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 					_snake_case	(  __snake_case  ,     __snake_case=0.999  ,     __snake_case="cosine"  ,     ):
					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

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

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







class     lowerCAmelCase_     (       __lowercase,  __lowercase ):
							UpperCAmelCase				   =     [e.name for e in KarrasDiffusionSchedulers]
							UpperCAmelCase				   =     2







							@register_to_config
							def __init__(   self      :     Optional[Any] ,			_A      :     int = 1000 ,			_A      :     float = 0.0_0085 ,			_A      :     float = 0.012 ,			_A      :     str = "linear" ,			_A      :     Optional[Union[np.ndarray, List[float]]] = None ,			_A      :     str = "epsilon" ,			_A      :     str = "linspace" ,			_A      :     int = 0 ,			):
												if trained_betas is not None:
																	_UpperCamelCase			     =     torch.tensor(_A ,			dtype=torch.floataa		)
												elif beta_schedule == "linear":
																	_UpperCamelCase			     =     torch.linspace(_A ,			_A ,			_A ,			dtype=torch.floataa		)
												elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	_UpperCamelCase			     =     (
																	    torch.linspace(beta_start**0.5 ,			beta_end**0.5 ,			_A ,			dtype=torch.floataa		) ** 2
																	)
												elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	_UpperCamelCase			     =     betas_for_alpha_bar(_A		)
												else:
																	raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""		)

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

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







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Dict ,			_A      :     List[str]=None		):
												if schedule_timesteps is None:
																	_UpperCamelCase			     =     self.timesteps

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

												return indices[pos].item()







							@property
							def  UpperCamelCase_						(   self      :     str		):
												# 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  UpperCamelCase_						(   self      :     str ,			_A      :     torch.FloatTensor ,			_A      :     Union[float, torch.FloatTensor] ,			):
												_UpperCamelCase			     =     self.index_for_timestep(_A		)

												if self.state_in_first_order:
																	_UpperCamelCase			     =     self.sigmas[step_index]
												else:
																	_UpperCamelCase			     =     self.sigmas_interpol[step_index]

												_UpperCamelCase			     =     sample / ((sigma**2 + 1) ** 0.5)
												return sample







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     int ,			_A      :     Union[str, torch.device] = None ,			_A      :     Optional[int] = None ,			):
												_UpperCamelCase			     =     num_inference_steps

												_UpperCamelCase			     =     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":
																	_UpperCamelCase			     =     np.linspace(0 ,			num_train_timesteps - 1 ,			_A ,			dtype=_A		)[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																	_UpperCamelCase			     =     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
																	_UpperCamelCase			     =     (np.arange(0 ,			_A		) * step_ratio).round()[::-1].copy().astype(_A		)
																	timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																	_UpperCamelCase			     =     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
																	_UpperCamelCase			     =     (np.arange(_A ,			0 ,			-step_ratio		)).round().copy().astype(_A		)
																	timesteps -= 1
												else:
																	raise ValueError(
																	    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""		)

												_UpperCamelCase			     =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
												_UpperCamelCase			     =     torch.from_numpy(np.log(_A		)		).to(_A		)

												_UpperCamelCase			     =     np.interp(_A ,			np.arange(0 ,			len(_A		)		) ,			_A		)
												_UpperCamelCase			     =     np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
												_UpperCamelCase			     =     torch.from_numpy(_A		).to(device=_A		)

												# interpolate sigmas
												_UpperCamelCase			     =     sigmas.log().lerp(sigmas.roll(1		).log() ,			0.5		).exp()

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

												if str(_A		).startswith('''mps'''		):
																	# mps does not support float64
																	_UpperCamelCase			     =     torch.from_numpy(_A		).to(_A ,			dtype=torch.floataa		)
												else:
																	_UpperCamelCase			     =     torch.from_numpy(_A		).to(_A		)

