

import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class 			__magic_name__					(    lowerCAmelCase_						,					unittest.TestCase   ):
			SCREAMING_SNAKE_CASE							     =		RealmTokenizer
			SCREAMING_SNAKE_CASE							     =		RealmTokenizerFast
			SCREAMING_SNAKE_CASE							     =		True
			SCREAMING_SNAKE_CASE							     =		True
			SCREAMING_SNAKE_CASE							     =		filter_non_english





			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						super().setUp()

						__a						   =[
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__a						   =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]      )      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Any:




						'''simple docstring'''
						__a						   ='UNwant\u00E9d,running'
						__a						   ='unwanted, running'
						return input_text, output_text





			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						__a						   =self.tokenizer_class(self.vocab_file      )

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





			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						if not self.test_rust_tokenizer:
									return

						__a						   =self.get_tokenizer()
						__a						   =self.get_rust_tokenizer()

						__a						   ='UNwant\u00E9d,running'

						__a						   =tokenizer.tokenize(__snake_case      )
						__a						   =rust_tokenizer.tokenize(__snake_case      )
						self.assertListEqual(__snake_case      ,						__snake_case      )

						__a						   =tokenizer.encode(__snake_case      ,						add_special_tokens=__snake_case      )
						__a						   =rust_tokenizer.encode(__snake_case      ,						add_special_tokens=__snake_case      )
						self.assertListEqual(__snake_case      ,						__snake_case      )

						__a						   =self.get_rust_tokenizer()
						__a						   =tokenizer.encode(__snake_case      )
						__a						   =rust_tokenizer.encode(__snake_case      )
						self.assertListEqual(__snake_case      ,						__snake_case      )

						# With lower casing
						__a						   =self.get_tokenizer(do_lower_case=__snake_case      )
						__a						   =self.get_rust_tokenizer(do_lower_case=__snake_case      )

						__a						   ='UNwant\u00E9d,running'

						__a						   =tokenizer.tokenize(__snake_case      )
						__a						   =rust_tokenizer.tokenize(__snake_case      )
						self.assertListEqual(__snake_case      ,						__snake_case      )

						__a						   =tokenizer.encode(__snake_case      ,						add_special_tokens=__snake_case      )
						__a						   =rust_tokenizer.encode(__snake_case      ,						add_special_tokens=__snake_case      )
						self.assertListEqual(__snake_case      ,						__snake_case      )

						__a						   =self.get_rust_tokenizer()
						__a						   =tokenizer.encode(__snake_case      )
						__a						   =rust_tokenizer.encode(__snake_case      )
						self.assertListEqual(__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				List[Any]:




						'''simple docstring'''
						__a						   =BasicTokenizer()

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





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						__a						   =BasicTokenizer(do_lower_case=__snake_case      )

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





			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						__a						   =BasicTokenizer(do_lower_case=__snake_case      ,						strip_accents=__snake_case      )

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





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




						'''simple docstring'''
						__a						   =BasicTokenizer(do_lower_case=__snake_case      ,						strip_accents=__snake_case      )

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





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						__a						   =BasicTokenizer(do_lower_case=__snake_case      )

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





			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a						   =BasicTokenizer(do_lower_case=__snake_case      )

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





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =BasicTokenizer(do_lower_case=__snake_case      ,						strip_accents=__snake_case      )

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





			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a						   =BasicTokenizer(do_lower_case=__snake_case      ,						strip_accents=__snake_case      )

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





			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						__a						   =BasicTokenizer(do_lower_case=__snake_case      ,						never_split=['[UNK]']      )

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





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						__a						   =['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

						__a						   ={}
						for i, token in enumerate(__snake_case      ):
									__a						   =i
						__a						   =WordpieceTokenizer(vocab=__snake_case      ,						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 	__magic_name__					(	self      )     ->				Union[str, Any]:




						'''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 	__magic_name__					(	self      )     ->				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 	__magic_name__					(	self      )     ->				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 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						__a						   =self.get_tokenizer()

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

						if self.test_rust_tokenizer:
									__a						   =self.get_rust_tokenizer()
									self.assertListEqual(
									    [rust_tokenizer.tokenize(__snake_case      ) for t in ['Test', '\xad', 'test']]      ,						[['[UNK]'], [], ['[UNK]']]      )





			@slow
			def 	__magic_name__					(	self      )     ->				Dict:




						'''simple docstring'''
						__a						   =self.tokenizer_class.from_pretrained('bert-base-uncased'      )

						__a						   =tokenizer.encode('sequence builders'      ,						add_special_tokens=__snake_case      )
						__a						   =tokenizer.encode('multi-sequence build'      ,						add_special_tokens=__snake_case      )

						__a						   =tokenizer.build_inputs_with_special_tokens(__snake_case      )
						__a						   =tokenizer.build_inputs_with_special_tokens(__snake_case      ,						__snake_case      )

