




"""simple docstring"""






import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class   a__			(       UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = MobileBertTokenizer
						snake_case__             = MobileBertTokenizerFast
						snake_case__             = True
						snake_case__             = True
						snake_case__             = filter_non_english
						snake_case__             = '''google/mobilebert-uncased'''





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Any:




											"""simple docstring"""







											super().setUp()

											_lowerCAmelCase:Optional[int]									=  [
											    '''[UNK]''',
											    '''[CLS]''',
											    '''[SEP]''',
											    '''[PAD]''',
											    '''[MASK]''',
											    '''want''',
											    '''##want''',
											    '''##ed''',
											    '''wa''',
											    '''un''',
											    '''runn''',
											    '''##ing''',
											    ''',''',
											    '''low''',
											    '''lowest''',
											]
											_lowerCAmelCase:List[Any]									=  os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['''vocab_file'''])
											with open(self.vocab_file     ,'''w'''     ,encoding='''utf-8''') as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))

											_lowerCAmelCase:Union[str, Any]									=  [
											    (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
											    for tokenizer_def in self.tokenizers_list
											]





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	str)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  '''UNwant\u00E9d,running'''
											_lowerCAmelCase:List[str]									=  '''unwanted, running'''
											return input_text, output_text





						def       __UpperCamelCase     (							self      :	Tuple)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.tokenizer_class(self.vocab_file)

											_lowerCAmelCase:Optional[int]									=  tokenizer.tokenize('''UNwant\u00E9d,running''')
											self.assertListEqual(a__     ,['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''])
											self.assertListEqual(tokenizer.convert_tokens_to_ids(a__)     ,[9, 6, 7, 12, 10, 11])





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       int:




											"""simple docstring"""







											if not self.test_rust_tokenizer:
																return

											_lowerCAmelCase:Optional[int]									=  self.get_tokenizer()
											_lowerCAmelCase:Any									=  self.get_rust_tokenizer()

											_lowerCAmelCase:Any									=  '''UNwant\u00E9d,running'''

											_lowerCAmelCase:Dict									=  tokenizer.tokenize(a__)
											_lowerCAmelCase:Any									=  rust_tokenizer.tokenize(a__)
											self.assertListEqual(a__     ,a__)

											_lowerCAmelCase:Tuple									=  tokenizer.encode(a__     ,add_special_tokens=a__)
											_lowerCAmelCase:int									=  rust_tokenizer.encode(a__     ,add_special_tokens=a__)
											self.assertListEqual(a__     ,a__)

											_lowerCAmelCase:Tuple									=  self.get_rust_tokenizer()
											_lowerCAmelCase:Tuple									=  tokenizer.encode(a__)
											_lowerCAmelCase:Union[str, Any]									=  rust_tokenizer.encode(a__)
											self.assertListEqual(a__     ,a__)

											# With lower casing
											_lowerCAmelCase:Tuple									=  self.get_tokenizer(do_lower_case=a__)
											_lowerCAmelCase:Any									=  self.get_rust_tokenizer(do_lower_case=a__)

											_lowerCAmelCase:Union[str, Any]									=  '''UNwant\u00E9d,running'''

											_lowerCAmelCase:int									=  tokenizer.tokenize(a__)
											_lowerCAmelCase:List[str]									=  rust_tokenizer.tokenize(a__)
											self.assertListEqual(a__     ,a__)

											_lowerCAmelCase:List[Any]									=  tokenizer.encode(a__     ,add_special_tokens=a__)
											_lowerCAmelCase:Dict									=  rust_tokenizer.encode(a__     ,add_special_tokens=a__)
											self.assertListEqual(a__     ,a__)

											_lowerCAmelCase:Any									=  self.get_rust_tokenizer()
											_lowerCAmelCase:List[str]									=  tokenizer.encode(a__)
											_lowerCAmelCase:str									=  rust_tokenizer.encode(a__)
											self.assertListEqual(a__     ,a__)





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:int									=  BasicTokenizer()

											self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''')     ,['''ah''', '''\u535A''', '''\u63A8''', '''zz'''])





						def       __UpperCamelCase     (							self      :	Dict)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:int									=  BasicTokenizer(do_lower_case=a__)

											self.assertListEqual(
											    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''')     ,['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''])
											self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')     ,['''hello'''])





