




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput






@dataclass
class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		42
      A__		     =		42






class  _lowerCAmelCase     (  _lowercase    ,			_lowercase	):
      A__		     =		1

      @register_to_config
      def __init__(   self      ,				__UpperCAmelCase = 2000      ,				__UpperCAmelCase = 0.15      ,				__UpperCAmelCase = 0.01      ,				__UpperCAmelCase = 1348.0      ,				__UpperCAmelCase = 1e-5      ,				__UpperCAmelCase = 1      ,				):
        # standard deviation of the initial noise distribution
        lowerCAmelCase__				:  int		       =     sigma_max

        # setable values
        lowerCAmelCase__				:  Optional[int]		       =     None

        self.set_sigmas(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None		):
        return sample

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None		):
        lowerCAmelCase__				:  List[str]		       =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

        lowerCAmelCase__				:  Any		       =     torch.linspace(1      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				device=__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None		):
        lowerCAmelCase__				:  Optional[int]		       =     sigma_min if sigma_min is not None else self.config.sigma_min
        lowerCAmelCase__				:  List[str]		       =     sigma_max if sigma_max is not None else self.config.sigma_max
        lowerCAmelCase__				:  int		       =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
          self.set_timesteps(__UpperCAmelCase      ,				__UpperCAmelCase		)

        lowerCAmelCase__				:  List[str]		       =     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        lowerCAmelCase__				:  Tuple		       =     torch.exp(torch.linspace(math.log(__UpperCAmelCase		)      ,				math.log(__UpperCAmelCase		)      ,				__UpperCAmelCase		)		)
        lowerCAmelCase__				:  str		       =     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        return torch.where(
            timesteps == 0      ,				torch.zeros_like(t.to(timesteps.device		)		)      ,				self.discrete_sigmas[timesteps - 1].to(timesteps.device		)      ,				)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = True      ,				):
        if self.timesteps is None:
          raise ValueError(
              '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''		)

        lowerCAmelCase__				:  List[str]		       =     timestep * torch.ones(
            sample.shape[0]      ,				device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
        lowerCAmelCase__				:  List[Any]		       =     (timestep * (len(self.timesteps		) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        lowerCAmelCase__				:  Any		       =     timesteps.to(self.discrete_sigmas.device		)

        lowerCAmelCase__				:  Tuple		       =     self.discrete_sigmas[timesteps].to(sample.device		)
        lowerCAmelCase__				:  Tuple		       =     self.get_adjacent_sigma(__UpperCAmelCase      ,				__UpperCAmelCase		).to(sample.device		)
        lowerCAmelCase__				:  int		       =     torch.zeros_like(__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        lowerCAmelCase__				:  int		       =     diffusion.flatten()
        while len(diffusion.shape		) < len(sample.shape		):
          lowerCAmelCase__				:  Dict		       =     diffusion.unsqueeze(-1		)
        lowerCAmelCase__				:  Any		       =     drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        lowerCAmelCase__				:  str		       =     randn_tensor(
            sample.shape      ,				layout=sample.layout      ,				generator=__UpperCAmelCase      ,				device=sample.device      ,				dtype=sample.dtype		)
        lowerCAmelCase__				:  Optional[Any]		       =     sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        lowerCAmelCase__				:  int		       =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
          return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=__UpperCAmelCase      ,				prev_sample_mean=__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = True      ,				):
        if self.timesteps is None:
          raise ValueError(
              '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''		)

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        lowerCAmelCase__				:  Union[str, Any]		       =     randn_tensor(sample.shape      ,				layout=sample.layout      ,				generator=__UpperCAmelCase		).to(sample.device		)

        # compute step size from the model_output, the noise, and the snr
        lowerCAmelCase__				:  Any		       =     torch.norm(model_output.reshape(model_output.shape[0]      ,				-1		)      ,				dim=-1		).mean()
        lowerCAmelCase__				:  str		       =     torch.norm(noise.reshape(noise.shape[0]      ,				-1		)      ,				dim=-1		).mean()
        lowerCAmelCase__				:  List[str]		       =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        lowerCAmelCase__				:  Any		       =     step_size * torch.ones(sample.shape[0]		).to(sample.device		)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        lowerCAmelCase__				:  str		       =     step_size.flatten()
        while len(step_size.shape		) < len(sample.shape		):
          lowerCAmelCase__				:  Any		       =     step_size.unsqueeze(-1		)
        lowerCAmelCase__				:  Optional[Any]		       =     sample + step_size * model_output
        lowerCAmelCase__				:  List[str]		       =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
          return (prev_sample,)

        return SchedulerOutput(prev_sample=__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowerCAmelCase__				:  Dict		       =     timesteps.to(original_samples.device		)
        lowerCAmelCase__				:  List[Any]		       =     self.discrete_sigmas.to(original_samples.device		)[timesteps]
        lowerCAmelCase__				:  Dict		       =     (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(__UpperCAmelCase		) * sigmas[:, None, None, None]
        )
        lowerCAmelCase__				:  Optional[int]		       =     noise + original_samples
        return noisy_samples







      def __len__(   self		):
        return self.config.num_train_timesteps







import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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     (  _lowercase    ,			unittest.TestCase	):
      # FIXME: add fast tests
      pass






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

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

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

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  Optional[int]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  List[Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  List[Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  int		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Any		       =     output.images
        lowerCAmelCase__				:  Any		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Any		       =     np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

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







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  str		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				subfolder='''scheduler'''      ,				revision='''onnx'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				scheduler=__UpperCAmelCase      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  str		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  Union[str, Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  Optional[Any]		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Tuple		       =     output.images
        lowerCAmelCase__				:  Optional[Any]		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Optional[int]		       =     np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

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

