamelCase ) <= set("""0123456789ABCDEF""" ):
          raise ValueError(
              """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
     # For every two hexadecimal digits (= a byte), turn it into an integer.
     # Then, string the result together into bytes, and return it.
     return bytes(int(data[i] + data[i + 1]  ,  1_6 ) for i in range(0  ,  len(__UpperCamelCase )  ,  2 ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()







import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
       import onnxruntime as ort


class   a__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     int							    =     """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Union[str, Any]=0    ):
            a_    :     Any										=    np.random.RandomState(lowerCamelCase_    )
            a_    :     Optional[Any]										=    {
                """prompt""": """A painting of a squirrel eating a burger""",
                """generator""": generator,
                """num_inference_steps""": 2,
                """guidance_scale""": 7.5,
                """output_type""": """numpy""",
            }
            return inputs

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[Any]										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     str										=    np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     str										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Any										=    PNDMScheduler.from_config(pipe.scheduler.config      ,							skip_prk_steps=lowerCamelCase_    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Tuple										=    np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Optional[Any]										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Optional[int]										=    LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Optional[int]										=    np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     int										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Optional[Any]										=    EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Optional[int]										=    np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Any										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     str										=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Tuple										=    np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Tuple										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Tuple										=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    pipe(**lowerCamelCase_    ).images
            a_    :     str										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     List[Any]										=    np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     int										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     List[str]										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    3 * [inputs["""prompt"""]]

            # forward
            a_    :     Optional[int]										=    pipe(**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    output.images[0, -3:, -3:, -1]

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     List[Any]										=    3 * [inputs.pop("""prompt"""    )]

            a_    :     List[Any]										=    pipe.tokenizer(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=pipe.tokenizer.model_max_length      ,							truncation=lowerCamelCase_      ,							return_tensors="""np"""      ,							)
            a_    :     str										=    text_inputs["""input_ids"""]

            a_    :     Dict										=    pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

            a_    :     List[Any]										=    prompt_embeds

            # forward
            a_    :     Tuple										=    pipe(**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    output.images[0, -3:, -3:, -1]

            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Any										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     int										=    self.get_dummy_inputs()
            a_    :     str										=    3 * ["""this is a negative prompt"""]
            a_    :     int										=    negative_prompt
            a_    :     Dict										=    3 * [inputs["""prompt"""]]

            # forward
            a_    :     Tuple										=    pipe(**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    output.images[0, -3:, -3:, -1]

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    3 * [inputs.pop("""prompt"""    )]

            a_    :     str										=    []
            for p in [prompt, negative_prompt]:
                 a_    :     Optional[int]										=    pipe.tokenizer(
                     lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=pipe.tokenizer.model_max_length      ,							truncation=lowerCamelCase_      ,							return_tensors="""np"""      ,							)
                 a_    :     int										=    text_inputs["""input_ids"""]

                 embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]    )

            a_			,			a_    :     Union[str, Any]										=    embeds

            # forward
            a_    :     Optional[Any]										=    pipe(**lowerCamelCase_    )
            a_    :     int										=    output.images[0, -3:, -3:, -1]

            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4


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

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

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[str]										=    ort.SessionOptions()
            a_    :     List[str]										=    False
            return options

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            # using the PNDM scheduler by default
            a_    :     int										=    OnnxStableDiffusionPipeline.from_pretrained(
                """CompVis/stable-diffusion-v1-4"""      ,							revision="""onnx"""      ,							safety_checker=lowerCamelCase_      ,							feature_extractor=lowerCamelCase_      ,							provider=self.gpu_provider      ,							sess_options=self.gpu_options      ,							)
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Tuple										=    """A painting of a squirrel eating a burger"""
            np.random.seed(0    )
            a_    :     int										=    sd_pipe([prompt]      ,							guidance_scale=6.0      ,							num_inference_steps=1_0      ,							output_type="""np"""    )
            a_    :     Optional[int]										=    output.images

