


import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  snake_case_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Optional[Any]					   =    OpenAIGPTTokenizer
					UpperCAmelCase__      :					Any					   =    OpenAIGPTTokenizerFast
					UpperCAmelCase__      :					str					   =    True
					UpperCAmelCase__      :					Any					   =    False
					def 				lowerCamelCase__(   self     :List[str]   )      ->					str:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												a__    =  [
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    'w</w>',
												    'r</w>',
												    't</w>',
												    'lo',
												    'low',
												    'er</w>',
												    'low</w>',
												    'lowest</w>',
												    'newer</w>',
												    'wider</w>',
												    '<unk>',
												]
												a__    =  dict(zip(__snake_case    ,range(len(__snake_case   )   )   )   )
												a__    =  ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

												a__    =  os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']   )
												a__    =  os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['merges_file']   )
												with open(self.vocab_file    ,'w'   ) as fp:
																			fp.write(json.dumps(__snake_case   )   )
												with open(self.merges_file    ,'w'   ) as fp:
																			fp.write('\n'.join(__snake_case   )   )
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :Optional[Any]   )      ->					int:
												return "lower newer", "lower newer"
					def 				lowerCamelCase__(   self     :Any   )      ->					List[str]:
												a__    =  OpenAIGPTTokenizer(self.vocab_file    ,self.merges_file   )

												a__    =  'lower'
												a__    =  ['low', 'er</w>']
												a__    =  tokenizer.tokenize(__snake_case   )
												self.assertListEqual(__snake_case    ,__snake_case   )

												a__    =  tokens + ['<unk>']
												a__    =  [14, 15, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case   )    ,__snake_case   )
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :Optional[Any]=15   )      ->					Optional[int]:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																										a__    =  self.rust_tokenizer_class.from_pretrained(__snake_case    ,**__snake_case   )

																										# Simple input
																										a__    =  'This is a simple input'
																										a__    =  ['This is a simple input 1', 'This is a simple input 2']
																										a__    =  ('This is a simple input', 'This is a pair')
																										a__    =  [
																										    ('This is a simple input 1', 'This is a simple input 2'),
																										    ('This is a simple pair 1', 'This is a simple pair 2'),
																										]

																										# Simple input tests
																										self.assertRaises(__snake_case    ,tokenizer_r.encode    ,__snake_case    ,max_length=__snake_case    ,padding='max_length'   )

																										# Simple input
																										self.assertRaises(__snake_case    ,tokenizer_r.encode_plus    ,__snake_case    ,max_length=__snake_case    ,padding='max_length'   )

																										# Simple input
																										self.assertRaises(
																										    __snake_case    ,tokenizer_r.batch_encode_plus    ,__snake_case    ,max_length=__snake_case    ,padding='max_length'    ,)

																										# Pair input
																										self.assertRaises(__snake_case    ,tokenizer_r.encode    ,__snake_case    ,max_length=__snake_case    ,padding='max_length'   )

																										# Pair input
																										self.assertRaises(__snake_case    ,tokenizer_r.encode_plus    ,__snake_case    ,max_length=__snake_case    ,padding='max_length'   )

																										# Pair input
																										self.assertRaises(
																										    __snake_case    ,tokenizer_r.batch_encode_plus    ,__snake_case    ,max_length=__snake_case    ,padding='max_length'    ,)



					def 				lowerCamelCase__(   self     :int   )      ->					str:
												pass







@require_ftfy
@require_spacy
@require_tokenizers
class  snake_case_     (lowerCamelCase_   ):
					pass








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  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[Any]					   =    1
					@register_to_config
					def __init__(   self     :Optional[int]    ,__snake_case     :int = 10_00    ,__snake_case     :Optional[Union[np.ndarray, List[float]]] = None   )      ->					int:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__snake_case   )

												# standard deviation of the initial noise distribution
												a__    =  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.
												a__    =  4

												# running values
												a__    =  []
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Union[str, torch.device] = None   )      ->					Union[str, Any]:
												a__    =  num_inference_steps
												a__    =  torch.linspace(1    ,0    ,num_inference_steps + 1   )[:-1]
												a__    =  torch.cat([steps, torch.tensor([0.0]   )]   )

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

												a__    =  (1.0 - self.betas**2) ** 0.5

												a__    =  (torch.atana(self.betas    ,self.alphas   ) / math.pi * 2)[:-1]
												a__    =  timesteps.to(__snake_case   )

												a__    =  []
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :torch.FloatTensor    ,__snake_case     :int    ,__snake_case     :torch.FloatTensor    ,__snake_case     :bool = 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'   )

												a__    =  (self.timesteps == timestep).nonzero().item()
												a__    =  timestep_index + 1

												a__    =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__snake_case   )

												if len(self.ets   ) == 1:
																			a__    =  self.ets[-1]
												elif len(self.ets   ) == 2:
																			a__    =  (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																			a__    =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																			a__    =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												a__    =  self._get_prev_sample(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case   )

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :torch.FloatTensor    ,*__snake_case     :int    ,**__snake_case     :Optional[int]   )      ->					torch.FloatTensor:
												return sample
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  self.alphas[timestep_index]
												a__    =  self.betas[timestep_index]

												a__    =  self.alphas[prev_timestep_index]
												a__    =  self.betas[prev_timestep_index]

												a__    =  (sample - sigma * ets) / max(__snake_case    ,1E-8   )
												a__    =  next_alpha * pred + ets * next_sigma

												return prev_sample



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




