



"""simple docstring"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCAmelCase       =     logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class    a		(					__lowerCAmelCase    ):
  UpperCamelCase		:			torch.FloatTensor
  UpperCamelCase		:			Optional[torch.FloatTensor]         =   None







def        __magic_name__			(							lowercase							,     lowercase=0.999							,     lowercase="cosine"							,     ):





   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   SCREAMING_SNAKE_CASE_:	List[str]            =[]
   for i in range(_lowerCamelCase	):
      SCREAMING_SNAKE_CASE_:	Optional[int]            =i / num_diffusion_timesteps
      SCREAMING_SNAKE_CASE_:	int            =(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    a		(					__lowerCAmelCase		, __lowerCAmelCase    ):
  UpperCamelCase		:			Any         =   [e.name for e in KarrasDiffusionSchedulers]
  UpperCamelCase		:			int         =   1



  @register_to_config
  def __init__(  self	:   Optional[int]    ,						lowerCAmelCase	:   str = 1000    ,						lowerCAmelCase	:   Optional[Any] = 0.0_0_0_1    ,						lowerCAmelCase	:   int = 0.0_2    ,						lowerCAmelCase	:   int = "linear"    ,						lowerCAmelCase	:   str = None    ,						lowerCAmelCase	:   List[Any] = "epsilon"    ,						lowerCAmelCase	:   List[str] = "linspace"    ,						lowerCAmelCase	:   int = 0    ,						)       ->   Dict:


     '''simple docstring'''




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

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

     SCREAMING_SNAKE_CASE_:	List[str]            =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
     SCREAMING_SNAKE_CASE_:	Any            =np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =torch.from_numpy(__SCREAMING_SNAKE_CASE						)

     # setable values
     SCREAMING_SNAKE_CASE_:	int            =None
     SCREAMING_SNAKE_CASE_:	str            =np.linspace(0    ,						num_train_timesteps - 1    ,						__SCREAMING_SNAKE_CASE    ,						dtype=__SCREAMING_SNAKE_CASE						)[::-1].copy()
     SCREAMING_SNAKE_CASE_:	str            =torch.from_numpy(__SCREAMING_SNAKE_CASE						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =False



  @property
  def 		lowerCamelCase__    (  self	:   str						)       ->   Tuple:


     '''simple docstring'''




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

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



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   int						)       ->   torch.FloatTensor:


     '''simple docstring'''




     if isinstance(__SCREAMING_SNAKE_CASE    ,						torch.Tensor						):
        SCREAMING_SNAKE_CASE_:	Optional[int]            =timestep.to(self.timesteps.device						)
     SCREAMING_SNAKE_CASE_:	List[str]            =(self.timesteps == timestep).nonzero().item()
     SCREAMING_SNAKE_CASE_:	Any            =self.sigmas[step_index]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =sample / ((sigma**2 + 1) ** 0.5)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =True
     return sample



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   Any    ,						lowerCAmelCase	:   Any = None						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =num_inference_steps

     # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
     if self.config.timestep_spacing == "linspace":
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =np.linspace(0    ,						self.config.num_train_timesteps - 1    ,						__SCREAMING_SNAKE_CASE    ,						dtype=__SCREAMING_SNAKE_CASE						)[
            ::-1
        ].copy()
     elif self.config.timestep_spacing == "leading":
        SCREAMING_SNAKE_CASE_:	Any            =self.config.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
        SCREAMING_SNAKE_CASE_:	int            =(np.arange(0    ,						__SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE						)
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        SCREAMING_SNAKE_CASE_:	List[Any]            =self.config.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
        SCREAMING_SNAKE_CASE_:	List[Any]            =(np.arange(self.config.num_train_timesteps    ,						0    ,						-step_ratio						)).round().copy().astype(__SCREAMING_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\'.'''						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =np.interp(__SCREAMING_SNAKE_CASE    ,						np.arange(0    ,						len(__SCREAMING_SNAKE_CASE						)						)    ,						__SCREAMING_SNAKE_CASE						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(device=__SCREAMING_SNAKE_CASE						)
     if str(__SCREAMING_SNAKE_CASE						).startswith("""mps"""						):
        # mps does not support float64
        SCREAMING_SNAKE_CASE_:	Any            =torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(__SCREAMING_SNAKE_CASE    ,						dtype=torch.floataa						)
     else:
        SCREAMING_SNAKE_CASE_:	List[Any]            =torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(device=__SCREAMING_SNAKE_CASE						)



  def 		lowerCamelCase__    (  self	:   int    ,						lowerCAmelCase	:   List[Any]    ,						lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   Dict = None    ,						lowerCAmelCase	:   Union[str, Any] = True    ,						)       ->   Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:


