





# 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 			__UpperCamelCase    (							A__    ):
     __A	:  torch.FloatTensor
     __A	:  torch.FloatTensor




class 			__UpperCamelCase    (							A__	, A__    ):
     __A	:  Dict							  =   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
        _UpperCAmelCase           =   sigma_max

        # setable values
        _UpperCAmelCase           =   None

        self.set_sigmas(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        return sample





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        _UpperCAmelCase           =   torch.linspace(1				,							_UpperCamelCase				,							_UpperCamelCase				,							device=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   sigma_min if sigma_min is not None else self.config.sigma_min
        _UpperCAmelCase           =   sigma_max if sigma_max is not None else self.config.sigma_max
        _UpperCAmelCase           =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
           self.set_timesteps(_UpperCamelCase				,							_UpperCamelCase					)

        _UpperCAmelCase           =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        _UpperCAmelCase           =   torch.exp(torch.linspace(math.log(_UpperCamelCase					)				,							math.log(_UpperCamelCase					)				,							_UpperCamelCase					)					)
        _UpperCAmelCase           =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]					)





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





     def 		UpperCamelCase(     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'''					)

        _UpperCAmelCase           =   timestep * torch.ones(
            sample.shape[0]				,							device=sample.device					)  # torch.repeat_interleave(timestep, sample.shape[0])
        _UpperCAmelCase           =   (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
        _UpperCAmelCase           =   timesteps.to(self.discrete_sigmas.device					)

        _UpperCAmelCase           =   self.discrete_sigmas[timesteps].to(sample.device					)
        _UpperCAmelCase           =   self.get_adjacent_sigma(_UpperCamelCase				,							_UpperCamelCase					).to(sample.device					)
        _UpperCAmelCase           =   torch.zeros_like(_UpperCamelCase					)
        _UpperCAmelCase           =   (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
        _UpperCAmelCase           =   diffusion.flatten()
        while len(diffusion.shape					) < len(sample.shape					):
           _UpperCAmelCase           =   diffusion.unsqueeze(-1					)
        _UpperCAmelCase           =   drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        _UpperCAmelCase           =   randn_tensor(
            sample.shape				,							layout=sample.layout				,							generator=_UpperCamelCase				,							device=sample.device				,							dtype=sample.dtype					)
        _UpperCAmelCase           =   sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        _UpperCAmelCase           =   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 		UpperCamelCase(     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
        _UpperCAmelCase           =   randn_tensor(sample.shape				,							layout=sample.layout				,							generator=_UpperCamelCase					).to(sample.device					)

        # compute step size from the model_output, the noise, and the snr
        _UpperCAmelCase           =   torch.norm(model_output.reshape(model_output.shape[0]				,							-1					)				,							dim=-1					).mean()
        _UpperCAmelCase           =   torch.norm(noise.reshape(noise.shape[0]				,							-1					)				,							dim=-1					).mean()
        _UpperCAmelCase           =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        _UpperCAmelCase           =   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
        _UpperCAmelCase           =   step_size.flatten()
        while len(step_size.shape					) < len(sample.shape					):
           _UpperCAmelCase           =   step_size.unsqueeze(-1					)
        _UpperCAmelCase           =   sample + step_size * model_output
        _UpperCAmelCase           =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample,)

        return SchedulerOutput(prev_sample=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        _UpperCAmelCase           =   timesteps.to(original_samples.device					)
        _UpperCAmelCase           =   self.discrete_sigmas.to(original_samples.device					)[timesteps]
        _UpperCAmelCase           =   (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(_UpperCamelCase					) * sigmas[:, None, None, None]
        )
        _UpperCAmelCase           =   noise + original_samples
        return noisy_samples





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






import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        warnings.warn(
            '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
            ''' use GLPNImageProcessor instead.'''				,							_UpperCamelCase				,							)
        super().__init__(*_UpperCamelCase				,							**_UpperCamelCase					)