
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_lowerCamelCase   :				Optional[int]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Tuple     			=						'''▁'''

_lowerCamelCase   :				List[str]     			=						{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

_lowerCamelCase   :				Any     			=						{
    '''facebook/s2t-small-librispeech-asr''': 10_24,
}

_lowerCamelCase   :				Optional[int]     			=						['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

_lowerCamelCase   :				Union[str, Any]     			=						{'''mustc''': MUSTC_LANGS}


class   lowercase      (       __SCREAMING_SNAKE_CASE       ):
							lowercase__  :    Tuple									=							VOCAB_FILES_NAMES
							lowercase__  :    Any									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    List[Any]									=							MAX_MODEL_INPUT_SIZES
							lowercase__  :    str									=							["input_ids", "attention_mask"]

							lowercase__  :    List[int]									=							[]




							def __init__(  self				:   str		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   int="<s>"		,							_UpperCamelCase				:   Optional[int]="</s>"		,							_UpperCamelCase				:   Tuple="<pad>"		,							_UpperCamelCase				:   List[str]="<unk>"		,							_UpperCamelCase				:   Optional[int]=False		,							_UpperCamelCase				:   Dict=False		,							_UpperCamelCase				:   Union[str, Any]=None		,							_UpperCamelCase				:   Dict=None		,							_UpperCamelCase				:   List[Any] = None		,							**_UpperCamelCase				:   Dict		,							)   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=__UpperCAmelCase		,							eos_token=__UpperCAmelCase		,							unk_token=__UpperCAmelCase		,							pad_token=__UpperCAmelCase		,							do_upper_case=__UpperCAmelCase		,							do_lower_case=__UpperCAmelCase		,							tgt_lang=__UpperCAmelCase		,							lang_codes=__UpperCAmelCase		,							sp_model_kwargs=self.sp_model_kwargs		,							**__UpperCAmelCase		,							)
												SCREAMING_SNAKE_CASE           =    do_upper_case
												SCREAMING_SNAKE_CASE           =    do_lower_case

												SCREAMING_SNAKE_CASE           =    load_json(__UpperCAmelCase )
												SCREAMING_SNAKE_CASE           =    {v: k for k, v in self.encoder.items()}
												SCREAMING_SNAKE_CASE           =    spm_file
												SCREAMING_SNAKE_CASE           =    load_spm(__UpperCAmelCase		,							self.sp_model_kwargs )

												if lang_codes is not None:
																	SCREAMING_SNAKE_CASE           =    lang_codes
																	SCREAMING_SNAKE_CASE           =    LANGUAGES[lang_codes]
																	SCREAMING_SNAKE_CASE           =    [F"<lang:{lang}>" for lang in self.langs]
																	SCREAMING_SNAKE_CASE           =    {lang: self.sp_model.PieceToId(F"<lang:{lang}>" ) for lang in self.langs}

																	SCREAMING_SNAKE_CASE           =    self.lang_tokens
																	SCREAMING_SNAKE_CASE           =    tgt_lang if tgt_lang is not None else self.langs[0]

																	self.set_tgt_lang_special_tokens(self._tgt_lang )
												else:
																	SCREAMING_SNAKE_CASE           =    {}




							@property
							def 		__snake_case(  self				:   List[Any] )   ->			int:



												'''simple docstring'''

												return len(self.encoder )




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



												'''simple docstring'''

												return self._tgt_lang




							@tgt_lang.setter
							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Dict )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    new_tgt_lang
												self.set_tgt_lang_special_tokens(__UpperCAmelCase )




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[str] )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.lang_code_to_id[tgt_lang]
												SCREAMING_SNAKE_CASE           =    [lang_code_id]




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Union[str, Any] )   ->			List[str]:



												'''simple docstring'''

												return self.sp_model.encode(__UpperCAmelCase		,							out_type=__UpperCAmelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   int )   ->			Any:



												'''simple docstring'''

												return self.encoder.get(__UpperCAmelCase		,							self.encoder[self.unk_token] )




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   str )   ->			str:



												'''simple docstring'''

												return self.decoder.get(__UpperCAmelCase		,							self.unk_token )