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  BasicTokenizer(do_lower_case=a__     ,strip_accents=a__)

											self.assertListEqual(
											    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')     ,['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
											self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')     ,['''h\u00E9llo'''])





						def       __UpperCamelCase     (							self      :	str)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  BasicTokenizer(do_lower_case=a__     ,strip_accents=a__)

											self.assertListEqual(
											    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')     ,['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
											self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')     ,['''hello'''])





						def       __UpperCamelCase     (							self      :	List[str])						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  BasicTokenizer(do_lower_case=a__)

											self.assertListEqual(
											    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')     ,['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
											self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')     ,['''hello'''])





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  BasicTokenizer(do_lower_case=a__)

											self.assertListEqual(
											    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''')     ,['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  BasicTokenizer(do_lower_case=a__     ,strip_accents=a__)

											self.assertListEqual(
											    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')     ,['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])





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




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  BasicTokenizer(do_lower_case=a__     ,strip_accents=a__)

											self.assertListEqual(
											    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')     ,['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  BasicTokenizer(do_lower_case=a__     ,never_split=['''[UNK]'''])

											self.assertListEqual(
											    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]''')     ,['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''])





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:str									=  ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

											_lowerCAmelCase:List[str]									=  {}
											for i, token in enumerate(a__):
																_lowerCAmelCase:List[Any]									=  i
											_lowerCAmelCase:Any									=  WordpieceTokenizer(vocab=a__     ,unk_token='''[UNK]''')

											self.assertListEqual(tokenizer.tokenize('''''')     ,[])

											self.assertListEqual(tokenizer.tokenize('''unwanted running''')     ,['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''])

											self.assertListEqual(tokenizer.tokenize('''unwantedX running''')     ,['''[UNK]''', '''runn''', '''##ing'''])





						def       __UpperCamelCase     (							self      :	int)						->       int:




											"""simple docstring"""







											self.assertTrue(_is_whitespace(''' '''))
											self.assertTrue(_is_whitespace('''\t'''))
											self.assertTrue(_is_whitespace('''\r'''))
											self.assertTrue(_is_whitespace('''\n'''))
											self.assertTrue(_is_whitespace('''\u00A0'''))

											self.assertFalse(_is_whitespace('''A'''))
											self.assertFalse(_is_whitespace('''-'''))





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Union[str, Any]:




											"""simple docstring"""







											self.assertTrue(_is_control('''\u0005'''))

											self.assertFalse(_is_control('''A'''))
											self.assertFalse(_is_control(''' '''))
											self.assertFalse(_is_control('''\t'''))
											self.assertFalse(_is_control('''\r'''))





						def       __UpperCamelCase     (							self      :	str)						->       Tuple:




											"""simple docstring"""







											self.assertTrue(_is_punctuation('''-'''))
											self.assertTrue(_is_punctuation('''$'''))
											self.assertTrue(_is_punctuation('''`'''))
											self.assertTrue(_is_punctuation('''.'''))

											self.assertFalse(_is_punctuation('''A'''))
											self.assertFalse(_is_punctuation(''' '''))





						def       __UpperCamelCase     (							self      :	str)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.get_tokenizer()
											_lowerCAmelCase:str									=  self.get_rust_tokenizer()

											# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
											self.assertListEqual([tokenizer.tokenize(a__) for t in ['''Test''', '''\xad''', '''test''']]     ,[['''[UNK]'''], [], ['''[UNK]''']])

											self.assertListEqual(
											    [rust_tokenizer.tokenize(a__) for t in ['''Test''', '''\xad''', '''test''']]     ,[['''[UNK]'''], [], ['''[UNK]''']])





						@slow
						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  self.tokenizer_class.from_pretrained('''google/mobilebert-uncased''')

											_lowerCAmelCase:str									=  tokenizer.encode('''sequence builders'''     ,add_special_tokens=a__)
											_lowerCAmelCase:int									=  tokenizer.encode('''multi-sequence build'''     ,add_special_tokens=a__)

											_lowerCAmelCase:int									=  tokenizer.build_inputs_with_special_tokens(a__)
											_lowerCAmelCase:str									=  tokenizer.build_inputs_with_special_tokens(a__     ,a__)

											assert encoded_sentence == [101] + text + [102]
											assert encoded_pair == [101] + text + [102] + text_a + [102]





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




											"""simple docstring"""







