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       A	(    _lowercase		,  _lowercase=0.999		,  _lowercase="cosine"		,  ):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowercase						):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_lowercase						):
																		return math.exp(t * -12.0						)

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

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





class 					lowercase__						(   UpperCamelCase_	,							UpperCamelCase_):
						UpperCamelCase_     =		[e.name for e in KarrasDiffusionSchedulers]
						UpperCamelCase_     =		2


						@register_to_config
						def __init__(       self	:				Dict    ,		UpperCamelCase__	:				int = 1000    ,		UpperCamelCase__	:				float = 0.0_0085    ,		UpperCamelCase__	:				float = 0.012    ,		UpperCamelCase__	:				str = "linear"    ,		UpperCamelCase__	:				Optional[Union[np.ndarray, List[float]]] = None    ,		UpperCamelCase__	:				str = "epsilon"    ,		UpperCamelCase__	:				str = "linspace"    ,		UpperCamelCase__	:				int = 0    ,		):





												'''simple docstring'''





												if trained_betas is not None:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							torch.tensor(UpperCamelCase__    ,		dtype=torch.floataa						)
												elif beta_schedule == "linear":
																		SCREAMING_SNAKE_CASE							:    int							=							torch.linspace(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		dtype=torch.floataa						)
												elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		SCREAMING_SNAKE_CASE							:    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
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							betas_for_alpha_bar(UpperCamelCase__						)
												else:
																		raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

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

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


						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Dict=None						):





												'''simple docstring'''





												if schedule_timesteps is None:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							self.timesteps

												SCREAMING_SNAKE_CASE							:    List[str]							=							(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:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							1 if len(UpperCamelCase__						) > 1 else 0
												else:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							timestep.cpu().item() if torch.is_tensor(UpperCamelCase__						) else timestep
																		SCREAMING_SNAKE_CASE							:    int							=							self._index_counter[timestep_int]

												return indices[pos].item()


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





												'''simple docstring'''





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

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


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				Union[float, torch.FloatTensor]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.index_for_timestep(UpperCamelCase__						)

												if self.state_in_first_order:
																		SCREAMING_SNAKE_CASE							:    Dict							=							self.sigmas[step_index]
												else:
																		SCREAMING_SNAKE_CASE							:    str							=							self.sigmas_interpol[step_index]

												SCREAMING_SNAKE_CASE							:    Dict							=							sample / ((sigma**2 + 1) ** 0.5)
												return sample


						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				Union[str, torch.device] = None    ,		UpperCamelCase__	:				Optional[int] = None    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							num_inference_steps

												SCREAMING_SNAKE_CASE							:    Dict							=							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":
																		SCREAMING_SNAKE_CASE							:    Tuple							=							np.linspace(0    ,		num_train_timesteps - 1    ,		UpperCamelCase__    ,		dtype=UpperCamelCase__						)[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																		SCREAMING_SNAKE_CASE							:    int							=							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
																		SCREAMING_SNAKE_CASE							:    Any							=							(np.arange(0    ,		UpperCamelCase__						) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__						)
																		timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																		SCREAMING_SNAKE_CASE							:    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
																		SCREAMING_SNAKE_CASE							:    int							=							(np.arange(UpperCamelCase__    ,		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'."""						)

												SCREAMING_SNAKE_CASE							:    int							=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
												SCREAMING_SNAKE_CASE							:    Any							=							torch.from_numpy(np.log(UpperCamelCase__						)						).to(UpperCamelCase__						)

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

												# interpolate sigmas
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							sigmas.log().lerp(sigmas.roll(1						).log()    ,		0.5						).exp()

												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.cat(
												    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)

												if str(UpperCamelCase__						).startswith('''mps'''						):
																		# mps does not support float64
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.from_numpy(UpperCamelCase__						).to(UpperCamelCase__    ,		dtype=torch.floataa						)
												else:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							torch.from_numpy(UpperCamelCase__						).to(UpperCamelCase__						)

