


"""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   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=0.999							,    SCREAMING_SNAKE_CASE="cosine"							,    )  ->     List[Any]:







							"""simple docstring"""


							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

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

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

							__snake_case     			=       []
							for i in range(lowerCamelCase_ ):
														__snake_case     			=       i / num_diffusion_timesteps
														__snake_case     			=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(lowerCamelCase_ ) / alpha_bar_fn(lowerCamelCase_ )							,    lowerCamelCase_ ) )
							return torch.tensor(lowerCamelCase_							,    dtype=torch.floataa )







class 							__magic_name__     (		lowerCAmelCase__      ,		lowerCAmelCase__   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   [e.name for e in KarrasDiffusionSchedulers]
					_SCREAMING_SNAKE_CASE       :					Dict     							=   2





					@register_to_config
					def __init__(				self   :							Tuple							,						snake_case_   :							List[str] = 1000							,						snake_case_   :							Tuple = 0.00085							,						snake_case_   :							int = 0.012							,						snake_case_   :							Union[str, Any] = "linear"							,						snake_case_   :							Union[str, Any] = None							,						snake_case_   :							Optional[int] = "epsilon"							,						snake_case_   :							Tuple = False							,						snake_case_   :							List[str] = False							,						snake_case_   :							Dict = 1.0							,						snake_case_   :							List[str] = "linspace"							,						snake_case_   :							Optional[Any] = 0							,						):





												if trained_betas is not None:
																			__snake_case     			=       torch.tensor(_lowerCamelCase							,						dtype=torch.floataa    )
												elif beta_schedule == "linear":
																			__snake_case     			=       torch.linspace(_lowerCamelCase							,						_lowerCamelCase							,						_lowerCamelCase							,						dtype=torch.floataa    )
												elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			__snake_case     			=       (
																			    torch.linspace(beta_start**0.5							,						beta_end**0.5							,						_lowerCamelCase							,						dtype=torch.floataa    ) ** 2
																			)
												elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			__snake_case     			=       betas_for_alpha_bar(_lowerCamelCase							,						alpha_transform_type="cosine"    )
												elif beta_schedule == "exp":
																			__snake_case     			=       betas_for_alpha_bar(_lowerCamelCase							,						alpha_transform_type="exp"    )
												else:
																			raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

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

												#  set all values
												self.set_timesteps(_lowerCamelCase							,						_lowerCamelCase							,						_lowerCamelCase    )
												__snake_case     			=       use_karras_sigmas





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							List[str]=None    ):





												if schedule_timesteps is None:
																			__snake_case     			=       self.timesteps

												__snake_case     			=       (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:
																			__snake_case     			=       1 if len(_lowerCamelCase    ) > 1 else 0
												else:
																			__snake_case     			=       timestep.cpu().item() if torch.is_tensor(_lowerCamelCase    ) else timestep
																			__snake_case     			=       self._index_counter[timestep_int]

												return indices[pos].item()





					@property
					def 							lowerCAmelCase      (				self   :							Dict    ):





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

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Any							,						):
												__snake_case     			=       self.index_for_timestep(_lowerCamelCase    )

												__snake_case     			=       self.sigmas[step_index]
												__snake_case     			=       sample / ((sigma**2 + 1) ** 0.5)
												return sample





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Tuple							,						snake_case_   :							int = None							,						snake_case_   :							Optional[int] = None							,						):
												__snake_case     			=       num_inference_steps

												__snake_case     			=       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":
																			__snake_case     			=       np.linspace(0							,						num_train_timesteps - 1							,						_lowerCamelCase							,						dtype=_lowerCamelCase    )[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																			__snake_case     			=       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
																			__snake_case     			=       (np.arange(0							,						_lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase    )
																			timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																			__snake_case     			=       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
																			__snake_case     			=       (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\'.'''    )

												__snake_case     			=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
												__snake_case     			=       np.log(_lowerCamelCase    )
												__snake_case     			=       np.interp(_lowerCamelCase							,						np.arange(0							,						len(_lowerCamelCase    )    )							,						_lowerCamelCase    )

												if self.config.use_karras_sigmas:
																			__snake_case     			=       self._convert_to_karras(in_sigmas=_lowerCamelCase							,						num_inference_steps=self.num_inference_steps    )
																			__snake_case     			=       np.array([self._sigma_to_t(_lowerCamelCase							,						_lowerCamelCase    ) for sigma in sigmas]    )

												__snake_case     			=       np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
												__snake_case     			=       torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )
												__snake_case     			=       torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

												__snake_case     			=       torch.from_numpy(_lowerCamelCase    )
												__snake_case     			=       torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

												if str(_lowerCamelCase    ).startswith("mps"    ):
																			# mps does not support float64
																			__snake_case     			=       timesteps.to(_lowerCamelCase							,						dtype=torch.floataa    )
												else:
																			__snake_case     			=       timesteps.to(device=_lowerCamelCase    )