											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
																					_lowerCAmelCase:List[Any]									=  self.rust_tokenizer_class.from_pretrained(a__     ,**a__)

																					_lowerCAmelCase:str									=  F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
																					_lowerCAmelCase:Optional[Any]									=  tokenizer_r.encode_plus(
																					    a__     ,return_attention_mask=a__     ,return_token_type_ids=a__     ,return_offsets_mapping=a__     ,add_special_tokens=a__     ,)

																					_lowerCAmelCase:List[Any]									=  tokenizer_r.do_lower_case if hasattr(a__     ,'''do_lower_case''') else False
																					_lowerCAmelCase:int									=  (
																					    [
																					        ((0, 0), tokenizer_r.cls_token),
																					        ((0, 1), '''A'''),
																					        ((1, 2), ''','''),
																					        ((3, 5), '''na'''),
																					        ((5, 6), '''##ï'''),
																					        ((6, 8), '''##ve'''),
																					        ((9, 15), tokenizer_r.mask_token),
																					        ((16, 21), '''Allen'''),
																					        ((21, 23), '''##NL'''),
																					        ((23, 24), '''##P'''),
																					        ((25, 33), '''sentence'''),
																					        ((33, 34), '''.'''),
																					        ((0, 0), tokenizer_r.sep_token),
																					    ]
																					    if not do_lower_case
																					    else [
																					        ((0, 0), tokenizer_r.cls_token),
																					        ((0, 1), '''a'''),
																					        ((1, 2), ''','''),
																					        ((3, 8), '''naive'''),
																					        ((9, 15), tokenizer_r.mask_token),
																					        ((16, 21), '''allen'''),
																					        ((21, 23), '''##nl'''),
																					        ((23, 24), '''##p'''),
																					        ((25, 33), '''sentence'''),
																					        ((33, 34), '''.'''),
																					        ((0, 0), tokenizer_r.sep_token),
																					    ]
																					)

																					self.assertEqual(
																					    [e[1] for e in expected_results]     ,tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']))
																					self.assertEqual([e[0] for e in expected_results]     ,tokens['''offset_mapping'''])







						def       __UpperCamelCase     (							self      :	Any)						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:int									=  ['''的''', '''人''', '''有''']
											_lowerCAmelCase:Any									=  ''''''.join(a__)
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
																					_lowerCAmelCase:Optional[int]									=  True
																					_lowerCAmelCase:Tuple									=  self.tokenizer_class.from_pretrained(a__     ,**a__)
																					_lowerCAmelCase:str									=  self.rust_tokenizer_class.from_pretrained(a__     ,**a__)

																					_lowerCAmelCase:Optional[Any]									=  tokenizer_p.encode(a__     ,add_special_tokens=a__)
																					_lowerCAmelCase:int									=  tokenizer_r.encode(a__     ,add_special_tokens=a__)

																					_lowerCAmelCase:int									=  tokenizer_r.convert_ids_to_tokens(a__)
																					_lowerCAmelCase:int									=  tokenizer_p.convert_ids_to_tokens(a__)

																					# it is expected that each Chinese character is not preceded by "##"
																					self.assertListEqual(a__     ,a__)
																					self.assertListEqual(a__     ,a__)

																					_lowerCAmelCase:int									=  False
																					_lowerCAmelCase:Union[str, Any]									=  self.rust_tokenizer_class.from_pretrained(a__     ,**a__)
																					_lowerCAmelCase:Any									=  self.tokenizer_class.from_pretrained(a__     ,**a__)

																					_lowerCAmelCase:Dict									=  tokenizer_r.encode(a__     ,add_special_tokens=a__)
																					_lowerCAmelCase:Any									=  tokenizer_p.encode(a__     ,add_special_tokens=a__)

																					_lowerCAmelCase:Tuple									=  tokenizer_r.convert_ids_to_tokens(a__)
																					_lowerCAmelCase:List[Any]									=  tokenizer_p.convert_ids_to_tokens(a__)

																					# it is expected that only the first Chinese character is not preceded by "##".
																					_lowerCAmelCase:List[Any]									=  [
																					    F'##{token}' if idx != 0 else token for idx, token in enumerate(a__)
																					]
																					self.assertListEqual(a__     ,a__)
																					self.assertListEqual(a__     ,a__)












"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42
						snake_case__             = None





