
import json
import os
from typing import List, Optional, Tuple

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


lowercase__						:List[str]					    =       logging.get_logger(__name__)

lowercase__						:Tuple					    =       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowercase__						:Any					    =       {
    "vocab_file": {
        "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json",
    },
    "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"},
}

lowercase__						:List[str]					    =       {
    "microsoft/biogpt": 1024,
}



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		set()
					lowercase		      =		word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)						)
										lowercase		      =		char
					return pairs







class 							lowercase						(   _SCREAMING_SNAKE_CASE						):
							lowercase_			:			Tuple			   =VOCAB_FILES_NAMES
							lowercase_			:			List[Any]			   =PRETRAINED_VOCAB_FILES_MAP
							lowercase_			:			Optional[Any]			   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_			:			Optional[int]			   =["""input_ids""", """attention_mask"""]




							def __init__(							self  ,A__  ,A__  ,A__="<unk>"  ,A__="<s>"  ,A__="</s>"  ,A__="</s>"  ,A__="<pad>"  ,**A__  ,):
												super().__init__(
												    bos_token=_SCREAMING_SNAKE_CASE  ,eos_token=_SCREAMING_SNAKE_CASE  ,sep_token=_SCREAMING_SNAKE_CASE  ,unk_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 BioGptTokenizer. '''
																	    '''See https://pypi.org/project/sacremoses/ for installation.''')

												lowercase		      =		'''en'''
												lowercase		      =		sacremoses
												# cache of sm.MosesTokenizer instance
												lowercase		      =		{}
												lowercase		      =		{}
												with open(_SCREAMING_SNAKE_CASE  ,encoding='''utf-8''') as vocab_handle:
																	lowercase		      =		json.load(_SCREAMING_SNAKE_CASE)
												lowercase		      =		{v: k for k, v in self.encoder.items()}
												with open(_SCREAMING_SNAKE_CASE  ,encoding='''utf-8''') as merges_handle:
																	lowercase		      =		merges_handle.read().split('''\n''')[:-1]
												lowercase		      =		[tuple(merge.split()[:2]) for merge in merges]
												lowercase		      =		dict(zip(_SCREAMING_SNAKE_CASE  ,range(len(_SCREAMING_SNAKE_CASE))))
												lowercase		      =		{}




							@property
							def 					A__ (							self):
												return len(self.encoder)




							def 					A__ (							self):
												return dict(self.encoder  ,**self.added_tokens_encoder)




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_tokenizer:
																	lowercase		      =		self.sm.MosesTokenizer(lang=_SCREAMING_SNAKE_CASE)
																	lowercase		      =		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 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_detokenizer:
																	lowercase		      =		self.sm.MosesDetokenizer(lang=_SCREAMING_SNAKE_CASE)
																	lowercase		      =		moses_detokenizer
												return self.cache_moses_detokenizer[lang].detokenize(_SCREAMING_SNAKE_CASE)




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

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

												while True:
																	lowercase		      =		min(_SCREAMING_SNAKE_CASE  ,key=lambda A__: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE  ,float('''inf''')))
																	if bigram not in self.bpe_ranks:
																						break
																	lowercase      ,		lowercase		      =		bigram
																	lowercase		      =		[]
																	lowercase		      =		0
																	while i < len(_SCREAMING_SNAKE_CASE):
																						try:
																											lowercase		      =		word.index(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)
																						except ValueError:
																											new_word.extend(word[i:])
																											break
																						else:
																											new_word.extend(word[i:j])
																											lowercase		      =		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
																	lowercase		      =		tuple(_SCREAMING_SNAKE_CASE)
																	lowercase		      =		new_word
																	if len(_SCREAMING_SNAKE_CASE) == 1:
																						break
																	else:
																						lowercase		      =		get_pairs(_SCREAMING_SNAKE_CASE)
												lowercase		      =		''' '''.join(_SCREAMING_SNAKE_CASE)
												if word == "\n  </w>":
																	lowercase		      =		'''\n</w>'''
												lowercase		      =		word
												return word




							def 					A__ (							self  ,A__  ,A__=False):
												if bypass_tokenizer:
																	lowercase		      =		text.split()
												else:
																	lowercase		      =		self.moses_tokenize(_SCREAMING_SNAKE_CASE  ,self.lang)

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

												return split_tokens




							def 					A__ (							self  ,A__):
												return self.encoder.get(_SCREAMING_SNAKE_CASE  ,self.encoder.get(self.unk_token))




							def 					A__ (							self  ,A__):
												return self.decoder.get(_SCREAMING_SNAKE_CASE  ,self.unk_token)




							def 					A__ (							self  ,A__):
												lowercase		      =		[t.replace(''' '''  ,'''''').replace('''</w>'''  ,''' ''') for t in tokens]
												lowercase		      =		''''''.join(_SCREAMING_SNAKE_CASE).split()
												# detokenize
												lowercase		      =		self.moses_detokenize(_SCREAMING_SNAKE_CASE  ,self.lang)
												return text




							def 					A__ (							self  ,A__  ,A__ = None):
												if token_ids_a is None:
																	return [self.sep_token_id] + token_ids_a
												lowercase		      =		[self.sep_token_id]
												return sep + token_ids_a + sep + token_ids_a




							def 					A__ (							self  ,A__  ,A__ = None  ,A__ = False):
												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 [1] + ([0] * len(_SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE))
												return [1] + ([0] * len(_SCREAMING_SNAKE_CASE))




