
"""simple docstring"""




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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				42
     _A		    =				None





def 				a__			(					lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							List[str]=0.999							,     lowerCAmelCase						:							Union[str, Any]="cosine"							,     ):






     '''simple docstring'''
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCAmelCase						:							Optional[int]		):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowerCAmelCase						:							List[Any]		):
               return math.exp(t * -12.0		)

     else:
          raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"		)

     UpperCAmelCase__				:			Optional[Any]							=    []
     for i in range(lowerCAmelCase		):
          UpperCAmelCase__				:			Any							=    i / num_diffusion_timesteps
          UpperCAmelCase__				:			List[str]							=    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(lowerCAmelCase		) / alpha_bar_fn(lowerCAmelCase		)							,     lowerCAmelCase		)		)
     return torch.tensor(lowerCAmelCase							,     dtype=torch.floataa		)







class 	_lowercase    (							lowerCAmelCase_						, lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				[e.name for e in KarrasDiffusionSchedulers]
     _A		    =				1



     @register_to_config
     def __init__(   self			,      __UpperCamelCase = 10_00			,      __UpperCamelCase = 0.0001			,      __UpperCamelCase = 0.02			,      __UpperCamelCase = "linear"			,      __UpperCamelCase = None			,      __UpperCamelCase = False			,      __UpperCamelCase = "epsilon"			,      __UpperCamelCase = "linspace"			,      __UpperCamelCase = 0			,      )->       List[Any]:
          if trained_betas is not None:
               UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(__UpperCamelCase			,      dtype=torch.floataa	)
          elif beta_schedule == "linear":
               UpperCAmelCase__				:			List[str]							=    torch.linspace(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               UpperCAmelCase__				:			Union[str, Any]							=    (
                   torch.linspace(beta_start**0.5			,      beta_end**0.5			,      __UpperCamelCase			,      dtype=torch.floataa	) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               UpperCAmelCase__				:			Any							=    betas_for_alpha_bar(__UpperCamelCase	)
          else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

          UpperCAmelCase__				:			Dict							=    1.0 - self.betas
          UpperCAmelCase__				:			int							=    torch.cumprod(self.alphas			,      dim=0	)

          UpperCAmelCase__				:			Optional[int]							=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          UpperCAmelCase__				:			Optional[int]							=    np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
          UpperCAmelCase__				:			Dict							=    torch.from_numpy(__UpperCamelCase	)

          # setable values
          UpperCAmelCase__				:			List[str]							=    None
          UpperCAmelCase__				:			str							=    use_karras_sigmas
          self.set_timesteps(__UpperCamelCase			,      __UpperCamelCase	)
          UpperCAmelCase__				:			List[str]							=    []
          UpperCAmelCase__				:			Dict							=    False



     @property
     def 			lowerCAmelCase__							(   self	)->       Any:
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       torch.FloatTensor:
          if isinstance(__UpperCamelCase			,      torch.Tensor	):
               UpperCAmelCase__				:			str							=    timestep.to(self.timesteps.device	)
          UpperCAmelCase__				:			Union[str, Any]							=    (self.timesteps == timestep).nonzero().item()
          UpperCAmelCase__				:			Optional[int]							=    self.sigmas[step_index]
          UpperCAmelCase__				:			Tuple							=    sample / ((sigma**2 + 1) ** 0.5)
          UpperCAmelCase__				:			List[Any]							=    True
          return sample



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)->       Dict:

          def lms_derivative(__UpperCamelCase	):
               UpperCAmelCase__				:			Dict							=    1.0
               for k in range(__UpperCamelCase	):
                    if current_order == k:
                         continue
                    prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
               return prod

          UpperCAmelCase__				:			int							=    integrate.quad(__UpperCamelCase			,      self.sigmas[t]			,      self.sigmas[t + 1]			,      epsrel=1E-4	)[0]

          return integrated_coeff



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase = None	)->       Optional[Any]:
          UpperCAmelCase__				:			Union[str, Any]							=    num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
               UpperCAmelCase__				:			Union[str, Any]							=    np.linspace(0			,      self.config.num_train_timesteps - 1			,      __UpperCamelCase			,      dtype=__UpperCamelCase	)[
                   ::-1
               ].copy()
          elif self.config.timestep_spacing == "leading":
               UpperCAmelCase__				:			Tuple							=    self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               UpperCAmelCase__				:			Tuple							=    (np.arange(0			,      __UpperCamelCase	) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase	)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               UpperCAmelCase__				:			str							=    self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               UpperCAmelCase__				:			Union[str, Any]							=    (np.arange(self.config.num_train_timesteps			,      0			,      -step_ratio	)).round().copy().astype(__UpperCamelCase	)
               timesteps -= 1
          else:
               raise ValueError(
                   F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)

