'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=0.9_9_9		,					UpperCAmelCase="cosine"		,					):
 if alpha_transform_type == "cosine":

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

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

 lowercase__    :   Optional[int]						= []
 for i in range(UpperCAmelCase					):
  lowercase__    :   List[str]						= i / num_diffusion_timesteps
  lowercase__    :   Union[str, Any]						= (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  (						a__   ,						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   [e.name for e in KarrasDiffusionSchedulers]
       SCREAMING_SNAKE_CASE 		=   2

       @register_to_config
       def __init__(						self     ,       __lowerCAmelCase = 1000     ,       __lowerCAmelCase = 0.0_0_0_8_5     ,       __lowerCAmelCase = 0.0_1_2     ,       __lowerCAmelCase = "linear"     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = "epsilon"     ,       __lowerCAmelCase = "linspace"     ,       __lowerCAmelCase = 0     ,       )  ->  str:
        if trained_betas is not None:
         lowercase__    :   Optional[Any]						= torch.tensor(__lowerCAmelCase     ,       dtype=torch.floataa			)
        elif beta_schedule == "linear":
         lowercase__    :   Union[str, Any]						= torch.linspace(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       dtype=torch.floataa			)
        elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         lowercase__    :   str						= (
             torch.linspace(beta_start**0.5     ,       beta_end**0.5     ,       __lowerCAmelCase     ,       dtype=torch.floataa			) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         lowercase__    :   Dict						= betas_for_alpha_bar(__lowerCAmelCase			)
        else:
         raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""			)

        lowercase__    :   Tuple						= 1.0 - self.betas
        lowercase__    :   Optional[Any]						= torch.cumprod(self.alphas     ,       dim=0			)

        #  set all values
        self.set_timesteps(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  Optional[int]:
        if schedule_timesteps is None:
         lowercase__    :   Any						= self.timesteps

        lowercase__    :   Optional[Any]						= (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter			) == 0:
         lowercase__    :   Union[str, Any]						= 1 if len(__lowerCAmelCase			) > 1 else 0
        else:
         lowercase__    :   int						= timestep.cpu().item() if torch.is_tensor(__lowerCAmelCase			) else timestep
         lowercase__    :   Any						= self._index_counter[timestep_int]

        return indices[pos].item()

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[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     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        lowercase__    :   str						= self.index_for_timestep(__lowerCAmelCase			)

        if self.state_in_first_order:
         lowercase__    :   int						= self.sigmas[step_index]
        else:
         lowercase__    :   List[str]						= self.sigmas_interpol[step_index]

        lowercase__    :   str						= sample / ((sigma**2 + 1) ** 0.5)
        return sample

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       )  ->  Optional[int]:
        lowercase__    :   List[str]						= num_inference_steps

        lowercase__    :   Optional[int]						= num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
         lowercase__    :   str						= np.linspace(0     ,       num_train_timesteps - 1     ,       __lowerCAmelCase     ,       dtype=__lowerCAmelCase			)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
         lowercase__    :   List[Any]						= 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
         lowercase__    :   int						= (np.arange(0     ,       __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(__lowerCAmelCase			)
         timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
         lowercase__    :   Dict						= 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
         lowercase__    :   Any						= (np.arange(__lowerCAmelCase     ,       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'."""			)

        lowercase__    :   Tuple						= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowercase__    :   List[str]						= torch.from_numpy(np.log(__lowerCAmelCase			)			).to(__lowerCAmelCase			)

        lowercase__    :   str						= np.interp(__lowerCAmelCase     ,       np.arange(0     ,       len(__lowerCAmelCase			)			)     ,       __lowerCAmelCase			)
        lowercase__    :   Any						= np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
        lowercase__    :   Optional[Any]						= torch.from_numpy(__lowerCAmelCase			).to(device=__lowerCAmelCase			)

        # interpolate sigmas
        lowercase__    :   Dict						= sigmas.log().lerp(sigmas.roll(1			).log()     ,       0.5			).exp()

        lowercase__    :   Tuple						= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
        lowercase__    :   Dict						= torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)

        if str(__lowerCAmelCase			).startswith('''mps'''			):
         # mps does not support float64
         lowercase__    :   Any						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase     ,       dtype=torch.floataa			)
        else:
         lowercase__    :   int						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase			)

