

"""simple docstring"""





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : torch.FloatTensor
 __UpperCAmelCase     : Optional[torch.FloatTensor]       		=     None







def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	str=0.999,      UpperCamelCase_					:	str="cosine",      )     ->						Optional[Any]:
   '''simple docstring'''


   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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




class  _lowerCAmelCase  (     lowercase					,lowercase       ):







 """simple docstring"""







 @register_to_config
 def __init__(      self							:      Tuple, UpperCAmelCase__							:      int = 1_0_0_0, UpperCAmelCase__							:      str = "fixed_small_log", UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[float] = 1.0, UpperCAmelCase__							:      str = "epsilon", UpperCAmelCase__							:      str = "squaredcos_cap_v2", ):
    if beta_schedule != "squaredcos_cap_v2":
       raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"     )

    __lowercase								=						betas_for_alpha_bar(UpperCAmelCase__     )

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

    # standard deviation of the initial noise distribution
    __lowercase								=						1.0

    # setable values
    __lowercase								=						None
    __lowercase								=						torch.from_numpy(np.arange(0, UpperCAmelCase__     )[::-1].copy()     )

    __lowercase								=						variance_type


 def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      Optional[int] = None     ):
    return sample


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      int, UpperCAmelCase__							:      Union[str, torch.device] = None     ):
    __lowercase								=						num_inference_steps
    __lowercase								=						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
    __lowercase								=						(np.arange(0, UpperCAmelCase__     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
    __lowercase								=						torch.from_numpy(UpperCAmelCase__     ).to(UpperCAmelCase__     )


 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      Any=None, UpperCAmelCase__							:      str=None, UpperCAmelCase__							:      Dict=None     ):
    if prev_timestep is None:
       __lowercase								=						t - 1

    __lowercase								=						self.alphas_cumprod[t]
    __lowercase								=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
    __lowercase								=						1 - alpha_prod_t
    __lowercase								=						1 - alpha_prod_t_prev

    if prev_timestep == t - 1:
       __lowercase								=						self.betas[t]
    else:
       __lowercase								=						1 - alpha_prod_t / alpha_prod_t_prev

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
    # and sample from it to get previous sample
    # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
    __lowercase								=						beta_prod_t_prev / beta_prod_t * beta

    if variance_type is None:
       __lowercase								=						self.config.variance_type

    # hacks - were probably added for training stability
    if variance_type == "fixed_small_log":
       __lowercase								=						torch.log(torch.clamp(UpperCAmelCase__, min=1E-20     )     )
       __lowercase								=						torch.exp(0.5 * variance     )
    elif variance_type == "learned_range":
       # NOTE difference with DDPM scheduler
       __lowercase								=						variance.log()
       __lowercase								=						beta.log()

       __lowercase								=						(predicted_variance + 1) / 2
       __lowercase								=						frac * max_log + (1 - frac) * min_log

    return variance


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      int, UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      Optional[int] = None, UpperCAmelCase__							:      str=None, UpperCAmelCase__							:      bool = True, ):
    __lowercase								=						timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
       __lowercase		,__lowercase								=						torch.split(UpperCAmelCase__, sample.shape[1], dim=1     )
    else:
       __lowercase								=						None

    # 1. compute alphas, betas
    if prev_timestep is None:
       __lowercase								=						t - 1

    __lowercase								=						self.alphas_cumprod[t]
    __lowercase								=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
    __lowercase								=						1 - alpha_prod_t
    __lowercase								=						1 - alpha_prod_t_prev

    if prev_timestep == t - 1:
       __lowercase								=						self.betas[t]
       __lowercase								=						self.alphas[t]
    else:
       __lowercase								=						1 - alpha_prod_t / alpha_prod_t_prev
       __lowercase								=						1 - beta

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if self.config.prediction_type == "epsilon":
       __lowercase								=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif self.config.prediction_type == "sample":
       __lowercase								=						model_output
    else:
       raise ValueError(
           F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
           " for the UnCLIPScheduler."     )

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
       __lowercase								=						torch.clamp(
           UpperCAmelCase__, -self.config.clip_sample_range, self.config.clip_sample_range     )

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    __lowercase								=						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
    __lowercase								=						alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    __lowercase								=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    __lowercase								=						0
    if t > 0:
       __lowercase								=						randn_tensor(
           model_output.shape, dtype=model_output.dtype, generator=UpperCAmelCase__, device=model_output.device     )

       __lowercase								=						self._get_variance(
           UpperCAmelCase__, predicted_variance=UpperCAmelCase__, prev_timestep=UpperCAmelCase__, )

       if self.variance_type == "fixed_small_log":
          __lowercase								=						variance
       elif self.variance_type == "learned_range":
          __lowercase								=						(0.5 * variance).exp()
       else:
          raise ValueError(
              F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
              " for the UnCLIPScheduler."     )

       __lowercase								=						variance * variance_noise

    __lowercase								=						pred_prev_sample + variance

    if not return_dict:
       return (pred_prev_sample,)

    return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase__, pred_original_sample=UpperCAmelCase__     )



 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      torch.IntTensor, ):
    # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
    __lowercase								=						self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype     )
    __lowercase								=						timesteps.to(original_samples.device     )

    __lowercase								=						alphas_cumprod[timesteps] ** 0.5
    __lowercase								=						sqrt_alpha_prod.flatten()
    while len(sqrt_alpha_prod.shape     ) < len(original_samples.shape     ):
       __lowercase								=						sqrt_alpha_prod.unsqueeze(-1     )

    __lowercase								=						(1 - alphas_cumprod[timesteps]) ** 0.5
    __lowercase								=						sqrt_one_minus_alpha_prod.flatten()
    while len(sqrt_one_minus_alpha_prod.shape     ) < len(original_samples.shape     ):
       __lowercase								=						sqrt_one_minus_alpha_prod.unsqueeze(-1     )

    __lowercase								=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples







"""simple docstring"""





import re

from filelock import FileLock


try:
   import nltk

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

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




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   re.sub("<n>",      "",      UpperCamelCase_)  # 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(UpperCamelCase_))




