




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


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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					torch.FloatTensor
     _SCREAMING_SNAKE_CASE      :					Optional[torch.FloatTensor]     					=    None





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]   ,       UpperCAmelCase_	:      str=0.9_99   ,       UpperCAmelCase_	:      int="cosine"   ,       )->			Union[str, Any]:

       """simple docstring"""




       if alpha_transform_type == "cosine":

              def alpha_bar_fn(UpperCAmelCase_	:      Any      ):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

       elif alpha_transform_type == "exp":

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

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

       a			   =[]
       for i in range(UpperCAmelCase_      ):
              a			   =i / num_diffusion_timesteps
              a			   =(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        UpperCAmelCase__				(							UpperCAmelCase__      ,		UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Optional[Any]     					=    [e.name for e in KarrasDiffusionSchedulers]
     _SCREAMING_SNAKE_CASE      :					Any     					=    1
     @register_to_config
     def __init__(				self ,    _lowerCAmelCase = 1_000 ,    _lowerCAmelCase = 0.00_01 ,    _lowerCAmelCase = 0.02 ,    _lowerCAmelCase = "linear" ,    _lowerCAmelCase = None ,    _lowerCAmelCase = "epsilon" ,    _lowerCAmelCase = "linspace" ,    _lowerCAmelCase = 0 ,    ):
            if trained_betas is not None:
                   a			   =torch.tensor(_lowerCAmelCase ,    dtype=torch.floataa	)
            elif beta_schedule == "linear":
                   a			   =torch.linspace(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    dtype=torch.floataa	)
            elif beta_schedule == "scaled_linear":
                   # this schedule is very specific to the latent diffusion model.
                   a			   =(
                       torch.linspace(beta_start**0.5 ,    beta_end**0.5 ,    _lowerCAmelCase ,    dtype=torch.floataa	) ** 2
                   )
            elif beta_schedule == "squaredcos_cap_v2":
                   # Glide cosine schedule
                   a			   =betas_for_alpha_bar(_lowerCAmelCase	)
            else:
                   raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

            a			   =1.0 - self.betas
            a			   =torch.cumprod(self.alphas ,    dim=0	)

            a			   =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
            a			   =np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
            a			   =torch.from_numpy(_lowerCAmelCase	)

            # setable values
            a			   =None
            a			   =np.linspace(0 ,    num_train_timesteps - 1 ,    _lowerCAmelCase ,    dtype=_lowerCAmelCase	)[::-1].copy()
            a			   =torch.from_numpy(_lowerCAmelCase	)
            a			   =False
     @property
     def        lowerCAmelCase__  (				self	):
            # 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 ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            if isinstance(_lowerCAmelCase ,    torch.Tensor	):
                   a			   =timestep.to(self.timesteps.device	)
            a			   =(self.timesteps == timestep).nonzero().item()
            a			   =self.sigmas[step_index]
            a			   =sample / ((sigma**2 + 1) ** 0.5)
            a			   =True
            return sample
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = None	):
            a			   =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":
                   a			   =np.linspace(0 ,    self.config.num_train_timesteps - 1 ,    _lowerCAmelCase ,    dtype=_lowerCAmelCase	)[
                       ::-1
                   ].copy()
            elif self.config.timestep_spacing == "leading":
                   a			   =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
                   a			   =(np.arange(0 ,    _lowerCAmelCase	) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase	)
                   timesteps += self.config.steps_offset
            elif self.config.timestep_spacing == "trailing":
                   a			   =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
                   a			   =(np.arange(self.config.num_train_timesteps ,    0 ,    -step_ratio	)).round().copy().astype(_lowerCAmelCase	)
                   timesteps -= 1
            else:
                   raise ValueError(
                       F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

            a			   =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
            a			   =np.interp(_lowerCAmelCase ,    np.arange(0 ,    len(_lowerCAmelCase	)	) ,    _lowerCAmelCase	)
            a			   =np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
            a			   =torch.from_numpy(_lowerCAmelCase	).to(device=_lowerCAmelCase	)
            if str(_lowerCAmelCase	).startswith("""mps"""	):
                   # mps does not support float64
                   a			   =torch.from_numpy(_lowerCAmelCase	).to(_lowerCAmelCase ,    dtype=torch.floataa	)
            else:
                   a			   =torch.from_numpy(_lowerCAmelCase	).to(device=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = None ,    _lowerCAmelCase = True ,    ):

            if (
                isinstance(_lowerCAmelCase ,    _lowerCAmelCase	)
                or isinstance(_lowerCAmelCase ,    torch.IntTensor	)
                or isinstance(_lowerCAmelCase ,    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(_lowerCAmelCase ,    torch.Tensor	):
                   a			   =timestep.to(self.timesteps.device	)

            a			   =(self.timesteps == timestep).nonzero().item()
            a			   =self.sigmas[step_index]

            # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
            if self.config.prediction_type == "epsilon":
                   a			   =sample - sigma * model_output
            elif self.config.prediction_type == "v_prediction":
                   # * c_out + input * c_skip
                   a			   =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
            elif self.config.prediction_type == "sample":
                   raise NotImplementedError("""prediction_type not implemented yet: sample"""	)
            else:
                   raise ValueError(
                       F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

            a			   =self.sigmas[step_index]
            a			   =self.sigmas[step_index + 1]
            a			   =(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
            a			   =(sigma_to**2 - sigma_up**2) ** 0.5

            # 2. Convert to an ODE derivative
            a			   =(sample - pred_original_sample) / sigma

            a			   =sigma_down - sigma

            a			   =sample + derivative * dt

            a			   =model_output.device
            a			   =randn_tensor(model_output.shape ,    dtype=model_output.dtype ,    device=_lowerCAmelCase ,    generator=_lowerCAmelCase	)

            a			   =prev_sample + noise * sigma_up

            if not return_dict:
                   return (prev_sample,)

            return EulerAncestralDiscreteSchedulerOutput(
                prev_sample=_lowerCAmelCase ,    pred_original_sample=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    ):
            # Make sure sigmas and timesteps have the same device and dtype as original_samples
            a			   =self.sigmas.to(device=original_samples.device ,    dtype=original_samples.dtype	)
            if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase	):
                   # mps does not support float64
                   a			   =self.timesteps.to(original_samples.device ,    dtype=torch.floataa	)
                   a			   =timesteps.to(original_samples.device ,    dtype=torch.floataa	)
            else:
                   a			   =self.timesteps.to(original_samples.device	)
                   a			   =timesteps.to(original_samples.device	)

            a			   =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

            a			   =original_samples + noise * sigma
            return noisy_samples






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






from __future__ import annotations





def 							lowerCamelCase    (							UpperCAmelCase_	:      list[int | float]      )->			int | float:

       """simple docstring"""




       if len(UpperCAmelCase_      ) == 0:
              raise ValueError("""find_min() arg is an empty sequence"""      )
       a			   =nums[0]
       for num in nums:
              a			   =min(UpperCAmelCase_   ,       UpperCAmelCase_      )
       return min_num


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