        # interpolate timesteps
        lowercase__    :   Optional[int]						= self.sigma_to_t(__lowerCAmelCase			).to(__lowerCAmelCase     ,       dtype=timesteps.dtype			)
        lowercase__    :   str						= torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])     ,       dim=-1			).flatten()

        lowercase__    :   Optional[Any]						= torch.cat([timesteps[:1], interleaved_timesteps]			)

        lowercase__    :   Optional[int]						= None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        lowercase__    :   List[str]						= defaultdict(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        # get log sigma
        lowercase__    :   Optional[Any]						= sigma.log()

        # get distribution
        lowercase__    :   Dict						= log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        lowercase__    :   Any						= dists.ge(0			).cumsum(dim=0			).argmax(dim=0			).clamp(max=self.log_sigmas.shape[0] - 2			)
        lowercase__    :   Optional[Any]						= low_idx + 1

        lowercase__    :   Tuple						= self.log_sigmas[low_idx]
        lowercase__    :   str						= self.log_sigmas[high_idx]

        # interpolate sigmas
        lowercase__    :   Union[str, Any]						= (low - log_sigma) / (low - high)
        lowercase__    :   Optional[int]						= w.clamp(0     ,       1			)

        # transform interpolation to time range
        lowercase__    :   Optional[int]						= (1 - w) * low_idx + w * high_idx
        lowercase__    :   Dict						= t.view(sigma.shape			)
        return t

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        return self.sample is None

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = True     ,       )  ->  Union[SchedulerOutput, Tuple]:
        lowercase__    :   Optional[Any]						= self.index_for_timestep(__lowerCAmelCase			)

        # advance index counter by 1
        lowercase__    :   str						= timestep.cpu().item() if torch.is_tensor(__lowerCAmelCase			) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
         lowercase__    :   Optional[Any]						= self.sigmas[step_index]
         lowercase__    :   List[str]						= self.sigmas_interpol[step_index + 1]
         lowercase__    :   Dict						= self.sigmas[step_index + 1]
        else:
         # 2nd order / KDPM2's method
         lowercase__    :   List[Any]						= self.sigmas[step_index - 1]
         lowercase__    :   str						= self.sigmas_interpol[step_index]
         lowercase__    :   Dict						= self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        lowercase__    :   Optional[Any]						= 0
        lowercase__    :   Optional[int]						= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
         lowercase__    :   Optional[int]						= sigma_hat if self.state_in_first_order else sigma_interpol
         lowercase__    :   Dict						= sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
         lowercase__    :   Union[str, Any]						= sigma_hat if self.state_in_first_order else sigma_interpol
         lowercase__    :   str						= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**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`"""			)

        if self.state_in_first_order:
         # 2. Convert to an ODE derivative for 1st order
         lowercase__    :   Any						= (sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         lowercase__    :   Optional[int]						= sigma_interpol - sigma_hat

         # store for 2nd order step
         lowercase__    :   Union[str, Any]						= sample
        else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         lowercase__    :   Optional[int]						= (sample - pred_original_sample) / sigma_interpol

         # 3. delta timestep
         lowercase__    :   str						= sigma_next - sigma_hat

         lowercase__    :   Tuple						= self.sample
         lowercase__    :   Tuple						= None

        lowercase__    :   Union[str, Any]						= sample + derivative * dt

        if not return_dict:
         return (prev_sample,)

        return SchedulerOutput(prev_sample=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowercase__    :   int						= 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
         lowercase__    :   Dict						= self.timesteps.to(original_samples.device     ,       dtype=torch.floataa			)
         lowercase__    :   List[Any]						= timesteps.to(original_samples.device     ,       dtype=torch.floataa			)
        else:
         lowercase__    :   Union[str, Any]						= self.timesteps.to(original_samples.device			)
         lowercase__    :   Tuple						= timesteps.to(original_samples.device			)

        lowercase__    :   List[Any]						= [self.index_for_timestep(__lowerCAmelCase     ,       __lowerCAmelCase			) for t in timesteps]

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

        lowercase__    :   List[str]						= original_samples + noise * sigma
        return noisy_samples


       def __len__(						self			)  ->  List[str]:
        return self.config.num_train_timesteps


'''simple docstring'''

import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,					[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,					['''default''', 0, 100 * 2**20, 900 * 2**20]					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config		,					'''IN_MEMORY_MAX_SIZE'''		,					UpperCAmelCase					)
 lowercase__    :   List[Any]						= datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  lowercase__    :   str						= dataset_size < in_memory_max_size
 else:
  lowercase__    :   Optional[int]						= False
 lowercase__    :   Optional[Any]						= is_small_dataset(UpperCAmelCase					)
 assert result == expected

