'''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 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      str=0.999   ,   UpperCAmelCase__ :      int="cosine"   ,   )    ->   Optional[Any]:







							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCAmelCase__ :      Optional[Any]				):
																					return math.cos((t + 0.008) / 1.008 * 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}'''				)

							lowercase_      :    Any       		= []
							for i in range(UpperCAmelCase__				):
														lowercase_      :    int       		= i / num_diffusion_timesteps
														lowercase_      :    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 					__magic_name__		(					_lowerCAmelCase,  _lowerCAmelCase):
			UpperCamelCase__       = [e.name for e in KarrasDiffusionSchedulers]
			UpperCamelCase__       = 2







			@register_to_config
			def __init__(			self       :						List[str]       ,    lowercase_       :						int = 1000       ,    lowercase_       :						float = 0.0_00_85       ,    lowercase_       :						float = 0.0_12       ,    lowercase_       :						str = "linear"       ,    lowercase_       :						Optional[Union[np.ndarray, List[float]]] = None       ,    lowercase_       :						str = "epsilon"       ,    lowercase_       :						Optional[bool] = False       ,    lowercase_       :						Optional[bool] = False       ,    lowercase_       :						float = 1.0       ,    lowercase_       :						str = "linspace"       ,    lowercase_       :						int = 0       ,    ):







										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_      :    int       		= (
																	    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_      :    Optional[int]       		= betas_for_alpha_bar(_lowerCAmelCase       ,    alpha_transform_type="""cosine"""					)
										elif beta_schedule == "exp":
																	lowercase_      :    List[Any]       		= betas_for_alpha_bar(_lowerCAmelCase       ,    alpha_transform_type="""exp"""					)
										else:
																	raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

										lowercase_      :    Any       		= 1.0 - self.betas
										lowercase_      :    Union[str, Any]       		= torch.cumprod(self.alphas       ,    dim=0					)

										#  set all values
										self.set_timesteps(_lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase					)
										lowercase_      :    Dict       		= use_karras_sigmas







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						List[str]       ,    lowercase_       :						Optional[Any]=None					):







										if schedule_timesteps is None:
																	lowercase_      :    Dict       		= self.timesteps

										lowercase_      :    List[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_      :    str       		= 1 if len(_lowerCAmelCase					) > 1 else 0
										else:
																	lowercase_      :    List[Any]       		= timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase					) else timestep
																	lowercase_      :    List[Any]       		= self._index_counter[timestep_int]

										return indices[pos].item()







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):







										if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						torch.FloatTensor       ,    lowercase_       :						Union[float, torch.FloatTensor]       ,    ):
										lowercase_      :    int       		= self.index_for_timestep(_lowerCAmelCase					)

										lowercase_      :    Union[str, Any]       		= self.sigmas[step_index]
										lowercase_      :    Union[str, Any]       		= sample / ((sigma**2 + 1) ** 0.5)
										return sample







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						int       ,    lowercase_       :						Union[str, torch.device] = None       ,    lowercase_       :						Optional[int] = None       ,    ):
										lowercase_      :    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_      :    Optional[Any]       		= np.linspace(0       ,    num_train_timesteps - 1       ,    _lowerCAmelCase       ,    dtype=_lowerCAmelCase					)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
																	lowercase_      :    Optional[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_      :    str       		= 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_      :    Union[str, 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_      :    str       		= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
										lowercase_      :    Dict       		= np.log(_lowerCAmelCase					)
										lowercase_      :    Optional[Any]       		= np.interp(_lowerCAmelCase       ,    np.arange(0       ,    len(_lowerCAmelCase					)					)       ,    _lowerCAmelCase					)

										if self.config.use_karras_sigmas:
																	lowercase_      :    Any       		= self._convert_to_karras(in_sigmas=_lowerCAmelCase       ,    num_inference_steps=self.num_inference_steps					)
																	lowercase_      :    str       		= np.array([self._sigma_to_t(_lowerCAmelCase       ,    _lowerCAmelCase					) for sigma in sigmas]					)

										lowercase_      :    str       		= np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
										lowercase_      :    List[str]       		= torch.from_numpy(_lowerCAmelCase					).to(device=_lowerCAmelCase					)
										lowercase_      :    int       		= torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

										lowercase_      :    int       		= torch.from_numpy(_lowerCAmelCase					)
										lowercase_      :    Dict       		= torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

										if str(_lowerCAmelCase					).startswith("""mps"""					):
																	# mps does not support float64
																	lowercase_      :    int       		= timesteps.to(_lowerCAmelCase       ,    dtype=torch.floataa					)
										else:
																	lowercase_      :    Union[str, Any]       		= timesteps.to(device=_lowerCAmelCase					)