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int=0.9_99      ,       snake_case			:					Union[str, Any]="cosine"      ,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(snake_case			:					Dict    ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

					elif alpha_transform_type == "exp":

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

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

					_lowerCAmelCase:List[str]									=  []
					for i in range(snake_case    ):
										_lowerCAmelCase:Union[str, Any]									=  i / num_diffusion_timesteps
										_lowerCAmelCase:Dict									=  (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   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Optional[Any]     ,a__      :	int = 1000     ,a__      :	float = 0.0001     ,a__      :	float = 0.02     ,a__      :	str = "linear"     ,a__      :	Optional[Union[np.ndarray, List[float]]] = None     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	int = 0     ,a__      :	str = "epsilon"     ,a__      :	float = 1.0     ,**a__      :	Any     ,)						->       str:




											"""simple docstring"""







											if kwargs.get('''set_alpha_to_one'''     ,a__) is not None:
																_lowerCAmelCase:Tuple									=  (
																    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																)
																deprecate('''set_alpha_to_one'''     ,'''1.0.0'''     ,a__     ,standard_warn=a__)
																_lowerCAmelCase:Optional[Any]									=  kwargs['''set_alpha_to_one''']
											if trained_betas is not None:
																_lowerCAmelCase:Any									=  torch.tensor(a__     ,dtype=torch.floataa)
											elif beta_schedule == "linear":
																_lowerCAmelCase:str									=  torch.linspace(a__     ,a__     ,a__     ,dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_lowerCAmelCase:int									=  (
																    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,a__     ,dtype=torch.floataa) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_lowerCAmelCase:Optional[int]									=  betas_for_alpha_bar(a__)
											else:
																raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

											_lowerCAmelCase:Any									=  1.0 - self.betas
											_lowerCAmelCase:Optional[Any]									=  torch.cumprod(self.alphas     ,dim=0)

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											_lowerCAmelCase:Optional[Any]									=  torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											_lowerCAmelCase:Any									=  1.0

											# setable values
											_lowerCAmelCase:Dict									=  None
											_lowerCAmelCase:List[Any]									=  torch.from_numpy(np.arange(0     ,a__).copy().astype(np.intaa))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	torch.FloatTensor     ,a__      :	Optional[int] = None)						->       torch.FloatTensor:




											"""simple docstring"""







											return sample





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	int     ,a__      :	Union[str, torch.device] = None)						->       List[Any]:




											"""simple docstring"""







											if num_inference_steps > self.config.num_train_timesteps:
																raise ValueError(
																    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
																    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
																    F' maximal {self.config.num_train_timesteps} timesteps.')

											_lowerCAmelCase:Union[str, Any]									=  num_inference_steps
											_lowerCAmelCase:Union[str, 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
											_lowerCAmelCase:str									=  (np.arange(0     ,a__) * step_ratio).round().copy().astype(np.intaa)
											_lowerCAmelCase:str									=  torch.from_numpy(a__).to(a__)
											self.timesteps += self.config.steps_offset





						def       __UpperCamelCase     (							self      :	int     ,a__      :	torch.FloatTensor     ,a__      :	int     ,a__      :	torch.FloatTensor     ,a__      :	float = 0.0     ,a__      :	bool = False     ,a__      :	Optional[torch.FloatTensor] = None     ,a__      :	bool = True     ,)						->       Union[DDIMSchedulerOutput, Tuple]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											_lowerCAmelCase:Optional[Any]									=  self.alphas_cumprod[timestep]
											_lowerCAmelCase:List[Any]									=  (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											_lowerCAmelCase:str									=  1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																_lowerCAmelCase:int									=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_lowerCAmelCase:str									=  model_output
											elif self.config.prediction_type == "sample":
																_lowerCAmelCase:List[str]									=  model_output
																_lowerCAmelCase:Optional[int]									=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																_lowerCAmelCase:str									=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_lowerCAmelCase:Dict									=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																raise ValueError(
																    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
																    ''' `v_prediction`''')

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																_lowerCAmelCase:List[Any]									=  pred_original_sample.clamp(
																    -self.config.clip_sample_range     ,self.config.clip_sample_range)

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:str									=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:Optional[Any]									=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=a__     ,pred_original_sample=a__)







						def __len__(							self      :	Dict)						->       Union[str, Any]:




											"""simple docstring"""







											return self.config.num_train_timesteps






