


'''simple docstring'''




# 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 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  torch.FloatTensor
   _SCREAMING_SNAKE_CASE  :  torch.FloatTensor




class 			_lowercase (	__lowercase						,					__lowercase							):
   _SCREAMING_SNAKE_CASE  :  int          =	1
   @register_to_config
   def __init__(						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      int = 2000       ,   SCREAMING_SNAKE_CASE_       :      float = 0.1_5       ,   SCREAMING_SNAKE_CASE_       :      float = 0.0_1       ,   SCREAMING_SNAKE_CASE_       :      float = 1_3_4_8.0       ,   SCREAMING_SNAKE_CASE_       :      float = 1e-5       ,   SCREAMING_SNAKE_CASE_       :      int = 1       ,   ) ->							str:
        # standard deviation of the initial noise distribution
        __snake_case           =						sigma_max

        # setable values
        __snake_case           =						None

        self.set_sigmas(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      torch.FloatTensor       ,   SCREAMING_SNAKE_CASE_       :      Optional[int] = None      ) ->							torch.FloatTensor:
        return sample
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      float = None       ,   SCREAMING_SNAKE_CASE_       :      Union[str, torch.device] = None      ) ->							Any:
        __snake_case           =						sampling_eps if sampling_eps is not None else self.config.sampling_eps

        __snake_case           =						torch.linspace(1       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   device=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      float = None       ,   SCREAMING_SNAKE_CASE_       :      float = None       ,   SCREAMING_SNAKE_CASE_       :      float = None      ) ->							str:
        __snake_case           =						sigma_min if sigma_min is not None else self.config.sigma_min
        __snake_case           =						sigma_max if sigma_max is not None else self.config.sigma_max
        __snake_case           =						sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
             self.set_timesteps(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        __snake_case           =						sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        __snake_case           =						torch.exp(torch.linspace(math.log(SCREAMING_SNAKE_CASE_      )       ,   math.log(SCREAMING_SNAKE_CASE_      )       ,   SCREAMING_SNAKE_CASE_      )      )
        __snake_case           =						torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]      )
   def a  (						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      int      ) ->							Union[str, Any]:
        return torch.where(
            timesteps == 0       ,   torch.zeros_like(t.to(timesteps.device      )      )       ,   self.discrete_sigmas[timesteps - 1].to(timesteps.device      )       ,   )
   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      torch.FloatTensor       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      torch.FloatTensor       ,   SCREAMING_SNAKE_CASE_       :      Optional[torch.Generator] = None       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   ) ->							Union[SdeVeOutput, Tuple]:
        if self.timesteps is None:
             raise ValueError(
                 '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'      )

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

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

        #  equation 6: sample noise for the diffusion term of
        __snake_case           =						randn_tensor(
            sample.shape       ,   layout=sample.layout       ,   generator=SCREAMING_SNAKE_CASE_       ,   device=sample.device       ,   dtype=sample.dtype      )
        __snake_case           =						sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        __snake_case           =						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=SCREAMING_SNAKE_CASE_       ,   prev_sample_mean=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      torch.FloatTensor       ,   SCREAMING_SNAKE_CASE_       :      torch.FloatTensor       ,   SCREAMING_SNAKE_CASE_       :      Optional[torch.Generator] = None       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   ) ->							Union[SchedulerOutput, Tuple]:
        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
        __snake_case           =						randn_tensor(sample.shape       ,   layout=sample.layout       ,   generator=SCREAMING_SNAKE_CASE_      ).to(sample.device      )

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

        if not return_dict:
             return (prev_sample,)

        return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      torch.FloatTensor       ,   SCREAMING_SNAKE_CASE_       :      torch.FloatTensor       ,   SCREAMING_SNAKE_CASE_       :      torch.FloatTensor       ,   ) ->							torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        __snake_case           =						timesteps.to(original_samples.device      )
        __snake_case           =						self.discrete_sigmas.to(original_samples.device      )[timesteps]
        __snake_case           =						(
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(SCREAMING_SNAKE_CASE_      ) * sigmas[:, None, None, None]
        )
        __snake_case           =						noise + original_samples
        return noisy_samples





   def __len__(						self       :      Union[str, Any]      ) ->							Optional[Any]:
        return self.config.num_train_timesteps





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version
def 					_snake_case	(  *__snake_case  ,     __snake_case = None  ,     __snake_case=True  ,     __snake_case=2						):
					from .. import __version__

					_UpperCamelCase			     =     take_from
					_UpperCamelCase			     =     ()
					if not isinstance(args[0]  ,     __snake_case						):
										_UpperCamelCase			     =     (args,)

					for attribute, version_name, message in args:
										if version.parse(version.parse(__snake_case						).base_version						) >= version.parse(__snake_case						):
															raise ValueError(
															    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
															    f""" version {__version__} is >= {version_name}"""						)

										_UpperCamelCase			     =     None
										if isinstance(__snake_case  ,     __snake_case						) and attribute in deprecated_kwargs:
															values += (deprecated_kwargs.pop(__snake_case						),)
															_UpperCamelCase			     =     f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
										elif hasattr(__snake_case  ,     __snake_case						):
															values += (getattr(__snake_case  ,     __snake_case						),)
															_UpperCamelCase			     =     f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
										elif deprecated_kwargs is None:
															_UpperCamelCase			     =     f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

										if warning is not None:
															_UpperCamelCase			     =     warning + ''' ''' if standard_warn else ''''''
															warnings.warn(warning + message  ,     __snake_case  ,     stacklevel=__snake_case						)

					if isinstance(__snake_case  ,     __snake_case						) and len(__snake_case						) > 0:
										_UpperCamelCase			     =     inspect.getouterframes(inspect.currentframe()						)[1]
										_UpperCamelCase			     =     call_frame.filename
										_UpperCamelCase			     =     call_frame.lineno
										_UpperCamelCase			     =     call_frame.function
										_UpperCamelCase     ,    _UpperCamelCase			     =     next(iter(deprecated_kwargs.items()						)						)
										raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""						)

					if len(__snake_case						) == 0:
										return
					elif len(__snake_case						) == 1:
										return values[0]
					return values