												# interpolate timesteps
												_UpperCamelCase			     =     self.sigma_to_t(_A		).to(_A ,			dtype=timesteps.dtype		)
												_UpperCamelCase			     =     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,			dim=-1		).flatten()

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

												_UpperCamelCase			     =     None

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







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     List[Any]		):
												# get log sigma
												_UpperCamelCase			     =     sigma.log()

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

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

												_UpperCamelCase			     =     self.log_sigmas[low_idx]
												_UpperCamelCase			     =     self.log_sigmas[high_idx]

												# interpolate sigmas
												_UpperCamelCase			     =     (low - log_sigma) / (low - high)
												_UpperCamelCase			     =     w.clamp(0 ,			1		)

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







							@property
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return self.sample is None







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     Union[torch.FloatTensor, np.ndarray] ,			_A      :     Union[float, torch.FloatTensor] ,			_A      :     Union[torch.FloatTensor, np.ndarray] ,			_A      :     bool = True ,			):
												_UpperCamelCase			     =     self.index_for_timestep(_A		)

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

												if self.state_in_first_order:
																	_UpperCamelCase			     =     self.sigmas[step_index]
																	_UpperCamelCase			     =     self.sigmas_interpol[step_index + 1]
																	_UpperCamelCase			     =     self.sigmas[step_index + 1]
												else:
																	# 2nd order / KDPM2's method
																	_UpperCamelCase			     =     self.sigmas[step_index - 1]
																	_UpperCamelCase			     =     self.sigmas_interpol[step_index]
																	_UpperCamelCase			     =     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
												_UpperCamelCase			     =     0
												_UpperCamelCase			     =     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":
																	_UpperCamelCase			     =     sigma_hat if self.state_in_first_order else sigma_interpol
																	_UpperCamelCase			     =     sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																	_UpperCamelCase			     =     sigma_hat if self.state_in_first_order else sigma_interpol
																	_UpperCamelCase			     =     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
																	_UpperCamelCase			     =     (sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	_UpperCamelCase			     =     sigma_interpol - sigma_hat

																	# store for 2nd order step
																	_UpperCamelCase			     =     sample
												else:
																	# DPM-Solver-2
																	# 2. Convert to an ODE derivative for 2nd order
																	_UpperCamelCase			     =     (sample - pred_original_sample) / sigma_interpol

																	# 3. delta timestep
																	_UpperCamelCase			     =     sigma_next - sigma_hat

																	_UpperCamelCase			     =     self.sample
																	_UpperCamelCase			     =     None

												_UpperCamelCase			     =     sample + derivative * dt

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=_A		)







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     torch.FloatTensor ,			_A      :     torch.FloatTensor ,			_A      :     torch.FloatTensor ,			):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												_UpperCamelCase			     =     self.sigmas.to(device=original_samples.device ,			dtype=original_samples.dtype		)
												if original_samples.device.type == "mps" and torch.is_floating_point(_A		):
																	# mps does not support float64
																	_UpperCamelCase			     =     self.timesteps.to(original_samples.device ,			dtype=torch.floataa		)
																	_UpperCamelCase			     =     timesteps.to(original_samples.device ,			dtype=torch.floataa		)
												else:
																	_UpperCamelCase			     =     self.timesteps.to(original_samples.device		)
																	_UpperCamelCase			     =     timesteps.to(original_samples.device		)

												_UpperCamelCase			     =     [self.index_for_timestep(_A ,			_A		) for t in timesteps]

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

												_UpperCamelCase			     =     original_samples + noise * sigma
												return noisy_samples







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

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

					try:
										if dataset.shape[1] != value_array.shape[1]:
															_UpperCamelCase			     =     (
															    '''Wrong input data\'s shape... '''
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(__snake_case						)
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError('''Wrong shape'''						)

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

					return answer
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return np.dot(__snake_case  ,     __snake_case						) / (norm(__snake_case						) * norm(__snake_case						))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
