


import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__						:str         =							logging.get_logger(__name__)

lowerCAmelCase__						:str         =							{
    '''src_vocab_file''': '''vocab-src.json''',
    '''tgt_vocab_file''': '''vocab-tgt.json''',
    '''merges_file''': '''merges.txt''',
}

lowerCAmelCase__						:Optional[int]         =							{
    '''src_vocab_file''': {
        '''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'''
    },
    '''tgt_vocab_file''': {
        '''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'''
    },
    '''merges_file''': {'''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'''},
}

lowerCAmelCase__						:str         =							{'''stas/tiny-wmt19-en-de''': 1_0_2_4}
lowerCAmelCase__						:str         =							{
    '''stas/tiny-wmt19-en-de''': {
        '''langs''': ['''en''', '''de'''],
        '''model_max_length''': 1_0_2_4,
        '''special_tokens_map_file''': None,
        '''full_tokenizer_file''': None,
    }
}

def  lowerCAmelCase__				(    a__:	Optional[int] )						->	Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase					=    set()
							_UpperCAmelCase					=    word[0]
							for char in word[1:]:
														pairs.add((prev_char, char) )
														_UpperCAmelCase					=    char
							return pairs

def  lowerCAmelCase__				(    a__:	List[Any] )						->	Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase					=    text.replace('，'    ,						',' )
							_UpperCAmelCase					=    re.sub(R'。\s*'    ,						'. '    ,						a__ )
							_UpperCAmelCase					=    text.replace('、'    ,						',' )
							_UpperCAmelCase					=    text.replace('”'    ,						'"' )
							_UpperCAmelCase					=    text.replace('“'    ,						'"' )
							_UpperCAmelCase					=    text.replace('∶'    ,						':' )
							_UpperCAmelCase					=    text.replace('：'    ,						':' )
							_UpperCAmelCase					=    text.replace('？'    ,						'?' )
							_UpperCAmelCase					=    text.replace('《'    ,						'"' )
							_UpperCAmelCase					=    text.replace('》'    ,						'"' )
							_UpperCAmelCase					=    text.replace('）'    ,						')' )
							_UpperCAmelCase					=    text.replace('！'    ,						'!' )
							_UpperCAmelCase					=    text.replace('（'    ,						'(' )
							_UpperCAmelCase					=    text.replace('；'    ,						';' )
							_UpperCAmelCase					=    text.replace('１'    ,						'1' )
							_UpperCAmelCase					=    text.replace('」'    ,						'"' )
							_UpperCAmelCase					=    text.replace('「'    ,						'"' )
							_UpperCAmelCase					=    text.replace('０'    ,						'0' )
							_UpperCAmelCase					=    text.replace('３'    ,						'3' )
							_UpperCAmelCase					=    text.replace('２'    ,						'2' )
							_UpperCAmelCase					=    text.replace('５'    ,						'5' )
							_UpperCAmelCase					=    text.replace('６'    ,						'6' )
							_UpperCAmelCase					=    text.replace('９'    ,						'9' )
							_UpperCAmelCase					=    text.replace('７'    ,						'7' )
							_UpperCAmelCase					=    text.replace('８'    ,						'8' )
							_UpperCAmelCase					=    text.replace('４'    ,						'4' )
							_UpperCAmelCase					=    re.sub(R'．\s*'    ,						'. '    ,						a__ )
							_UpperCAmelCase					=    text.replace('～'    ,						'~' )
							_UpperCAmelCase					=    text.replace('’'    ,						'\'' )
							_UpperCAmelCase					=    text.replace('…'    ,						'...' )
							_UpperCAmelCase					=    text.replace('━'    ,						'-' )
							_UpperCAmelCase					=    text.replace('〈'    ,						'<' )
							_UpperCAmelCase					=    text.replace('〉'    ,						'>' )
							_UpperCAmelCase					=    text.replace('【'    ,						'[' )
							_UpperCAmelCase					=    text.replace('】'    ,						']' )
							_UpperCAmelCase					=    text.replace('％'    ,						'%' )
							return text

def  lowerCAmelCase__				(    a__:	List[str] )						->	Dict:


							'''simple docstring'''