												# empty dt and derivative
												__snake_case     			=       None
												__snake_case     			=       None

												# for exp beta schedules, such as the one for `pipeline_shap_e.py`
												# we need an index counter
												__snake_case     			=       defaultdict(_lowerCamelCase    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							List[str]							,						snake_case_   :							Optional[int]    ):
												__snake_case     			=       np.log(_lowerCamelCase    )

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

												# get sigmas range
												__snake_case     			=       np.cumsum((dists >= 0)							,						axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
												__snake_case     			=       low_idx + 1

												__snake_case     			=       log_sigmas[low_idx]
												__snake_case     			=       log_sigmas[high_idx]

												# interpolate sigmas
												__snake_case     			=       (low - log_sigma) / (low - high)
												__snake_case     			=       np.clip(_lowerCamelCase							,						0							,						1    )

												# transform interpolation to time range
												__snake_case     			=       (1 - w) * low_idx + w * high_idx
												__snake_case     			=       t.reshape(sigma.shape    )
												return t





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       in_sigmas[-1].item()
												__snake_case     			=       in_sigmas[0].item()

												__snake_case     			=       7.0  # 7.0 is the value used in the paper
												__snake_case     			=       np.linspace(0							,						1							,						_lowerCamelCase    )
												__snake_case     			=       sigma_min ** (1 / rho)
												__snake_case     			=       sigma_max ** (1 / rho)
												__snake_case     			=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas





					@property
					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												return self.dt is None





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							int							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Tuple							,						snake_case_   :							Any = True							,						):
												__snake_case     			=       self.index_for_timestep(_lowerCamelCase    )

												# advance index counter by 1
												__snake_case     			=       timestep.cpu().item() if torch.is_tensor(_lowerCamelCase    ) else timestep
												self._index_counter[timestep_int] += 1

												if self.state_in_first_order:
																			__snake_case     			=       self.sigmas[step_index]
																			__snake_case     			=       self.sigmas[step_index + 1]
												else:
																			# 2nd order / Heun's method
																			__snake_case     			=       self.sigmas[step_index - 1]
																			__snake_case     			=       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
												__snake_case     			=       0
												__snake_case     			=       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":
																			__snake_case     			=       sigma_hat if self.state_in_first_order else sigma_next
																			__snake_case     			=       sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																			__snake_case     			=       sigma_hat if self.state_in_first_order else sigma_next
																			__snake_case     			=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																			    sample / (sigma_input**2 + 1)
																			)
												elif self.config.prediction_type == "sample":
																			__snake_case     			=       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:
																			__snake_case     			=       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
																			__snake_case     			=       (sample - pred_original_sample) / sigma_hat
																			# 3. delta timestep
																			__snake_case     			=       sigma_next - sigma_hat

																			# store for 2nd order step
																			__snake_case     			=       derivative
																			__snake_case     			=       dt
																			__snake_case     			=       sample
												else:
																			# 2. 2nd order / Heun's method
																			__snake_case     			=       (sample - pred_original_sample) / sigma_next
																			__snake_case     			=       (self.prev_derivative + derivative) / 2

																			# 3. take prev timestep & sample
																			__snake_case     			=       self.dt
																			__snake_case     			=       self.sample

																			# free dt and derivative
																			# Note, this puts the scheduler in "first order mode"
																			__snake_case     			=       None
																			__snake_case     			=       None
																			__snake_case     			=       None

												__snake_case     			=       sample + derivative * dt

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=_lowerCamelCase    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]							,						snake_case_   :							Optional[int]							,						):
												__snake_case     			=       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
																			__snake_case     			=       self.timesteps.to(original_samples.device							,						dtype=torch.floataa    )
																			__snake_case     			=       timesteps.to(original_samples.device							,						dtype=torch.floataa    )
												else:
																			__snake_case     			=       self.timesteps.to(original_samples.device    )
																			__snake_case     			=       timesteps.to(original_samples.device    )

												__snake_case     			=       [self.index_for_timestep(_lowerCamelCase							,						_lowerCamelCase    ) for t in timesteps]

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

												__snake_case     			=       original_samples + noise * sigma
												return noisy_samples





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


"""simple docstring"""







from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					float
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bool:







							"""simple docstring"""


							def is_valid_tree(SCREAMING_SNAKE_CASE ) -> bool:
														if node is None:
																					return True

														if not isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					return False

														try:
																					float(node.data )
														except (TypeError, ValueError):
																					return False

														return is_valid_tree(node.left ) and is_valid_tree(node.right )

							if not is_valid_tree(SCREAMING_SNAKE_CASE ):
														raise ValueError(
														    "Each node should be type of TreeNode and data should be float." )

							def is_binary_search_tree_recursive_check(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) -> bool:

														if node is None:
																					return True

														return (
														    left_bound < node.data < right_bound
														    and is_binary_search_tree_recursive_check(node.left							,    SCREAMING_SNAKE_CASE							,    node.data )
														    and is_binary_search_tree_recursive_check(
														        node.right							,    node.data							,    SCREAMING_SNAKE_CASE )
														)

							return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE							,    -float("inf" )							,    float("inf" ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
