



"""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__			(     lowerCamelCase__				,      lowerCamelCase__=0.9_99				,      lowerCamelCase__="cosine"				,      ) ->							Dict:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__			):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

							elif alpha_transform_type == "exp":

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

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

							A         =					[]
							for i in range(lowerCamelCase__			):
														A         =					i / num_diffusion_timesteps
														A         =					(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      UpperCAmelCase__		(      UpperCamelCase   ,UpperCamelCase     ):
		lowerCAmelCase_       :	List[str] 							=     [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_       :	Optional[Any] 							=     2



		@register_to_config
		def __init__( self  :    Tuple      ,							snake_case  :    int = 1_000      ,							snake_case  :    float = 0.00085      ,							snake_case  :    float = 0.012      ,							snake_case  :    str = "linear"      ,							snake_case  :    Optional[Union[np.ndarray, List[float]]] = None      ,							snake_case  :    str = "epsilon"      ,							snake_case  :    Optional[bool] = False      ,							snake_case  :    Optional[bool] = False      ,							snake_case  :    float = 1.0      ,							snake_case  :    str = "linspace"      ,							snake_case  :    int = 0      ,							)			->						Optional[int]:






									'''simple docstring'''

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

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

									#  set all values
									self.set_timesteps(snake_case      ,							snake_case      ,							snake_case				)
									A         =					use_karras_sigmas



		def 					A_		( self  :    List[str]      ,							snake_case  :    str      ,							snake_case  :    Union[str, Any]=None				)			->						Optional[Any]:






									'''simple docstring'''

									if schedule_timesteps is None:
																A         =					self.timesteps

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

									return indices[pos].item()



		@property
		def 					A_		( self  :    Dict				)			->						Dict:






									'''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  :    Optional[Any]      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    Union[float, torch.FloatTensor]      ,							)			->						torch.FloatTensor:






									'''simple docstring'''

									A         =					self.index_for_timestep(snake_case				)

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



		def 					A_		( self  :    Dict      ,							snake_case  :    int      ,							snake_case  :    Union[str, torch.device] = None      ,							snake_case  :    Optional[int] = None      ,							)			->						Optional[Any]:






									'''simple docstring'''

									A         =					num_inference_steps

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

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

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

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

									A         =					torch.from_numpy(snake_case				)
									A         =					torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2				)]				)

									if str(snake_case				).startswith('mps'				):
																# mps does not support float64
																A         =					timesteps.to(snake_case      ,							dtype=torch.floataa				)
									else:
																A         =					timesteps.to(device=snake_case				)

									# empty dt and derivative
									A         =					None
									A         =					None

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



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Any				)			->						List[Any]:






									'''simple docstring'''

									A         =					np.log(snake_case				)

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

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

									A         =					log_sigmas[low_idx]
									A         =					log_sigmas[high_idx]

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

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



		def 					A_		( self  :    List[Any]      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    Tuple				)			->						torch.FloatTensor:






									'''simple docstring'''

									A         =					in_sigmas[-1].item()
									A         =					in_sigmas[0].item()

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



		@property
		def 					A_		( self  :    Optional[Any]				)			->						Tuple:






									'''simple docstring'''

									return self.dt is None



		def 					A_		( self  :    List[Any]      ,							snake_case  :    Union[torch.FloatTensor, np.ndarray]      ,							snake_case  :    Union[float, torch.FloatTensor]      ,							snake_case  :    Union[torch.FloatTensor, np.ndarray]      ,							snake_case  :    bool = True      ,							)			->						Union[SchedulerOutput, Tuple]:






									'''simple docstring'''

									A         =					self.index_for_timestep(snake_case				)

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

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

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

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

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

									A         =					sample + derivative * dt

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=snake_case				)



		def 					A_		( self  :    Tuple      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    torch.FloatTensor      ,							)			->						torch.FloatTensor:






									'''simple docstring'''

									A         =					self.sigmas.to(device=original_samples.device      ,							dtype=original_samples.dtype				)
									if original_samples.device.type == "mps" and torch.is_floating_point(snake_case				):
																# mps does not support float64
																A         =					self.timesteps.to(original_samples.device      ,							dtype=torch.floataa				)
																A         =					timesteps.to(original_samples.device      ,							dtype=torch.floataa				)
									else:
																A         =					self.timesteps.to(original_samples.device				)
																A         =					timesteps.to(original_samples.device				)

									A         =					[self.index_for_timestep(snake_case      ,							snake_case				) for t in timesteps]

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

									A         =					original_samples + noise * sigma
									return noisy_samples



		def __len__( self  :    str				)			->						List[str]:






									'''simple docstring'''

									return self.config.num_train_timesteps








"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							float:

							def get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			) -> str:
														A         =					[]
														A         =					min(len(_stra			)				,      len(_stra			)			) // 2
														for i, l in enumerate(_stra			):
																					A         =					int(max(0				,      i - limit			)			)
																					A         =					int(min(i + limit + 1				,      len(_stra			)			)			)
																					if l in _stra[left:right]:
																												matched.append(lowerCamelCase__			)
																												A         =					f"""{_stra[0:_stra.index(lowerCamelCase__			)]} {_stra[_stra.index(lowerCamelCase__			) + 1:]}"""

														return "".join(lowerCamelCase__			)

							# matching characters
							A         =					get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					len(lowerCamelCase__			)

							# transposition
							A         =					(
							    len([(ca, ca) for ca, ca in zip(lowerCamelCase__				,      lowerCamelCase__			) if ca != ca]			) // 2
							)

							if not match_count:
														A         =					0.0
							else:
														A         =					(
														    1
														    / 3
														    * (
														        match_count / len(lowerCamelCase__			)
														        + match_count / len(lowerCamelCase__			)
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							A         =					0
							for ca, ca in zip(stra[:4]				,      stra[:4]			):
														if ca == ca:
																					prefix_len += 1
														else:
																					break

							return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(jaro_winkler('hello', 'world'))