										# empty dt and derivative
										lowercase_      :    Optional[Any]       		= None
										lowercase_      :    Union[str, Any]       		= None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										lowercase_      :    Union[str, Any]       		= defaultdict(_lowerCAmelCase					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= np.log(_lowerCAmelCase					)

										# get distribution
										lowercase_      :    List[Any]       		= log_sigma - log_sigmas[:, np.newaxis]

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

										lowercase_      :    Optional[int]       		= log_sigmas[low_idx]
										lowercase_      :    Dict       		= log_sigmas[high_idx]

										# interpolate sigmas
										lowercase_      :    Tuple       		= (low - log_sigma) / (low - high)
										lowercase_      :    Union[str, Any]       		= np.clip(_lowerCAmelCase       ,    0       ,    1					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						torch.FloatTensor       ,    lowercase_       :						int					):
										lowercase_      :    str       		= in_sigmas[-1].item()
										lowercase_      :    str       		= in_sigmas[0].item()

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







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										return self.dt is None







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						Union[torch.FloatTensor, np.ndarray]       ,    lowercase_       :						Union[float, torch.FloatTensor]       ,    lowercase_       :						Union[torch.FloatTensor, np.ndarray]       ,    lowercase_       :						bool = True       ,    ):
										lowercase_      :    Any       		= self.index_for_timestep(_lowerCAmelCase					)

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

										if self.state_in_first_order:
																	lowercase_      :    str       		= self.sigmas[step_index]
																	lowercase_      :    List[str]       		= self.sigmas[step_index + 1]
										else:
																	# 2nd order / Heun's method
																	lowercase_      :    Any       		= self.sigmas[step_index - 1]
																	lowercase_      :    List[str]       		= 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_      :    Dict       		= 0
										lowercase_      :    Tuple       		= 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_      :    List[Any]       		= sigma_hat if self.state_in_first_order else sigma_next
																	lowercase_      :    int       		= sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
																	lowercase_      :    int       		= sigma_hat if self.state_in_first_order else sigma_next
																	lowercase_      :    str       		= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																	    sample / (sigma_input**2 + 1)
																	)
										elif self.config.prediction_type == "sample":
																	lowercase_      :    List[Any]       		= model_output
										else:
																	raise ValueError(
																	    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

										if self.config.clip_sample:
																	lowercase_      :    List[Any]       		= pred_original_sample.clamp(
																	    -self.config.clip_sample_range       ,    self.config.clip_sample_range					)

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

																	# store for 2nd order step
																	lowercase_      :    Optional[Any]       		= derivative
																	lowercase_      :    Dict       		= dt
																	lowercase_      :    int       		= sample
										else:
																	# 2. 2nd order / Heun's method
																	lowercase_      :    Dict       		= (sample - pred_original_sample) / sigma_next
																	lowercase_      :    Union[str, Any]       		= (self.prev_derivative + derivative) / 2

																	# 3. take prev timestep & sample
																	lowercase_      :    List[Any]       		= self.dt
																	lowercase_      :    Union[str, Any]       		= self.sample

																	# free dt and derivative
																	# Note, this puts the scheduler in "first order mode"
																	lowercase_      :    int       		= None
																	lowercase_      :    Optional[Any]       		= None
																	lowercase_      :    Union[str, Any]       		= None

										lowercase_      :    Any       		= sample + derivative * dt

										if not return_dict:
																	return (prev_sample,)

										return SchedulerOutput(prev_sample=_lowerCAmelCase					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						torch.FloatTensor       ,    lowercase_       :						torch.FloatTensor       ,    lowercase_       :						torch.FloatTensor       ,    ):
										lowercase_      :    str       		= 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_      :    Optional[int]       		= self.timesteps.to(original_samples.device       ,    dtype=torch.floataa					)
																	lowercase_      :    Tuple       		= timesteps.to(original_samples.device       ,    dtype=torch.floataa					)
										else:
																	lowercase_      :    List[str]       		= self.timesteps.to(original_samples.device					)
																	lowercase_      :    int       		= timesteps.to(original_samples.device					)

										lowercase_      :    Tuple       		= [self.index_for_timestep(_lowerCAmelCase       ,    _lowerCAmelCase					) for t in timesteps]

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

										lowercase_      :    str       		= original_samples + noise * sigma
										return noisy_samples







			def __len__(			self       :						Tuple					):
										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 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   Any:
							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_      :    List[Any]       		= False
							lowercase_      :    Any       		= is_small_dataset(UpperCAmelCase__				)
							assert result == expected





