'''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     __UpperCAmelCase       ( lowercase_		):
  __A :				torch.FloatTensor
  __A :				Optional[torch.FloatTensor]											= None






def snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Tuple=0.9_9_9	,   __snake_case  :							Any="cosine"	,   )			->					Optional[int]:







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__snake_case  :							Optional[int]):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__snake_case  :							List[Any]):
         return math.exp(t * -1_2.0)

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

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






class     __UpperCAmelCase       ( lowercase_						,       lowercase_		):





  @register_to_config
  def __init__(	self    ,     _lowerCamelCase = 1000    ,     _lowerCamelCase = "fixed_small_log"    ,     _lowerCamelCase = True    ,     _lowerCamelCase = 1.0    ,     _lowerCamelCase = "epsilon"    ,     _lowerCamelCase = "squaredcos_cap_v2"    ,     ):







     if beta_schedule != "squaredcos_cap_v2":
        raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )

     lowerCAmelCase_			=      betas_for_alpha_bar(_lowerCamelCase )

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

     # standard deviation of the initial noise distribution
     lowerCAmelCase_			=      1.0

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

     lowerCAmelCase_			=      variance_type





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     return sample





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      num_inference_steps
     lowerCAmelCase_			=      (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
     lowerCAmelCase_			=      (np.arange(0    ,     _lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
     lowerCAmelCase_			=      torch.from_numpy(_lowerCamelCase ).to(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None ):







     if prev_timestep is None:
        lowerCAmelCase_			=      t - 1

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

     if prev_timestep == t - 1:
        lowerCAmelCase_			=      self.betas[t]
     else:
        lowerCAmelCase_			=      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
     lowerCAmelCase_			=      beta_prod_t_prev / beta_prod_t * beta

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

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

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

     return variance





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase=None    ,     _lowerCamelCase = True    ,     ):
     lowerCAmelCase_			=      timestep

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

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

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

     if prev_timestep == t - 1:
        lowerCAmelCase_			=      self.betas[t]
        lowerCAmelCase_			=      self.alphas[t]
     else:
        lowerCAmelCase_			=      1 - alpha_prod_t / alpha_prod_t_prev
        lowerCAmelCase_			=      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":
        lowerCAmelCase_			=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     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 `sample`'''
            ''' for the UnCLIPScheduler.''' )

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
        lowerCAmelCase_			=      torch.clamp(
            _lowerCamelCase    ,     -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
     lowerCAmelCase_			=      (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
     lowerCAmelCase_			=      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
     lowerCAmelCase_			=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

        lowerCAmelCase_			=      self._get_variance(
            _lowerCamelCase    ,     predicted_variance=_lowerCamelCase    ,     prev_timestep=_lowerCamelCase    ,     )

        if self.variance_type == "fixed_small_log":
           lowerCAmelCase_			=      variance
        elif self.variance_type == "learned_range":
           lowerCAmelCase_			=      (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.''' )

        lowerCAmelCase_			=      variance * variance_noise

     lowerCAmelCase_			=      pred_prev_sample + variance

     if not return_dict:
        return (pred_prev_sample,)

     return UnCLIPSchedulerOutput(prev_sample=_lowerCamelCase    ,     pred_original_sample=_lowerCamelCase )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     ):
     lowerCAmelCase_			=      self.alphas_cumprod.to(device=original_samples.device    ,     dtype=original_samples.dtype )
     lowerCAmelCase_			=      timesteps.to(original_samples.device )

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

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

     lowerCAmelCase_			=      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples









'''simple docstring'''

import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class     __lowerCAmelCase		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	str       =				RoFormerTokenizer
      lowerCAmelCase__   :	Dict       =				RoFormerTokenizerFast
      lowerCAmelCase__   :	List[str]       =				True
      lowerCAmelCase__   :	List[str]       =				True



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             super().setUp()



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       int					):

             '''simple docstring'''







             return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					'''永和服装饰品有限公司,今天天气非常好'''
             lowercase__      =					'''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
             return input_text, output_text



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					self.get_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)

             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)

             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.get_rust_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)
             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)
             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             pass




