"""simple docstring"""





import math
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 SchedulerMixin, SchedulerOutput


class      UpperCAmelCase_	(    _a ,							_a):
      lowerCamelCase__		:    Any   =  1


      @register_to_config
      def __init__(   self		,			a = 1_0_0_0		,			a = None							)       ->					Dict:
             # set `betas`, `alphas`, `timesteps`
             self.set_timesteps(a							)

             # standard deviation of the initial noise distribution
             lowercase__			:		Optional[int]       =	1.0

             # For now we only support F-PNDM, i.e. the runge-kutta method
             # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
             # mainly at formula (9), (12), (13) and the Algorithm 2.
             lowercase__			:		List[Any]       =	4

             # running values
             lowercase__			:		Dict       =	[]


      def 		_UpperCAmelCase       (   self		,			a		,			a = None							)       ->					Optional[int]:
             lowercase__			:		int       =	num_inference_steps
             lowercase__			:		Optional[int]       =	torch.linspace(1		,			0		,			num_inference_steps + 1							)[:-1]
             lowercase__			:		str       =	torch.cat([steps, torch.tensor([0.0]							)]							)

             if self.config.trained_betas is not None:
                    lowercase__			:		Optional[int]       =	torch.tensor(self.config.trained_betas		,			dtype=torch.floataa							)
             else:
                    lowercase__			:		Optional[int]       =	torch.sin(steps * math.pi / 2							) ** 2

             lowercase__			:		int       =	(1.0 - self.betas**2) ** 0.5

             lowercase__			:		Optional[int]       =	(torch.atana(self.betas		,			self.alphas							) / math.pi * 2)[:-1]
             lowercase__			:		int       =	timesteps.to(a							)

             lowercase__			:		Any       =	[]


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a = True		,			)       ->					Union[SchedulerOutput, Tuple]:
             if self.num_inference_steps is None:
                    raise ValueError(
                        'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'							)

             lowercase__			:		Optional[int]       =	(self.timesteps == timestep).nonzero().item()
             lowercase__			:		int       =	timestep_index + 1

             lowercase__			:		Tuple       =	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
             self.ets.append(a							)

             if len(self.ets							) == 1:
                    lowercase__			:		Tuple       =	self.ets[-1]
             elif len(self.ets							) == 2:
                    lowercase__			:		Dict       =	(3 * self.ets[-1] - self.ets[-2]) / 2
             elif len(self.ets							) == 3:
                    lowercase__			:		List[Any]       =	(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
             else:
                    lowercase__			:		Dict       =	(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

             lowercase__			:		str       =	self._get_prev_sample(a		,			a		,			a		,			a							)

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=a							)


      def 		_UpperCAmelCase       (   self		,			a		,			*a		,			**a							)       ->					torch.FloatTensor:
             return sample


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a							)       ->					int:
             lowercase__			:		Dict       =	self.alphas[timestep_index]
             lowercase__			:		Tuple       =	self.betas[timestep_index]

             lowercase__			:		Dict       =	self.alphas[prev_timestep_index]
             lowercase__			:		Tuple       =	self.betas[prev_timestep_index]

             lowercase__			:		Optional[Any]       =	(sample - sigma * ets) / max(a		,			1e-8							)
             lowercase__			:		Optional[int]       =	next_alpha * pred + ets * next_sigma

             return prev_sample


      def __len__(   self							)       ->					Union[str, Any]:
             return self.config.num_train_timesteps



'''simple docstring'''




from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class  A				(							__snake_case						):
     __magic_name__						=		DistilBertTokenizer
     __magic_name__						=		DistilBertTokenizerFast
     __magic_name__						=		True





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''	)

         A       :   Dict					=						tokenizer.encode('''sequence builders'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						tokenizer.encode('''multi-sequence build'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