            a_    :     Tuple										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     Optional[int]										=    np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     str										=    DDIMScheduler.from_pretrained(
                """runwayml/stable-diffusion-v1-5"""      ,							subfolder="""scheduler"""      ,							revision="""onnx"""    )
            a_    :     Any										=    OnnxStableDiffusionPipeline.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      ,							)
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Union[str, Any]										=    """open neural network exchange"""
            a_    :     str										=    np.random.RandomState(0    )
            a_    :     str										=    sd_pipe([prompt]      ,							guidance_scale=7.5      ,							num_inference_steps=1_0      ,							generator=lowerCamelCase_      ,							output_type="""np"""    )
            a_    :     str										=    output.images
            a_    :     Optional[Any]										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     List[str]										=    np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Optional[int]										=    LMSDiscreteScheduler.from_pretrained(
                """runwayml/stable-diffusion-v1-5"""      ,							subfolder="""scheduler"""      ,							revision="""onnx"""    )
            a_    :     List[str]										=    OnnxStableDiffusionPipeline.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      ,							)
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Optional[int]										=    """open neural network exchange"""
            a_    :     Tuple										=    np.random.RandomState(0    )
            a_    :     Tuple										=    sd_pipe([prompt]      ,							guidance_scale=7.5      ,							num_inference_steps=1_0      ,							generator=lowerCamelCase_      ,							output_type="""np"""    )
            a_    :     Tuple										=    output.images
            a_    :     str										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     str										=    np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     List[str]										=    0

            def test_callback_fn(lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     np.ndarray    ) -> None:
                 a_    :     List[Any]										=    True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 0:
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      a_    :     Union[str, Any]										=    latents[0, -3:, -3:, -1]
                      a_    :     Optional[Any]										=    np.array(
                          [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]    )

                      assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3
                 elif step == 5:
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      a_    :     List[str]										=    latents[0, -3:, -3:, -1]
                      a_    :     int										=    np.array(
                          [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]    )

                      assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3

            a_    :     Optional[int]										=    False

            a_    :     List[Any]										=    OnnxStableDiffusionPipeline.from_pretrained(
                """runwayml/stable-diffusion-v1-5"""      ,							revision="""onnx"""      ,							safety_checker=lowerCamelCase_      ,							feature_extractor=lowerCamelCase_      ,							provider=self.gpu_provider      ,							sess_options=self.gpu_options      ,							)
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     List[str]										=    """Andromeda galaxy in a bottle"""

            a_    :     Union[str, Any]										=    np.random.RandomState(0    )
            pipe(
                prompt=lowerCamelCase_      ,							num_inference_steps=5      ,							guidance_scale=7.5      ,							generator=lowerCamelCase_      ,							callback=lowerCamelCase_      ,							callback_steps=1      ,							)
            assert test_callback_fn.has_been_called
            assert number_of_steps == 6

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Tuple										=    OnnxStableDiffusionPipeline.from_pretrained(
                """runwayml/stable-diffusion-v1-5"""      ,							revision="""onnx"""      ,							safety_checker=lowerCamelCase_      ,							feature_extractor=lowerCamelCase_      ,							provider=self.gpu_provider      ,							sess_options=self.gpu_options      ,							)
            assert isinstance(lowerCamelCase_      ,							lowerCamelCase_    )
            assert pipe.safety_checker is None

            a_    :     Union[str, Any]										=    pipe("""example prompt"""      ,							num_inference_steps=2    ).images[0]
            assert image is not None

            # check that there's no error when saving a pipeline with one of the models being None
            with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(lowerCamelCase_    )
                 a_    :     Tuple										=    OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase_    )

            # sanity check that the pipeline still works
            assert pipe.safety_checker is None
            a_    :     Any										=    pipe("""example prompt"""      ,							num_inference_steps=2    ).images[0]
            assert image is not None