          UpperCAmelCase__				:			Dict							=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          UpperCAmelCase__				:			List[str]							=    np.log(__UpperCamelCase	)
          UpperCAmelCase__				:			str							=    np.interp(__UpperCamelCase			,      np.arange(0			,      len(__UpperCamelCase	)	)			,      __UpperCamelCase	)

          if self.use_karras_sigmas:
               UpperCAmelCase__				:			Tuple							=    self._convert_to_karras(in_sigmas=__UpperCamelCase	)
               UpperCAmelCase__				:			Optional[Any]							=    np.array([self._sigma_to_t(__UpperCamelCase			,      __UpperCamelCase	) for sigma in sigmas]	)

          UpperCAmelCase__				:			List[Any]							=    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)

          UpperCAmelCase__				:			List[Any]							=    torch.from_numpy(__UpperCamelCase	).to(device=__UpperCamelCase	)
          if str(__UpperCamelCase	).startswith("mps"	):
               # mps does not support float64
               UpperCAmelCase__				:			Optional[Any]							=    torch.from_numpy(__UpperCamelCase	).to(__UpperCamelCase			,      dtype=torch.floataa	)
          else:
               UpperCAmelCase__				:			Dict							=    torch.from_numpy(__UpperCamelCase	).to(device=__UpperCamelCase	)

          UpperCAmelCase__				:			Optional[Any]							=    []



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       Union[str, Any]:
          # get log sigma
          UpperCAmelCase__				:			Dict							=    np.log(__UpperCamelCase	)

          # get distribution
          UpperCAmelCase__				:			str							=    log_sigma - log_sigmas[:, np.newaxis]

          # get sigmas range
          UpperCAmelCase__				:			str							=    np.cumsum((dists >= 0)			,      axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
          UpperCAmelCase__				:			Tuple							=    low_idx + 1

          UpperCAmelCase__				:			Optional[Any]							=    log_sigmas[low_idx]
          UpperCAmelCase__				:			int							=    log_sigmas[high_idx]

          # interpolate sigmas
          UpperCAmelCase__				:			Optional[Any]							=    (low - log_sigma) / (low - high)
          UpperCAmelCase__				:			Dict							=    np.clip(__UpperCamelCase			,      0			,      1	)

          # transform interpolation to time range
          UpperCAmelCase__				:			List[str]							=    (1 - w) * low_idx + w * high_idx
          UpperCAmelCase__				:			Dict							=    t.reshape(sigma.shape	)
          return t



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       torch.FloatTensor:
          UpperCAmelCase__				:			float							=    in_sigmas[-1].item()
          UpperCAmelCase__				:			float							=    in_sigmas[0].item()

          UpperCAmelCase__				:			Optional[Any]							=    7.0  # 7.0 is the value used in the paper
          UpperCAmelCase__				:			List[Any]							=    np.linspace(0			,      1			,      self.num_inference_steps	)
          UpperCAmelCase__				:			Optional[int]							=    sigma_min ** (1 / rho)
          UpperCAmelCase__				:			List[str]							=    sigma_max ** (1 / rho)
          UpperCAmelCase__				:			List[Any]							=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = 4			,      __UpperCamelCase = True			,      )->       Union[LMSDiscreteSchedulerOutput, Tuple]:
          if not self.is_scale_input_called:
               warnings.warn(
                   "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                   "See `StableDiffusionPipeline` for a usage example."	)

          if isinstance(__UpperCamelCase			,      torch.Tensor	):
               UpperCAmelCase__				:			str							=    timestep.to(self.timesteps.device	)
          UpperCAmelCase__				:			List[str]							=    (self.timesteps == timestep).nonzero().item()
          UpperCAmelCase__				:			Union[str, Any]							=    self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               UpperCAmelCase__				:			int							=    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               UpperCAmelCase__				:			int							=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
               UpperCAmelCase__				:			str							=    model_output
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

          # 2. Convert to an ODE derivative
          UpperCAmelCase__				:			Optional[int]							=    (sample - pred_original_sample) / sigma
          self.derivatives.append(__UpperCamelCase	)
          if len(self.derivatives	) > order:
               self.derivatives.pop(0	)

          # 3. Compute linear multistep coefficients
          UpperCAmelCase__				:			Optional[Any]							=    min(step_index + 1			,      __UpperCamelCase	)
          UpperCAmelCase__				:			Dict							=    [self.get_lms_coefficient(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	) for curr_order in range(__UpperCamelCase	)]

