


"""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       a_ (	lowercase__ :Optional[Any],			lowercase__ :List[str]=0.999,			lowercase__ :Optional[int]="cosine",			):
			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(lowercase__ :Optional[Any]     ):
									return math.exp(t * -12.0     )

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

			__lowerCamelCase          =      []
			for i in range(lowercase__     ):
						__lowerCamelCase          =      i / num_diffusion_timesteps
						__lowerCamelCase          =      (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 			__snake_case   (lowerCamelCase			,		lowerCamelCase  ):
							__a					 =       [e.name for e in KarrasDiffusionSchedulers]
							__a					 =       2




							@register_to_config
							def __init__(     self:    Any     ,							A_:    int = 10_00     ,							A_:    float = 0.00_085     ,							A_:    float = 0.012     ,							A_:    str = "linear"     ,							A_:    Optional[Union[np.ndarray, List[float]]] = None     ,							A_:    str = "epsilon"     ,							A_:    Optional[bool] = False     ,							A_:    Optional[bool] = False     ,							A_:    float = 1.0     ,							A_:    str = "linspace"     ,							A_:    int = 0     ,							):
										if trained_betas is not None:
													__lowerCamelCase          =      torch.tensor(A_     ,							dtype=torch.floataa   )
										elif beta_schedule == "linear":
													__lowerCamelCase          =      torch.linspace(A_     ,							A_     ,							A_     ,							dtype=torch.floataa   )
										elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__lowerCamelCase          =      (
													    torch.linspace(beta_start**0.5     ,							beta_end**0.5     ,							A_     ,							dtype=torch.floataa   ) ** 2
													)
										elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__lowerCamelCase          =      betas_for_alpha_bar(A_     ,							alpha_transform_type="""cosine"""   )
										elif beta_schedule == "exp":
													__lowerCamelCase          =      betas_for_alpha_bar(A_     ,							alpha_transform_type="""exp"""   )
										else:
													raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'   )

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

										#  set all values
										self.set_timesteps(A_     ,							A_     ,							A_   )
										__lowerCamelCase          =      use_karras_sigmas




							def   __a   (     self:    Optional[Any]     ,							A_:    List[Any]     ,							A_:    Tuple=None   ):
										if schedule_timesteps is None:
													__lowerCamelCase          =      self.timesteps

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

										return indices[pos].item()




							@property
							def   __a   (     self:    Tuple   ):
										# 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   __a   (     self:    Union[str, Any]     ,							A_:    torch.FloatTensor     ,							A_:    Union[float, torch.FloatTensor]     ,							):
										__lowerCamelCase          =      self.index_for_timestep(A_   )

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




							def   __a   (     self:    str     ,							A_:    int     ,							A_:    Union[str, torch.device] = None     ,							A_:    Optional[int] = None     ,							):
										__lowerCamelCase          =      num_inference_steps

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

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

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

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

										__lowerCamelCase          =      torch.from_numpy(A_   )
										__lowerCamelCase          =      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2   )]   )

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

										# empty dt and derivative
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None

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




							def   __a   (     self:    Any     ,							A_:    int     ,							A_:    int   ):
										# get log sigma
										__lowerCamelCase          =      np.log(A_   )

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

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

										__lowerCamelCase          =      log_sigmas[low_idx]
										__lowerCamelCase          =      log_sigmas[high_idx]

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

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




							def   __a   (     self:    Dict     ,							A_:    torch.FloatTensor     ,							A_:    Tuple   ):
										__lowerCamelCase          =      in_sigmas[-1].item()
										__lowerCamelCase          =      in_sigmas[0].item()

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




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




							def   __a   (     self:    Union[str, Any]     ,							A_:    Union[torch.FloatTensor, np.ndarray]     ,							A_:    Union[float, torch.FloatTensor]     ,							A_:    Union[torch.FloatTensor, np.ndarray]     ,							A_:    bool = True     ,							):
										__lowerCamelCase          =      self.index_for_timestep(A_   )

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

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

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

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

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

										__lowerCamelCase          =      sample + derivative * dt

										if not return_dict:
													return (prev_sample,)

										return SchedulerOutput(prev_sample=A_   )




							def   __a   (     self:    str     ,							A_:    torch.FloatTensor     ,							A_:    torch.FloatTensor     ,							A_:    torch.FloatTensor     ,							):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										__lowerCamelCase          =      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
													__lowerCamelCase          =      self.timesteps.to(original_samples.device     ,							dtype=torch.floataa   )
													__lowerCamelCase          =      timesteps.to(original_samples.device     ,							dtype=torch.floataa   )
										else:
													__lowerCamelCase          =      self.timesteps.to(original_samples.device   )
													__lowerCamelCase          =      timesteps.to(original_samples.device   )

										__lowerCamelCase          =      [self.index_for_timestep(A_     ,							A_   ) for t in timesteps]

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

										__lowerCamelCase          =      original_samples + noise * sigma
										return noisy_samples




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








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__magic_name__						: Tuple    =				{}


try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: str    =				['MLukeTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mluke import MLukeTokenizer


else:
					import sys

					__magic_name__						: int    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