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





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						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      )

												__a						   =f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
												__a						   =tokenizer_r.encode_plus(
												    __snake_case      ,						return_attention_mask=__snake_case      ,						return_token_type_ids=__snake_case      ,						return_offsets_mapping=__snake_case      ,						add_special_tokens=__snake_case      ,						)

												__a						   =tokenizer_r.do_lower_case if hasattr(__snake_case      ,						'do_lower_case'      ) else False
												__a						   =(
												    [
												        ((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']      )





			@slow
			def 	__magic_name__					(	self      )     ->				Union[str, Any]:




						'''simple docstring'''
						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      )
												__a						   =self.tokenizer_class.from_pretrained(__snake_case      ,						**__snake_case      )
												__a						   =[['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

												__a						   =tokenizer_r.batch_encode_candidates(__snake_case      ,						max_length=10      ,						return_tensors='np'      )
												__a						   =tokenizer_p.batch_encode_candidates(__snake_case      ,						max_length=10      ,						return_tensors='np'      )

												__a						   =(2, 2, 10)

												self.assertEqual(encoded_sentence_r['input_ids'].shape      ,						__snake_case      )
												self.assertEqual(encoded_sentence_r['attention_mask'].shape      ,						__snake_case      )
												self.assertEqual(encoded_sentence_r['token_type_ids'].shape      ,						__snake_case      )

												self.assertEqual(encoded_sentence_p['input_ids'].shape      ,						__snake_case      )
												self.assertEqual(encoded_sentence_p['attention_mask'].shape      ,						__snake_case      )
												self.assertEqual(encoded_sentence_p['token_type_ids'].shape      ,						__snake_case      )









import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def  UpperCamelCase_(							_snake_case     :						List[Any] ,  _snake_case     :						Optional[int]=0.999 ,  _snake_case     :						List[Any]="cosine" ,  ):




			"""simple docstring"""






			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_snake_case     :						Union[str, Any]			):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_snake_case     :						Dict			):
									return math.exp(t * -12.0			)

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

			__a						   =[]
			for i in range(_snake_case			):
						__a						   =i / num_diffusion_timesteps
						__a						   =(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 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		[e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE							     =		2





			@register_to_config
			def __init__(	self      ,						__snake_case = 1000      ,						__snake_case = 0.0_0085      ,						__snake_case = 0.012      ,						__snake_case = "linear"      ,						__snake_case = None      ,						__snake_case = "epsilon"      ,						__snake_case = "linspace"      ,						__snake_case = 0      ,						)     ->				Union[str, Any]:




						'''simple docstring'''
						if trained_betas is not None:
									__a						   =torch.tensor(__snake_case      ,						dtype=torch.floataa      )
						elif beta_schedule == "linear":
									__a						   =torch.linspace(__snake_case      ,						__snake_case      ,						__snake_case      ,						dtype=torch.floataa      )
						elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									__a						   =(
									    torch.linspace(beta_start**0.5      ,						beta_end**0.5      ,						__snake_case      ,						dtype=torch.floataa      ) ** 2
									)
						elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__a						   =betas_for_alpha_bar(__snake_case      )
						else:
									raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'      )

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

						#  set all values
						self.set_timesteps(__snake_case      ,						__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case=None      )     ->				Any:




						'''simple docstring'''
						if schedule_timesteps is None:
									__a						   =self.timesteps

						__a						   =(schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter      ) == 0:
									__a						   =1 if len(__snake_case      ) > 1 else 0
						else:
									__a						   =timestep.cpu().item() if torch.is_tensor(__snake_case      ) else timestep
									__a						   =self._index_counter[timestep_int]

						return indices[pos].item()





			@property
			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						)     ->				torch.FloatTensor:




						'''simple docstring'''
						__a						   =self.index_for_timestep(__snake_case      )

						if self.state_in_first_order:
									__a						   =self.sigmas[step_index]
						else:
									__a						   =self.sigmas_interpol[step_index - 1]

						__a						   =sample / ((sigma**2 + 1) ** 0.5)
						return sample





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = None      ,						)     ->				List[str]:




						'''simple docstring'''
						__a						   =num_inference_steps

						__a						   =num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
									__a						   =np.linspace(0      ,						num_train_timesteps - 1      ,						__snake_case      ,						dtype=__snake_case      )[::-1].copy()
						elif self.config.timestep_spacing == "leading":
									__a						   =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
									__a						   =(np.arange(0      ,						__snake_case      ) * step_ratio).round()[::-1].copy().astype(__snake_case      )
									timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
									__a						   =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
									__a						   =(np.arange(__snake_case      ,						0      ,						-step_ratio      )).round().copy().astype(__snake_case      )
									timesteps -= 1
						else:
									raise ValueError(
									    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'      )

						__a						   =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
						__a						   =torch.from_numpy(np.log(__snake_case      )      ).to(__snake_case      )