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[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 					A__ (							self  ,A__  ,A__ = None):
												if not os.path.isdir(_SCREAMING_SNAKE_CASE):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase		      =		os.path.join(
												    _SCREAMING_SNAKE_CASE  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
												lowercase		      =		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''')

												lowercase		      =		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 A__: kv[1]):
																						if index != token_index:
																											logger.warning(
																											    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																											    ''' Please check that the tokenizer is not corrupted!''')
																											lowercase		      =		token_index
																						writer.write(''' '''.join(_SCREAMING_SNAKE_CASE) + '''\n''')
																						index += 1

												return vocab_file, merge_file




							def __getstate__(							self):
												lowercase		      =		self.__dict__.copy()
												lowercase		      =		None
												return state





							def __setstate__(							self  ,A__):
												lowercase		      =		d

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

												lowercase		      =		sacremoses








import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: Optional[int]       =					(DPMSolverMultistepScheduler,)
							_lowerCamelCase				: Optional[Any]       =					(("""num_inference_steps""", 25),)





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """beta_schedule""": """linear""",
								    """solver_order""": 2,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								    """sample_max_value""": 1.0,
								    """algorithm_type""": """dpmsolver++""",
								    """solver_type""": """midpoint""",
								    """lower_order_final""": False,
								    """lambda_min_clipped""": -float("""inf"""    ),
								    """variance_type""": None,
								}

								config.update(**_SCREAMING_SNAKE_CASE    )
								return config





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   self.dummy_sample
								a_     			=   0.1 * sample
								a_     			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									# copy over dummy past residuals
									a_     			=   dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(_SCREAMING_SNAKE_CASE    )
										a_     			=   scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
										new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
										# copy over dummy past residuals
										a_     			=   dummy_past_residuals[: new_scheduler.config.solver_order]

									a_			, a_     			=   sample, sample
									for t in range(_SCREAMING_SNAKE_CASE			,       time_step + scheduler.config.solver_order + 1    ):
										a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
										a_     			=   new_scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





							def   __magic_name__      (							self    ):
								pass





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   self.dummy_sample
								a_     			=   0.1 * sample
								a_     			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config()
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

									# copy over dummy past residuals (must be after setting timesteps)
									a_     			=   dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(_SCREAMING_SNAKE_CASE    )
										a_     			=   scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
										# copy over dummy past residuals
										new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

										# copy over dummy past residual (must be after setting timesteps)
										a_     			=   dummy_past_residuals[: new_scheduler.config.solver_order]

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									a_     			=   new_scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=None			,       **_SCREAMING_SNAKE_CASE    ):
								if scheduler is None:
									a_     			=   self.scheduler_classes[0]
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								a_     			=   10
								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

								for i, t in enumerate(scheduler.timesteps    ):
									a_     			=   model(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ).prev_sample

								return sample





							def   __magic_name__      (							self    ):
								a_     			=   dict(self.forward_default_kwargs    )

								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )

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

									a_     			=   self.dummy_sample
									a_     			=   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"""    ):
										a_     			=   num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									a_     			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									a_     			=   dummy_past_residuals[: scheduler.config.solver_order]

									a_     			=   scheduler.timesteps[5]
									a_     			=   scheduler.timesteps[6]

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





							def   __magic_name__      (							self    ):
								for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
								for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       sample_max_value=_SCREAMING_SNAKE_CASE			,       algorithm_type="""dpmsolver++"""			,       solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       )





							def   __magic_name__      (							self    ):
								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
												if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
													if order == 3:
														continue
												else:
													self.check_over_configs(
													    solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       algorithm_type=_SCREAMING_SNAKE_CASE			,       )
												a_     			=   self.full_loop(
												    solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       algorithm_type=_SCREAMING_SNAKE_CASE			,       )
												assert not torch.isnan(_SCREAMING_SNAKE_CASE    ).any(), "Samples have nan numbers"





							def   __magic_name__      (							self    ):
								self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )
								self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(lambda_min_clipped=-float("""inf"""    )    )
								self.check_over_configs(lambda_min_clipped=-5.1    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE    )
								self.check_over_configs(variance_type="""learned_range"""    )





							def   __magic_name__      (							self    ):
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE			,       time_step=0    )





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop()
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(thresholding=_SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0.8_7			,       sample_max_value=0.5    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 1.1_3_6_4    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(prediction_type="""v_prediction"""    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.2_2_5_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(prediction_type="""v_prediction"""			,       use_karras_sigmas=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.2_0_9_6    ) < 1E-3





							def   __magic_name__      (							self    ):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								a_     			=   DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
								a_     			=   self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3

								a_     			=   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
								a_     			=   UniPCMultistepScheduler.from_config(scheduler.config    )
								a_     			=   DEISMultistepScheduler.from_config(scheduler.config    )
								a_     			=   DPMSolverMultistepScheduler.from_config(scheduler.config    )

								a_     			=   self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.scheduler_classes[0]
								a_     			=   self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0    )
								a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								a_     			=   10
								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

								for i, t in enumerate(scheduler.timesteps    ):
									a_     			=   model(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ).prev_sample

								assert sample.dtype == torch.floataa





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

									scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
									assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps