'''simple docstring'''




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

import numpy as np
import torch

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


__SCREAMING_SNAKE_CASE :  Dict  =						logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						torch.FloatTensor
      __UpperCamelCase:						Optional[torch.FloatTensor]      			=		None

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Dict    ,			_UpperCAmelCase						:   Any=0.9_9_9    ,			_UpperCAmelCase						:   Union[str, Any]="cosine"    ,			)					->					Any:


       """simple docstring"""







       if alpha_transform_type == "cosine":

              def alpha_bar_fn(_UpperCAmelCase						:   str					):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(_UpperCAmelCase						:   str					):
                     return math.exp(t * -1_2.0					)

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

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


class 			lowerCamelCase_			(snake_case__							,				snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						Optional[Any]      			=		[e.name for e in KarrasDiffusionSchedulers]
      __UpperCamelCase:						List[Any]      			=		1




      @register_to_config
      def __init__( self		:       int		,      A		:       int = 1000		,      A		:       float = 0.0_001		,      A		:       float = 0.02		,      A		:       str = "linear"		,      A		:       Optional[Union[np.ndarray, List[float]]] = None		,      A		:       str = "epsilon"		,      A		:       str = "linear"		,      A		:       Optional[bool] = False		,      A		:       str = "linspace"		,      A		:       int = 0		,      ):
             if trained_betas is not None:
                    _UpperCAmelCase  :						Any							   =					torch.tensor(A		,      dtype=torch.floataa )
             elif beta_schedule == "linear":
                    _UpperCAmelCase  :						Optional[Any]							   =					torch.linspace(A		,      A		,      A		,      dtype=torch.floataa )
             elif beta_schedule == "scaled_linear":
                    # this schedule is very specific to the latent diffusion model.
                    _UpperCAmelCase  :						int							   =					(
                        torch.linspace(beta_start**0.5		,      beta_end**0.5		,      A		,      dtype=torch.floataa ) ** 2
                    )
             elif beta_schedule == "squaredcos_cap_v2":
                    # Glide cosine schedule
                    _UpperCAmelCase  :						Tuple							   =					betas_for_alpha_bar(A )
             else:
                    raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )

             _UpperCAmelCase  :						str							   =					1.0 - self.betas
             _UpperCAmelCase  :						Any							   =					torch.cumprod(self.alphas		,      dim=0 )

             _UpperCAmelCase  :						Tuple							   =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
             _UpperCAmelCase  :						Union[str, Any]							   =					np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
             _UpperCAmelCase  :						List[Any]							   =					torch.from_numpy(A )

             # setable values
             _UpperCAmelCase  :						Any							   =					None
             _UpperCAmelCase  :						Union[str, Any]							   =					np.linspace(0		,      num_train_timesteps - 1		,      A		,      dtype=A )[::-1].copy()
             _UpperCAmelCase  :						int							   =					torch.from_numpy(A )
             _UpperCAmelCase  :						int							   =					False
             _UpperCAmelCase  :						str							   =					use_karras_sigmas




      @property
      def   _A			( 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   _A			( self		:       Tuple		,      A		:       torch.FloatTensor		,      A		:       Union[float, torch.FloatTensor] ):
             if isinstance(A		,      torch.Tensor ):
                    _UpperCAmelCase  :						Optional[Any]							   =					timestep.to(self.timesteps.device )
             _UpperCAmelCase  :						Dict							   =					(self.timesteps == timestep).nonzero().item()
             _UpperCAmelCase  :						Optional[Any]							   =					self.sigmas[step_index]

             _UpperCAmelCase  :						Tuple							   =					sample / ((sigma**2 + 1) ** 0.5)

             _UpperCAmelCase  :						Any							   =					True
             return sample




      def   _A			( self		:       Tuple		,      A		:       int		,      A		:       Union[str, torch.device] = None ):
             _UpperCAmelCase  :						Optional[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  :						Optional[Any]							   =					np.linspace(0		,      self.config.num_train_timesteps - 1		,      A		,      dtype=A )[
                        ::-1
                    ].copy()
             elif self.config.timestep_spacing == "leading":
                    _UpperCAmelCase  :						Optional[Any]							   =					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  :						Dict							   =					(np.arange(0		,      A ) * step_ratio).round()[::-1].copy().astype(A )
                    timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                    _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  :						Optional[Any]							   =					(np.arange(self.config.num_train_timesteps		,      0		,      -step_ratio )).round().copy().astype(A )
                    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  :						str							   =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
             _UpperCAmelCase  :						Union[str, Any]							   =					np.log(A )

             if self.config.interpolation_type == "linear":
                    _UpperCAmelCase  :						str							   =					np.interp(A		,      np.arange(0		,      len(A ) )		,      A )
             elif self.config.interpolation_type == "log_linear":
                    _UpperCAmelCase  :						Tuple							   =					torch.linspace(np.log(sigmas[-1] )		,      np.log(sigmas[0] )		,      num_inference_steps + 1 ).exp()
             else:
                    raise ValueError(
                        F"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
                        " 'linear' or 'log_linear'" )

             if self.use_karras_sigmas:
                    _UpperCAmelCase  :						List[str]							   =					self._convert_to_karras(in_sigmas=A		,      num_inference_steps=self.num_inference_steps )
                    _UpperCAmelCase  :						Optional[int]							   =					np.array([self._sigma_to_t(A		,      A ) for sigma in sigmas] )