						__a						   =np.interp(__snake_case      ,						np.arange(0      ,						len(__snake_case      )      )      ,						__snake_case      )
						__a						   =np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
						__a						   =torch.from_numpy(__snake_case      ).to(device=__snake_case      )

						# compute up and down sigmas
						__a						   =sigmas.roll(-1      )
						__a						   =0.0
						__a						   =(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
						__a						   =(sigmas_next**2 - sigmas_up**2) ** 0.5
						__a						   =0.0

						# compute interpolated sigmas
						__a						   =sigmas.log().lerp(sigmas_down.log()      ,						0.5      ).exp()
						__a						   =0.0

						# set sigmas
						__a						   =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
						__a						   =torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
						__a						   =torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
						__a						   =torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

						if str(__snake_case      ).startswith('mps'      ):
									# mps does not support float64
									__a						   =torch.from_numpy(__snake_case      ).to(__snake_case      ,						dtype=torch.floataa      )
						else:
									__a						   =torch.from_numpy(__snake_case      ).to(__snake_case      )

						__a						   =self.sigma_to_t(__snake_case      ).to(__snake_case      ,						dtype=timesteps.dtype      )
						__a						   =torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,						dim=-1      ).flatten()

						__a						   =torch.cat([timesteps[:1], interleaved_timesteps]      )

						__a						   =None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						__a						   =defaultdict(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						# get log sigma
						__a						   =sigma.log()

						# get distribution
						__a						   =log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						__a						   =dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
						__a						   =low_idx + 1

						__a						   =self.log_sigmas[low_idx]
						__a						   =self.log_sigmas[high_idx]

						# interpolate sigmas
						__a						   =(low - log_sigma) / (low - high)
						__a						   =w.clamp(0      ,						1      )

						# transform interpolation to time range
						__a						   =(1 - w) * low_idx + w * high_idx
						__a						   =t.view(sigma.shape      )
						return t





			@property
			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						return self.sample is None





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = True      ,						)     ->				Union[SchedulerOutput, Tuple]:




						'''simple docstring'''
						__a						   =self.index_for_timestep(__snake_case      )

						# advance index counter by 1
						__a						   =timestep.cpu().item() if torch.is_tensor(__snake_case      ) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
									__a						   =self.sigmas[step_index]
									__a						   =self.sigmas_interpol[step_index]
									__a						   =self.sigmas_up[step_index]
									__a						   =self.sigmas_down[step_index - 1]
						else:
									# 2nd order / KPDM2's method
									__a						   =self.sigmas[step_index - 1]
									__a						   =self.sigmas_interpol[step_index - 1]
									__a						   =self.sigmas_up[step_index - 1]
									__a						   =self.sigmas_down[step_index - 1]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						__a						   =0
						__a						   =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						__a						   =model_output.device
						__a						   =randn_tensor(model_output.shape      ,						dtype=model_output.dtype      ,						device=__snake_case      ,						generator=__snake_case      )

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
									__a						   =sigma_hat if self.state_in_first_order else sigma_interpol
									__a						   =sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
									__a						   =sigma_hat if self.state_in_first_order else sigma_interpol
									__a						   =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**2 + 1)
									)
						elif self.config.prediction_type == "sample":
									raise NotImplementedError('prediction_type not implemented yet: sample'      )
						else:
									raise ValueError(
									    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

						if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									__a						   =(sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									__a						   =sigma_interpol - sigma_hat

									# store for 2nd order step
									__a						   =sample
									__a						   =dt
									__a						   =sample + derivative * dt
						else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									__a						   =(sample - pred_original_sample) / sigma_interpol
									# 3. delta timestep
									__a						   =sigma_down - sigma_hat

									__a						   =self.sample
									__a						   =None

									__a						   =sample + derivative * dt
									__a						   =prev_sample + noise * sigma_up

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						)     ->				torch.FloatTensor:




						'''simple docstring'''
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						__a						   =self.sigmas.to(device=original_samples.device      ,						dtype=original_samples.dtype      )
						if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case      ):
									# mps does not support float64
									__a						   =self.timesteps.to(original_samples.device      ,						dtype=torch.floataa      )
									__a						   =timesteps.to(original_samples.device      ,						dtype=torch.floataa      )
						else:
									__a						   =self.timesteps.to(original_samples.device      )
									__a						   =timesteps.to(original_samples.device      )

						__a						   =[self.index_for_timestep(__snake_case      ,						__snake_case      ) for t in timesteps]

						__a						   =sigmas[step_indices].flatten()
						while len(sigma.shape      ) < len(original_samples.shape      ):
									__a						   =sigma.unsqueeze(-1      )

						__a						   =original_samples + noise * sigma
						return noisy_samples





			def __len__(	self      )     ->				Optional[int]:




						'''simple docstring'''
						return self.config.num_train_timesteps