     '''simple docstring'''




     if (
         isinstance(__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE						)
         or isinstance(__SCREAMING_SNAKE_CASE    ,						torch.IntTensor						)
         or isinstance(__SCREAMING_SNAKE_CASE    ,						torch.LongTensor						)
     ):
        raise ValueError(
            (
                """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                """ one of the `scheduler.timesteps` as a timestep."""
            )    ,						)

     if not self.is_scale_input_called:
        logger.warning(
            """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
            """See `StableDiffusionPipeline` for a usage example."""						)

     if isinstance(__SCREAMING_SNAKE_CASE    ,						torch.Tensor						):
        SCREAMING_SNAKE_CASE_:	int            =timestep.to(self.timesteps.device						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =(self.timesteps == timestep).nonzero().item()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.sigmas[step_index]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        SCREAMING_SNAKE_CASE_:	List[str]            =sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'''						)

     SCREAMING_SNAKE_CASE_:	int            =self.sigmas[step_index]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.sigmas[step_index + 1]
     SCREAMING_SNAKE_CASE_:	str            =(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
     SCREAMING_SNAKE_CASE_:	Dict            =(sigma_to**2 - sigma_up**2) ** 0.5

     # 2. Convert to an ODE derivative
     SCREAMING_SNAKE_CASE_:	List[Any]            =(sample - pred_original_sample) / sigma

     SCREAMING_SNAKE_CASE_:	int            =sigma_down - sigma

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =sample + derivative * dt

     SCREAMING_SNAKE_CASE_:	List[Any]            =model_output.device
     SCREAMING_SNAKE_CASE_:	Optional[int]            =randn_tensor(model_output.shape    ,						dtype=model_output.dtype    ,						device=__SCREAMING_SNAKE_CASE    ,						generator=__SCREAMING_SNAKE_CASE						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =prev_sample + noise * sigma_up

     if not return_dict:
        return (prev_sample,)

     return EulerAncestralDiscreteSchedulerOutput(
         prev_sample=__SCREAMING_SNAKE_CASE    ,						pred_original_sample=__SCREAMING_SNAKE_CASE						)



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   Optional[Any]    ,						lowerCAmelCase	:   List[str]    ,						)       ->   torch.FloatTensor:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =self.sigmas.to(device=original_samples.device    ,						dtype=original_samples.dtype						)
     if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE						):
        # mps does not support float64
        SCREAMING_SNAKE_CASE_:	str            =self.timesteps.to(original_samples.device    ,						dtype=torch.floataa						)
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =timesteps.to(original_samples.device    ,						dtype=torch.floataa						)
     else:
        SCREAMING_SNAKE_CASE_:	Dict            =self.timesteps.to(original_samples.device						)
        SCREAMING_SNAKE_CASE_:	List[str]            =timesteps.to(original_samples.device						)

     SCREAMING_SNAKE_CASE_:	int            =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

     SCREAMING_SNAKE_CASE_:	int            =original_samples + noise * sigma
     return noisy_samples





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


     '''simple docstring'''




     return self.config.num_train_timesteps








"""simple docstring"""
from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   List[str]						)       ->   None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =vocab
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =normalizers.BertNormalizer(
         clean_text=lowerCAmelCase    ,						handle_chinese_chars=lowerCAmelCase    ,						strip_accents=lowerCAmelCase    ,						lowercase=lowerCAmelCase    ,						)
     try:
        import rjieba
     except ImportError:
        raise ImportError(
            """You need to install rjieba to use RoFormerTokenizer. """
            """See https://pypi.org/project/rjieba/ for installation."""						)
     SCREAMING_SNAKE_CASE_:	Dict            =rjieba



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   NormalizedString						)       ->   List[NormalizedString]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =[]

     # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
     for token, start, end in self.jieba.tokenize(str(lowerCAmelCase						)    ,						hmm=lowerCAmelCase						):
        if token in self.vocab:
           splits.append(normalized_string[start:end]						)
        else:
           SCREAMING_SNAKE_CASE_:	List[Any]            =self.normalizers.normalize_str(lowerCAmelCase						).split()
           for token in token_list:
              if token:
                 SCREAMING_SNAKE_CASE_:	Dict            =start + len(lowerCAmelCase						)
                 splits.append(normalized_string[start:end]						)
                 SCREAMING_SNAKE_CASE_:	Optional[Any]            =end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

     return splits





  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   PreTokenizedString						)       ->   Tuple:


     '''simple docstring'''




     pretok.split(self.jieba_split						)



