


from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[str]							    =     ["""vqvae"""]

       def __init__(   self      :     str      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Mel      ,							lowerCamelCase_      :     Union[DDIMScheduler, DDPMScheduler]      ,							):
            super().__init__()
            self.register_modules(unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							mel=lowerCamelCase_      ,							vqvae=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            return 5_0 if isinstance(self.scheduler      ,							lowerCamelCase_    ) else 1_0_0_0

       @torch.no_grad()
       def __call__(   self      :     List[str]      ,							lowerCamelCase_      :     int = 1      ,							lowerCamelCase_      :     str = None      ,							lowerCamelCase_      :     np.ndarray = None      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = None      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     List[Any]=True      ,							):
            a_    :     Optional[Any]										=    steps or self.get_default_steps()
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Optional[int]										=    step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size    ) == int:
                 a_    :     str										=    (self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                 a_    :     Dict										=    randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )      ,							generator=lowerCamelCase_      ,							device=self.device      ,							)
            a_    :     Tuple										=    noise
            a_    :     Union[str, Any]										=    None

            if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    self.mel.audio_slice_to_image(lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    np.frombuffer(input_image.tobytes()      ,							dtype="""uint8"""    ).reshape(
                     (input_image.height, input_image.width)    )
                 a_    :     Union[str, Any]										=    (input_image / 2_5_5) * 2 - 1
                 a_    :     Tuple										=    torch.tensor(input_image[np.newaxis, :, :]      ,							dtype=torch.float    ).to(self.device    )

                 if self.vqvae is not None:
                      a_    :     Dict										=    self.vqvae.encode(torch.unsqueeze(lowerCamelCase_      ,							0    )    ).latent_dist.sample(
                          generator=lowerCamelCase_    )[0]
                      a_    :     List[Any]										=    self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                      a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							self.scheduler.timesteps[start_step - 1]    )

                 a_    :     Union[str, Any]										=    (
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 a_    :     int										=    int(mask_start_secs * pixels_per_second    )
                 a_    :     Tuple										=    int(mask_end_secs * pixels_per_second    )
                 a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							torch.tensor(self.scheduler.timesteps[start_step:]    )    )

            for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]    )    ):
                 if isinstance(self.unet      ,							lowerCamelCase_    ):
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 else:
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]

                 if isinstance(self.scheduler      ,							lowerCamelCase_    ):
                      a_    :     Any										=    self.scheduler.step(
                          model_output=lowerCamelCase_      ,							timestep=lowerCamelCase_      ,							sample=lowerCamelCase_      ,							eta=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							)["""prev_sample"""]
                 else:
                      a_    :     Optional[Any]										=    self.scheduler.step(
                          model_output=lowerCamelCase_      ,							timestep=lowerCamelCase_      ,							sample=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							)["""prev_sample"""]

                 if mask is not None:
                      if mask_start > 0:
                           a_    :     Any										=    mask[:, step, :, :mask_start]
                      if mask_end > 0:
                           a_    :     Dict										=    mask[:, step, :, -mask_end:]

            if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 a_    :     str										=    1 / self.vqvae.config.scaling_factor * images
                 a_    :     List[Any]										=    self.vqvae.decode(lowerCamelCase_    )["""sample"""]

            a_    :     Union[str, Any]										=    (images / 2 + 0.5).clamp(0      ,							1    )
            a_    :     Dict										=    images.cpu().permute(0      ,							2      ,							3      ,							1    ).numpy()
            a_    :     Any										=    (images * 2_5_5).round().astype("""uint8"""    )
            a_    :     int										=    list(
                (Image.fromarray(_[:, :, 0]    ) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(lowerCamelCase_      ,							mode="""RGB"""    ).convert("""L"""    ) for _ in images)    )

            a_    :     str										=    [self.mel.image_to_audio(lowerCamelCase_    ) for _ in images]
            if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

            return BaseOutput(**AudioPipelineOutput(np.array(lowerCamelCase_    )[:, np.newaxis, :]    )      ,							**ImagePipelineOutput(lowerCamelCase_    )    )

       @torch.no_grad()
       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[Image.Image]      ,							lowerCamelCase_      :     int = 5_0    ):
            assert isinstance(self.scheduler      ,							lowerCamelCase_    )
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Any										=    np.array(
                [np.frombuffer(image.tobytes()      ,							dtype="""uint8"""    ).reshape((1, image.height, image.width)    ) for image in images]    )
            a_    :     Union[str, Any]										=    (sample / 2_5_5) * 2 - 1
            a_    :     Optional[Any]										=    torch.Tensor(lowerCamelCase_    ).to(self.device    )

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps      ,							(0,)    )    ):
                 a_    :     str										=    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 a_    :     Any										=    self.scheduler.alphas_cumprod[t]
                 a_    :     Dict										=    (
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 a_    :     Optional[Any]										=    1 - alpha_prod_t
                 a_    :     Optional[int]										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 a_    :     Tuple										=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
                 a_    :     Tuple										=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 a_    :     Tuple										=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     float    ):
            a_    :     Dict										=    acos(torch.dot(torch.flatten(lowerCamelCase_    )      ,							torch.flatten(lowerCamelCase_    )    ) / torch.norm(lowerCamelCase_    ) / torch.norm(lowerCamelCase_    )    )
            return sin((1 - alpha) * theta    ) * xa / sin(lowerCamelCase_    ) + sin(alpha * theta    ) * xa / sin(lowerCamelCase_    )







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



