


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

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


_A							     =       logging.get_logger(__name__)

_A							     =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_A							     =       {
    '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'},
}

_A							     =       {
    'microsoft/biogpt': 1024,
}

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Any	)     ->	Optional[int]:



	"""simple docstring"""






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

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





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE="<unk>"			,       _SCREAMING_SNAKE_CASE="<s>"			,       _SCREAMING_SNAKE_CASE="</s>"			,       _SCREAMING_SNAKE_CASE="</s>"			,       _SCREAMING_SNAKE_CASE="<pad>"			,       **_SCREAMING_SNAKE_CASE			,       ):
								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."""    )

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





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





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





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





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

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

								while True:
									a_     			=   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
									a_			, a_     			=   bigram
									a_     			=   []
									a_     			=   0
									while i < len(_SCREAMING_SNAKE_CASE    ):
										try:
											a_     			=   word.index(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
										except ValueError:
											new_word.extend(word[i:]    )
											break
										else:
											new_word.extend(word[i:j]    )
											a_     			=   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
									a_     			=   tuple(_SCREAMING_SNAKE_CASE    )
									a_     			=   new_word
									if len(_SCREAMING_SNAKE_CASE    ) == 1:
										break
									else:
										a_     			=   get_pairs(_SCREAMING_SNAKE_CASE    )
								a_     			=   """ """.join(_SCREAMING_SNAKE_CASE    )
								if word == "\n  </w>":
									a_     			=   """\n</w>"""
								a_     			=   word
								return word





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

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

								return split_tokens





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





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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   [t.replace(""" """			,       """"""    ).replace("""</w>"""			,       """ """    ) for t in tokens]
								a_     			=   """""".join(_SCREAMING_SNAKE_CASE    ).split()
								# detokenize
								a_     			=   self.moses_detokenize(_SCREAMING_SNAKE_CASE			,       self.lang    )
								return text





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = None    ):
								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								a_     			=   [self.sep_token_id]
								return sep + token_ids_a + sep + token_ids_a





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

								a_     			=   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 {merge_file}: BPE merge indices are not consecutive."""
											    """ Please check that the tokenizer is not corrupted!"""    )
											a_     			=   token_index
										writer.write(""" """.join(_SCREAMING_SNAKE_CASE    ) + """\n"""    )
										index += 1

								return vocab_file, merge_file





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





							def __setstate__(							self			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   d

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

								a_     			=   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