          # 4. Compute previous sample based on the derivatives path
          UpperCAmelCase__				:			Dict							=    sample + sum(
              coeff * derivative for coeff, derivative in zip(__UpperCamelCase			,      reversed(self.derivatives	)	)	)

          if not return_dict:
               return (prev_sample,)

          return LMSDiscreteSchedulerOutput(prev_sample=__UpperCamelCase			,      pred_original_sample=__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      )->       torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCAmelCase__				:			List[str]							=    self.sigmas.to(device=original_samples.device			,      dtype=original_samples.dtype	)
          if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase	):
               # mps does not support float64
               UpperCAmelCase__				:			Dict							=    self.timesteps.to(original_samples.device			,      dtype=torch.floataa	)
               UpperCAmelCase__				:			Optional[Any]							=    timesteps.to(original_samples.device			,      dtype=torch.floataa	)
          else:
               UpperCAmelCase__				:			List[str]							=    self.timesteps.to(original_samples.device	)
               UpperCAmelCase__				:			int							=    timesteps.to(original_samples.device	)

          UpperCAmelCase__				:			Dict							=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          UpperCAmelCase__				:			Any							=    sigmas[step_indices].flatten()
          while len(sigma.shape	) < len(original_samples.shape	):
               UpperCAmelCase__				:			Any							=    sigma.unsqueeze(-1	)

          UpperCAmelCase__				:			Union[str, Any]							=    original_samples + noise * sigma
          return noisy_samples



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





"""simple docstring"""




import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


A__   :						Optional[Any] 							=     set()
A__   :						List[str] 							=     get_logger(__name__)



def 				a__			(					lowerCAmelCase						:							Optional[str] = None		):






     '''simple docstring'''

     def decorator(lowerCAmelCase						:							Callable		):
          global _emitted_deprecation_warnings

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			Any							=    deprecated_class_or_function.__init__
               UpperCAmelCase__				:			Optional[int]							=    deprecated_class_or_function.__name__
          else:
               UpperCAmelCase__				:			str							=    deprecated_class_or_function
               UpperCAmelCase__				:			List[Any]							=    deprecated_function.__name__
               # Support deprecating __init__ class method: class name instead
               UpperCAmelCase__				:			List[str]							=    name if name != "__init__" else deprecated_function.__qualname__.split("."		)[-2]

          UpperCAmelCase__				:			Optional[Any]							=    (
              F"{name} is deprecated and will be removed in the next major version of datasets." + F" {help_message}"
              if help_message
              else ""
          )

          @wraps(lowerCAmelCase		)
          def wrapper(*lowerCAmelCase						:							int							,     **lowerCAmelCase						:							str		):
               UpperCAmelCase__				:			List[str]							=    hash(lowerCAmelCase		)
               if func_hash not in _emitted_deprecation_warnings:
                    warnings.warn(lowerCAmelCase							,     category=lowerCAmelCase							,     stacklevel=2		)
                    _emitted_deprecation_warnings.add(lowerCAmelCase		)
               return deprecated_function(*lowerCAmelCase							,     **lowerCAmelCase		)

          UpperCAmelCase__				:			List[str]							=    "deprecated"

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			List[str]							=    wrapper
               return deprecated_class_or_function
          else:
               return wrapper

     return decorator







class 	_lowercase    (							enum.EnumMeta					):


     '''simple docstring'''





     def __getattribute__(   cls			,      __UpperCamelCase	)->       Optional[Any]:
          UpperCAmelCase__				:			Optional[int]							=    super().__getattribute__(__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj



     def __getitem__(   cls			,      __UpperCamelCase	)->       Optional[int]:
          UpperCAmelCase__				:			Union[str, Any]							=    super().__getitem__(__UpperCamelCase	)
          if member._on_access:
               member._on_access()
          return member



     def __call__(   cls			,      __UpperCamelCase			,      __UpperCamelCase=None			,      *, __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=1	)->       Dict:
          UpperCAmelCase__				:			Optional[int]							=    super().__call__(__UpperCamelCase			,      __UpperCamelCase			,      module=__UpperCamelCase			,      qualname=__UpperCamelCase			,      type=__UpperCamelCase			,      start=__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj







class 	_lowercase    (							enum.Enum						, metaclass=lowerCAmelCase_					):


     '''simple docstring'''





     def __new__(   cls			,      __UpperCamelCase	)->       Union[str, Any]:
          UpperCAmelCase__				:			int							=    object.__new__(cls	)
          UpperCAmelCase__				:			int							=    value
          UpperCAmelCase__				:			Optional[int]							=    member.deprecate
          return member



     @property
     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          return ""



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			Tuple							=    F" {self.help_message}" if self.help_message else ""
          warnings.warn(
              F"'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."
              + help_message			,      __UpperCamelCase			,      stacklevel=3			,      )