												# interpolate timesteps
												SCREAMING_SNAKE_CASE							:    List[str]							=							self.sigma_to_t(UpperCamelCase__						).to(UpperCamelCase__    ,		dtype=timesteps.dtype						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])    ,		dim=-1						).flatten()

												SCREAMING_SNAKE_CASE							:    str							=							torch.cat([timesteps[:1], interleaved_timesteps]						)

												SCREAMING_SNAKE_CASE							:    int							=							None

												# for exp beta schedules, such as the one for `pipeline_shap_e.py`
												# we need an index counter
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							defaultdict(UpperCamelCase__						)


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							sigma.log()

												# get distribution
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							log_sigma - self.log_sigmas[:, None]

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.log_sigmas[low_idx]
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.log_sigmas[high_idx]

												# interpolate sigmas
												SCREAMING_SNAKE_CASE							:    str							=							(low - log_sigma) / (low - high)
												SCREAMING_SNAKE_CASE							:    Any							=							w.clamp(0    ,		1						)

												# transform interpolation to time range
												SCREAMING_SNAKE_CASE							:    str							=							(1 - w) * low_idx + w * high_idx
												SCREAMING_SNAKE_CASE							:    str							=							t.view(sigma.shape						)
												return t


						@property
						def        __A       (       self	:				Any						):





												'''simple docstring'''





												return self.sample is None


						def        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				Union[torch.FloatTensor, np.ndarray]    ,		UpperCamelCase__	:				Union[float, torch.FloatTensor]    ,		UpperCamelCase__	:				Union[torch.FloatTensor, np.ndarray]    ,		UpperCamelCase__	:				bool = True    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.index_for_timestep(UpperCamelCase__						)

												# advance index counter by 1
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							timestep.cpu().item() if torch.is_tensor(UpperCamelCase__						) else timestep
												self._index_counter[timestep_int] += 1

												if self.state_in_first_order:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							self.sigmas[step_index]
																		SCREAMING_SNAKE_CASE							:    List[str]							=							self.sigmas_interpol[step_index + 1]
																		SCREAMING_SNAKE_CASE							:    Tuple							=							self.sigmas[step_index + 1]
												else:
																		# 2nd order / KDPM2's method
																		SCREAMING_SNAKE_CASE							:    Tuple							=							self.sigmas[step_index - 1]
																		SCREAMING_SNAKE_CASE							:    Tuple							=							self.sigmas_interpol[step_index]
																		SCREAMING_SNAKE_CASE							:    Any							=							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
												SCREAMING_SNAKE_CASE							:    int							=							0
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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":
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							sigma_hat if self.state_in_first_order else sigma_interpol
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							sigma_hat if self.state_in_first_order else sigma_interpol
																		SCREAMING_SNAKE_CASE							:    Dict							=							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
																		SCREAMING_SNAKE_CASE							:    List[str]							=							(sample - pred_original_sample) / sigma_hat
																		# 3. delta timestep
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							sigma_interpol - sigma_hat

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

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

																		SCREAMING_SNAKE_CASE							:    str							=							self.sample
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							None

												SCREAMING_SNAKE_CASE							:    str							=							sample + derivative * dt

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase__						)


						def        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				torch.FloatTensor    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    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
																		SCREAMING_SNAKE_CASE							:    str							=							self.timesteps.to(original_samples.device    ,		dtype=torch.floataa						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							timesteps.to(original_samples.device    ,		dtype=torch.floataa						)
												else:
																		SCREAMING_SNAKE_CASE							:    str							=							self.timesteps.to(original_samples.device						)
																		SCREAMING_SNAKE_CASE							:    Any							=							timesteps.to(original_samples.device						)

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

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

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							original_samples + noise * sigma
												return noisy_samples



						def __len__(       self	:				Optional[Any]						):





												'''simple docstring'''





												return self.config.num_train_timesteps






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       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							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:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