							_UpperCAmelCase					=    []
							for char in text:
														_UpperCAmelCase					=    unicodedata.category(a__ )
														if cat.startswith('C' ):
																					continue
														output.append(a__ )
							return "".join(a__ )







class 	__a   (       UpperCAmelCase    ):
		_a			:						Optional[Any]    			=			VOCAB_FILES_NAMES
		_a			:						Any    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Dict    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						Optional[int]    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						List[str]    			=			['input_ids', 'attention_mask']
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="<unk>"    ,					_SCREAMING_SNAKE_CASE="<s>"    ,					_SCREAMING_SNAKE_CASE="</s>"    ,					_SCREAMING_SNAKE_CASE="<pad>"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> Any:




									"""simple docstring"""





									super().__init__(
									    langs=_SCREAMING_SNAKE_CASE    ,					src_vocab_file=_SCREAMING_SNAKE_CASE    ,					tgt_vocab_file=_SCREAMING_SNAKE_CASE    ,					merges_file=_SCREAMING_SNAKE_CASE    ,					do_lower_case=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									try:
																import sacremoses
									except ImportError:
																raise ImportError(
																    'You need to install sacremoses to use XLMTokenizer. '
																    'See https://pypi.org/project/sacremoses/ for installation.'		)

									_UpperCAmelCase					=    sacremoses

									_UpperCAmelCase					=    src_vocab_file
									_UpperCAmelCase					=    tgt_vocab_file
									_UpperCAmelCase					=    merges_file
									_UpperCAmelCase					=    do_lower_case

									# cache of sm.MosesPunctNormalizer instance
									_UpperCAmelCase					=    {}
									# cache of sm.MosesTokenizer instance
									_UpperCAmelCase					=    {}
									_UpperCAmelCase					=    {}

									if langs and len(_SCREAMING_SNAKE_CASE		) == 2:
																_UpperCAmelCase	, _UpperCAmelCase					=    langs
									else:
																raise ValueError(
																    f'''arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '''
																    'Usually that means that tokenizer can\'t find a mapping for the given model path '
																    'in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.'		)

									with open(_SCREAMING_SNAKE_CASE    ,					encoding='utf-8'		) as src_vocab_handle:
																_UpperCAmelCase					=    json.load(_SCREAMING_SNAKE_CASE		)
									with open(_SCREAMING_SNAKE_CASE    ,					encoding='utf-8'		) as tgt_vocab_handle:
																_UpperCAmelCase					=    json.load(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    {v: k for k, v in tgt_vocab.items()}
									with open(_SCREAMING_SNAKE_CASE    ,					encoding='utf-8'		) as merges_handle:
																_UpperCAmelCase					=    merges_handle.read().split('\n'		)[:-1]
									_UpperCAmelCase					=    [tuple(merge.split()[:2]		) for merge in merges]
									_UpperCAmelCase					=    dict(zip(_SCREAMING_SNAKE_CASE    ,					range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
									_UpperCAmelCase					=    {}
		def 			UpperCAmelCase__       (    self		)				-> Dict[str, int]:




									"""simple docstring"""





									return self.get_src_vocab()
		@property
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									return self.src_vocab_size
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[int]:




									"""simple docstring"""





									if lang not in self.cache_moses_punct_normalizer:
																_UpperCAmelCase					=    self.sm.MosesPunctNormalizer(lang=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    punct_normalizer
									return self.cache_moses_punct_normalizer[lang].normalize(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									if lang not in self.cache_moses_tokenizer:
																_UpperCAmelCase					=    self.sm.MosesTokenizer(lang=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    moses_tokenizer
									return self.cache_moses_tokenizer[lang].tokenize(
									    _SCREAMING_SNAKE_CASE    ,					aggressive_dash_splits=_SCREAMING_SNAKE_CASE    ,					return_str=_SCREAMING_SNAKE_CASE    ,					escape=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> List[Any]:




									"""simple docstring"""





									if lang not in self.cache_moses_detokenizer:
																_UpperCAmelCase					=    self.sm.MosesDetokenizer(lang=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    moses_detokenizer
									return self.cache_moses_detokenizer[lang].detokenize(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    replace_unicode_punct(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.moses_punct_norm(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    remove_non_printing_char(_SCREAMING_SNAKE_CASE		)
									return text
		@property
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									return len(self.encoder		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									return len(self.decoder		)
		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									return dict(self.encoder    ,					**self.added_tokens_encoder		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									return dict(self.decoder    ,					**self.added_tokens_decoder		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    tuple(token[:-1]		) + (token[-1] + '</w>',)
									if token in self.cache:
																return self.cache[token]
									_UpperCAmelCase					=    get_pairs(_SCREAMING_SNAKE_CASE		)

									if not pairs:
																return token + "</w>"

									while True:
																_UpperCAmelCase					=    min(_SCREAMING_SNAKE_CASE    ,					key=lambda _SCREAMING_SNAKE_CASE		: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE    ,					float('inf'		)		)		)
																if bigram not in self.bpe_ranks:
																							break
																_UpperCAmelCase	, _UpperCAmelCase					=    bigram
																_UpperCAmelCase					=    []
																_UpperCAmelCase					=    0
																while i < len(_SCREAMING_SNAKE_CASE		):
																							try:
																														_UpperCAmelCase					=    word.index(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							except ValueError:
																														new_word.extend(word[i:]		)
																														break
																							else:
																														new_word.extend(word[i:j]		)
																														_UpperCAmelCase					=    j

																							if word[i] == first and i < len(_SCREAMING_SNAKE_CASE		) - 1 and word[i + 1] == second:
																														new_word.append(first + second		)
																														i += 2
																							else:
																														new_word.append(word[i]		)
																														i += 1
																_UpperCAmelCase					=    tuple(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    new_word
																if len(_SCREAMING_SNAKE_CASE		) == 1:
																							break
																else:
																							_UpperCAmelCase					=    get_pairs(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    ' '.join(_SCREAMING_SNAKE_CASE		)
									if word == "\n  </w>":
																_UpperCAmelCase					=    '\n</w>'
									_UpperCAmelCase					=    word
									return word
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE="en"    ,					_SCREAMING_SNAKE_CASE=False		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.src_lang

									if self.do_lower_case:
																_UpperCAmelCase					=    text.lower()

									if bypass_tokenizer:
																_UpperCAmelCase					=    text.split()
									else:
																_UpperCAmelCase					=    self.moses_pipeline(_SCREAMING_SNAKE_CASE    ,					lang=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    self.moses_tokenize(_SCREAMING_SNAKE_CASE    ,					lang=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    []
									for token in text:
																if token:
																							split_tokens.extend(list(self.bpe(_SCREAMING_SNAKE_CASE		).split(' '		)		)		)

									return split_tokens
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									return self.encoder.get(_SCREAMING_SNAKE_CASE    ,					self.encoder.get(self.unk_token		)		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									return self.decoder.get(_SCREAMING_SNAKE_CASE    ,					self.unk_token		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [t.replace(' '    ,					''		).replace('</w>'    ,					' '		) for t in tokens]
									_UpperCAmelCase					=    ''.join(_SCREAMING_SNAKE_CASE		).split()
									# detokenize
									_UpperCAmelCase					=    self.moses_detokenize(_SCREAMING_SNAKE_CASE    ,					self.tgt_lang		)
									return text
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> List[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]

									# no bos used in fairseq
									if token_ids_a is None:
																return token_ids_a + sep
									return token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = False		)				-> List[int]:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE    ,					token_ids_a=_SCREAMING_SNAKE_CASE    ,					already_has_special_tokens=_SCREAMING_SNAKE_CASE		)
									# no bos used in fairseq
									if token_ids_a is not None:
																return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> List[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]

									# no bos used in fairseq
									if token_ids_a is None:
																return len(token_ids_a + sep		) * [0]
									return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> Tuple[str]:




									"""simple docstring"""





									if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return

									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['src_vocab_file']		)
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['tgt_vocab_file']		)
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']		)

									with open(_SCREAMING_SNAKE_CASE    ,					'w'    ,					encoding='utf-8'		) as f:
																f.write(json.dumps(self.encoder    ,					indent=2    ,					sort_keys=_SCREAMING_SNAKE_CASE    ,					ensure_ascii=_SCREAMING_SNAKE_CASE		) + '\n'		)

									with open(_SCREAMING_SNAKE_CASE    ,					'w'    ,					encoding='utf-8'		) as f:
																_UpperCAmelCase					=    {v: k for k, v in self.decoder.items()}
																f.write(json.dumps(_SCREAMING_SNAKE_CASE    ,					indent=2    ,					sort_keys=_SCREAMING_SNAKE_CASE    ,					ensure_ascii=_SCREAMING_SNAKE_CASE		) + '\n'		)

									_UpperCAmelCase					=    0
									with open(_SCREAMING_SNAKE_CASE    ,					'w'    ,					encoding='utf-8'		) as writer:
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,					key=lambda _SCREAMING_SNAKE_CASE		: kv[1]		):
																							if index != token_index:
																														logger.warning(
																														    f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
																														    ' Please check that the tokenizer is not corrupted!'		)
																														_UpperCAmelCase					=    token_index
																							writer.write(' '.join(_SCREAMING_SNAKE_CASE		) + '\n'		)
																							index += 1

									return src_vocab_file, tgt_vocab_file, merges_file
		def __getstate__(    self		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    self.__dict__.copy()
									_UpperCAmelCase					=    None
									return state






		def __setstate__(    self    ,					_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase					=    d

									try:
																import sacremoses
									except ImportError:
																raise ImportError(
																    'You need to install sacremoses to use XLMTokenizer. '
																    'See https://pypi.org/project/sacremoses/ for installation.'		)

									_UpperCAmelCase					=    sacremoses




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 	__a   (       unittest.TestCase    ):
		# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
		_a			:						List[Any]    			=			(ScoreSdeVeScheduler,)
		_a			:						Any    			=			()
		@property
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    4
									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    8
									_UpperCAmelCase					=    8

									_UpperCAmelCase					=    torch.rand((batch_size, num_channels, height, width)		)

									return sample
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    4
									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    8
									_UpperCAmelCase					=    8

									_UpperCAmelCase					=    batch_size * num_channels * height * width
									_UpperCAmelCase					=    torch.arange(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    sample.reshape(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    sample / num_elems
									_UpperCAmelCase					=    sample.permute(3    ,					0    ,					1    ,					2		)

									return sample
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									def model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					*_SCREAMING_SNAKE_CASE		):
																return sample * t / (t + 1)

									return model
		def 			UpperCAmelCase__       (    self    ,					**_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'num_train_timesteps': 2000,
									    'snr': 0.15,
									    'sigma_min': 0.01,
									    'sigma_max': 1348,
									    'sampling_eps': 1e-5,
									}

									config.update(**_SCREAMING_SNAKE_CASE		)
									return config
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																_UpperCAmelCase					=    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

																_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_correct(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)
									kwargs.update(_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

																_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_correct(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									for timesteps in [10, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]    ,					[1, 100, 1000]		):
																self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE    ,					sigma_max=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									for t in [0.1, 0.5, 0.75]:
																self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    self.scheduler_classes[0]
									_UpperCAmelCase					=    self.get_scheduler_config()
									_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    3

									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter

									scheduler.set_sigmas(_SCREAMING_SNAKE_CASE		)
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    torch.manual_seed(0		)

									for i, t in enumerate(scheduler.timesteps		):
																_UpperCAmelCase					=    scheduler.sigmas[i]

																for _ in range(scheduler.config.correct_steps		):
																							with torch.no_grad():
																														_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																with torch.no_grad():
																							_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase	, _UpperCAmelCase					=    output.prev_sample, output.prev_sample_mean

									_UpperCAmelCase					=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert np.isclose(result_sum.item()    ,					14372758528.0		)
									assert np.isclose(result_mean.item()    ,					18714530.0		)






		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    kwargs.pop('num_inference_steps'    ,					_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
																elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							_UpperCAmelCase					=    num_inference_steps

																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					0    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					1    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																self.assertEqual(output_a.shape    ,					sample.shape		)
																self.assertEqual(output_a.shape    ,					output_a.shape		)
