






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     (       a_							,a_=0.999							,a_="cosine"							,)     ->						List[str]:
					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

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

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




class 				__A     (			a      ,     a ):





				"""simple docstring"""







				UpperCamelCase__ :      Optional[Any]			 =[e.name for e in KarrasDiffusionSchedulers]
				UpperCamelCase__ :      Optional[int]			 =2


				@register_to_config
				def __init__( self							,  lowerCamelCase__ = 1000							,  lowerCamelCase__ = 0.00_085							,  lowerCamelCase__ = 0.012							,  lowerCamelCase__ = "linear"							,  lowerCamelCase__ = None							,  lowerCamelCase__ = "epsilon"							,  lowerCamelCase__ = False							,  lowerCamelCase__ = False							,  lowerCamelCase__ = 1.0							,  lowerCamelCase__ = "linspace"							,  lowerCamelCase__ = 0							,  ):




									"""simple docstring"""

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

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

									#  set all values
									self.set_timesteps(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    )
									__UpperCamelCase      :					str       		=use_karras_sigmas


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__=None    ):




									"""simple docstring"""

									if schedule_timesteps is None:
														__UpperCamelCase      :					Union[str, Any]       		=self.timesteps

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

									return indices[pos].item()


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

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

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


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=self.index_for_timestep(lowerCamelCase__    )

									__UpperCamelCase      :					List[str]       		=self.sigmas[step_index]
									__UpperCamelCase      :					Optional[Any]       		=sample / ((sigma**2 + 1) ** 0.5)
									return sample


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=num_inference_steps

									__UpperCamelCase      :					Union[str, Any]       		=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      :					Dict       		=np.linspace(0							,  num_train_timesteps - 1							,  lowerCamelCase__							,  dtype=lowerCamelCase__    )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
														__UpperCamelCase      :					List[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
														__UpperCamelCase      :					List[str]       		=(np.arange(0							,  lowerCamelCase__    ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__    )
														timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
														__UpperCamelCase      :					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
														__UpperCamelCase      :					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\'.'    )

									__UpperCamelCase      :					List[Any]       		=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
									__UpperCamelCase      :					int       		=np.log(lowerCamelCase__    )
									__UpperCamelCase      :					str       		=np.interp(lowerCamelCase__							,  np.arange(0							,  len(lowerCamelCase__    )    )							,  lowerCamelCase__    )

									if self.config.use_karras_sigmas:
														__UpperCamelCase      :					Optional[Any]       		=self._convert_to_karras(in_sigmas=lowerCamelCase__							,  num_inference_steps=self.num_inference_steps    )
														__UpperCamelCase      :					List[Any]       		=np.array([self._sigma_to_t(lowerCamelCase__							,  lowerCamelCase__    ) for sigma in sigmas]    )

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

									__UpperCamelCase      :					List[Any]       		=torch.from_numpy(lowerCamelCase__    )
									__UpperCamelCase      :					str       		=torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

									if str(lowerCamelCase__    ).startswith('mps'    ):
														# mps does not support float64
														__UpperCamelCase      :					Optional[int]       		=timesteps.to(lowerCamelCase__							,  dtype=torch.floataa    )
									else:
														__UpperCamelCase      :					List[Any]       		=timesteps.to(device=lowerCamelCase__    )

									# empty dt and derivative
									__UpperCamelCase      :					Dict       		=None
									__UpperCamelCase      :					Optional[Any]       		=None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									__UpperCamelCase      :					List[str]       		=defaultdict(lowerCamelCase__    )


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=np.log(lowerCamelCase__    )

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

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

									__UpperCamelCase      :					Optional[int]       		=log_sigmas[low_idx]
									__UpperCamelCase      :					Optional[int]       		=log_sigmas[high_idx]

									# interpolate sigmas
									__UpperCamelCase      :					Any       		=(low - log_sigma) / (low - high)
									__UpperCamelCase      :					int       		=np.clip(lowerCamelCase__							,  0							,  1    )

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


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					float       		=in_sigmas[-1].item()
									__UpperCamelCase      :					float       		=in_sigmas[0].item()

									__UpperCamelCase      :					Dict       		=7.0  # 7.0 is the value used in the paper
									__UpperCamelCase      :					str       		=np.linspace(0							,  1							,  lowerCamelCase__    )
									__UpperCamelCase      :					int       		=sigma_min ** (1 / rho)
									__UpperCamelCase      :					Tuple       		=sigma_max ** (1 / rho)
									__UpperCamelCase      :					Dict       		=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return self.dt is None


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = True							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=self.index_for_timestep(lowerCamelCase__    )

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

									if self.state_in_first_order:
														__UpperCamelCase      :					List[str]       		=self.sigmas[step_index]
														__UpperCamelCase      :					Tuple       		=self.sigmas[step_index + 1]
									else:
														# 2nd order / Heun's method
														__UpperCamelCase      :					Union[str, Any]       		=self.sigmas[step_index - 1]
														__UpperCamelCase      :					int       		=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      :					Any       		=0
									__UpperCamelCase      :					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":
														__UpperCamelCase      :					Optional[int]       		=sigma_hat if self.state_in_first_order else sigma_next
														__UpperCamelCase      :					Tuple       		=sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
														__UpperCamelCase      :					Dict       		=sigma_hat if self.state_in_first_order else sigma_next
														__UpperCamelCase      :					Union[str, Any]       		=model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
									elif self.config.prediction_type == "sample":
														__UpperCamelCase      :					Dict       		=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:
														__UpperCamelCase      :					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
														__UpperCamelCase      :					int       		=(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														__UpperCamelCase      :					List[str]       		=sigma_next - sigma_hat

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

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

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

									__UpperCamelCase      :					str       		=sample + derivative * dt

									if not return_dict:
														return (prev_sample,)

									return SchedulerOutput(prev_sample=lowerCamelCase__    )


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  ):




									"""simple docstring"""

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

									__UpperCamelCase      :					List[str]       		=[self.index_for_timestep(lowerCamelCase__							,  lowerCamelCase__    ) for t in timesteps]

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

									__UpperCamelCase      :					Tuple       		=original_samples + noise * sigma
									return noisy_samples







				def __len__( self    ):




									"""simple docstring"""

									return self.config.num_train_timesteps





'''simple docstring'''
import re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

if NLTK_AVAILABLE:
		with FileLock('''.lock''') as lock:
				nltk.download('''punkt''', quiet=True)


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__a   )   )