             _UpperCAmelCase  :						Optional[Any]							   =					np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
             _UpperCAmelCase  :						Any							   =					torch.from_numpy(A ).to(device=A )
             if str(A ).startswith("mps" ):
                    # mps does not support float64
                    _UpperCAmelCase  :						Optional[Any]							   =					torch.from_numpy(A ).to(A		,      dtype=torch.floataa )
             else:
                    _UpperCAmelCase  :						Optional[int]							   =					torch.from_numpy(A ).to(device=A )




      def   _A			( self		:       Union[str, Any]		,      A		:       Dict		,      A		:       List[str] ):
             # get log sigma
             _UpperCAmelCase  :						Union[str, Any]							   =					np.log(A )

             # get distribution
             _UpperCAmelCase  :						int							   =					log_sigma - log_sigmas[:, np.newaxis]

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

             _UpperCAmelCase  :						Dict							   =					log_sigmas[low_idx]
             _UpperCAmelCase  :						Dict							   =					log_sigmas[high_idx]

             # interpolate sigmas
             _UpperCAmelCase  :						str							   =					(low - log_sigma) / (low - high)
             _UpperCAmelCase  :						str							   =					np.clip(A		,      0		,      1 )

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




      def   _A			( self		:       Any		,      A		:       torch.FloatTensor		,      A		:       Dict ):
             _UpperCAmelCase  :						float							   =					in_sigmas[-1].item()
             _UpperCAmelCase  :						float							   =					in_sigmas[0].item()

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




      def   _A			( self		:       Optional[int]		,      A		:       torch.FloatTensor		,      A		:       Union[float, torch.FloatTensor]		,      A		:       torch.FloatTensor		,      A		:       float = 0.0		,      A		:       float = 0.0		,      A		:       float = float("inf" )		,      A		:       float = 1.0		,      A		:       Optional[torch.Generator] = None		,      A		:       bool = True		,      ):

             if (
                 isinstance(A		,      A )
                 or isinstance(A		,      torch.IntTensor )
                 or isinstance(A		,      torch.LongTensor )
             ):
                    raise ValueError(
                        (
                            "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                            " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                            " one of the `scheduler.timesteps` as a timestep."
                        )		,      )

             if not self.is_scale_input_called:
                    logger.warning(
                        "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                        "See `StableDiffusionPipeline` for a usage example." )

             if isinstance(A		,      torch.Tensor ):
                    _UpperCAmelCase  :						List[str]							   =					timestep.to(self.timesteps.device )

             _UpperCAmelCase  :						Union[str, Any]							   =					(self.timesteps == timestep).nonzero().item()
             _UpperCAmelCase  :						str							   =					self.sigmas[step_index]

             _UpperCAmelCase  :						Dict							   =					min(s_churn / (len(self.sigmas ) - 1)		,      2**0.5 - 1 ) if s_tmin <= sigma <= s_tmax else 0.0

             _UpperCAmelCase  :						List[Any]							   =					randn_tensor(
                 model_output.shape		,      dtype=model_output.dtype		,      device=model_output.device		,      generator=A )

             _UpperCAmelCase  :						Optional[Any]							   =					noise * s_noise
             _UpperCAmelCase  :						Dict							   =					sigma * (gamma + 1)

             if gamma > 0:
                    _UpperCAmelCase  :						Union[str, Any]							   =					sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             # NOTE: "original_sample" should not be an expected prediction_type but is left in for
             # backwards compatibility
             if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
                    _UpperCAmelCase  :						List[Any]							   =					model_output
             elif self.config.prediction_type == "epsilon":
                    _UpperCAmelCase  :						List[str]							   =					sample - sigma_hat * model_output
             elif self.config.prediction_type == "v_prediction":
                    # * c_out + input * c_skip
                    _UpperCAmelCase  :						Dict							   =					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
             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  :						int							   =					(sample - pred_original_sample) / sigma_hat

             _UpperCAmelCase  :						Any							   =					self.sigmas[step_index + 1] - sigma_hat

             _UpperCAmelCase  :						str							   =					sample + derivative * dt

             if not return_dict:
                    return (prev_sample,)

             return EulerDiscreteSchedulerOutput(prev_sample=A		,      pred_original_sample=A )




      def   _A			( self		:       Any		,      A		:       torch.FloatTensor		,      A		:       torch.FloatTensor		,      A		:       torch.FloatTensor		,      ):
             # Make sure sigmas and timesteps have the same device and dtype as original_samples
             _UpperCAmelCase  :						Dict							   =					self.sigmas.to(device=original_samples.device		,      dtype=original_samples.dtype )
             if original_samples.device.type == "mps" and torch.is_floating_point(A ):
                    # mps does not support float64
                    _UpperCAmelCase  :						Any							   =					self.timesteps.to(original_samples.device		,      dtype=torch.floataa )
                    _UpperCAmelCase  :						str							   =					timesteps.to(original_samples.device		,      dtype=torch.floataa )
             else:
                    _UpperCAmelCase  :						int							   =					self.timesteps.to(original_samples.device )
                    _UpperCAmelCase  :						Dict							   =					timesteps.to(original_samples.device )

             _UpperCAmelCase  :						Any							   =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

             _UpperCAmelCase  :						Union[str, Any]							   =					sigmas[step_indices].flatten()
             while len(sigma.shape ) < len(original_samples.shape ):
                    _UpperCAmelCase  :						Optional[int]							   =					sigma.unsqueeze(-1 )

             _UpperCAmelCase  :						Any							   =					original_samples + noise * sigma
             return noisy_samples





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


'''simple docstring'''




import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"    ,			[
        (Version("1.0.0"					), True),
        ("1.0.0", True),
        (Version("2.0.0"					), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]    ,			)
def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   List[Any]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Union[str, Any]							   =					Version("1.0.0"					)
       assert (version == other) is expected_equality
       assert (version != other) is not expected_equality
       assert (hash(_UpperCAmelCase					) == hash(_UpperCAmelCase					)) is expected_equality