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    """"""
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						SCREAMING_SNAKE_CASE           =    self.sp_model.decode(__UpperCAmelCase )
																						out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																						SCREAMING_SNAKE_CASE           =    []
																	else:
																						current_sub_tokens.append(__UpperCAmelCase )
												SCREAMING_SNAKE_CASE           =    self.sp_model.decode(__UpperCAmelCase )
												out_string += decoded.upper() if self.do_upper_case else decoded
												return out_string.strip()




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Union[str, Any]=None )   ->			List[int]:



												'''simple docstring'''

												if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + [self.eos_token_id]
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Union[str, Any] = None		,							_UpperCamelCase				:   Dict = False )   ->			List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=__UpperCAmelCase		,							token_ids_a=__UpperCAmelCase		,							already_has_special_tokens=__UpperCAmelCase )

												SCREAMING_SNAKE_CASE           =    [1] * len(self.prefix_tokens )
												SCREAMING_SNAKE_CASE           =    [1]
												if token_ids_a is None:
																	return prefix_ones + ([0] * len(__UpperCAmelCase )) + suffix_ones
												return prefix_ones + ([0] * len(__UpperCAmelCase )) + ([0] * len(__UpperCAmelCase )) + suffix_ones




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.encoder.copy()
												vocab.update(self.added_tokens_encoder )
												return vocab




							def __getstate__(  self				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

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




							def __setstate__(  self				:   List[str]		,							_UpperCamelCase				:   str )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    d

												# for backward compatibility
												if not hasattr(self		,							"sp_model_kwargs" ):
																	SCREAMING_SNAKE_CASE           =    {}

												SCREAMING_SNAKE_CASE           =    load_spm(self.spm_file		,							self.sp_model_kwargs )





							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   str = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    Path(__UpperCAmelCase )
												assert save_dir.is_dir(), F"{save_directory} should be a directory"
												SCREAMING_SNAKE_CASE           =    save_dir / (
												    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
												)
												SCREAMING_SNAKE_CASE           =    save_dir / (
												    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
												)

												save_json(self.encoder		,							__UpperCAmelCase )

												if os.path.abspath(self.spm_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.spm_file ):
																	copyfile(self.spm_file		,							__UpperCAmelCase )
												elif not os.path.isfile(self.spm_file ):
																	with open(__UpperCAmelCase		,							"wb" ) as fi:
																						SCREAMING_SNAKE_CASE           =    self.sp_model.serialized_model_proto()
																						fi.write(__UpperCAmelCase )

												return (str(__UpperCAmelCase ), str(__UpperCAmelCase ))





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict[str, Any]					):
					SCREAMING_SNAKE_CASE           =    sentencepiece.SentencePieceProcessor(**lowerCAmelCase__					)
					spm.Load(str(lowerCAmelCase__					)					)
					return spm





def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):





					with open(lowerCAmelCase__ ,	"r"					) as f:
										return json.load(lowerCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							str					):





					with open(lowerCAmelCase__ ,	"w"					) as f:
										json.dump(lowerCAmelCase__ ,	lowerCAmelCase__ ,	indent=2					)





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class   lowercase      (       a       ):
							lowercase__  :    Dict									=							(UniPCMultistepScheduler,)
							lowercase__  :    Optional[int]									=							(("""num_inference_steps""", 25),)




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {
												    "num_train_timesteps": 1_000,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												    "solver_order": 2,
												    "solver_type": "bh2",
												}

												config.update(**_UpperCamelCase )
												return config




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Dict=0		,							**_UpperCamelCase				:   List[str] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Union[str, Any]=0		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )
																	scheduler.set_timesteps(_UpperCamelCase )

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

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

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

																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    new_scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Tuple=None		,							**_UpperCamelCase				:   List[Any] )   ->			str:



												'''simple docstring'''

												if scheduler is None:
																	SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter
												scheduler.set_timesteps(_UpperCamelCase )

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

												return sample




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )

												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	SCREAMING_SNAKE_CASE           =    self.dummy_sample
																	SCREAMING_SNAKE_CASE           =    0.1 * sample

																	if num_inference_steps is not None and hasattr(_UpperCamelCase		,							"set_timesteps" ):
																						scheduler.set_timesteps(_UpperCamelCase )
																	elif num_inference_steps is not None and not hasattr(_UpperCamelCase		,							"set_timesteps" ):
																						SCREAMING_SNAKE_CASE           =    num_inference_steps

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

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

																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   str )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    UniPCMultistepScheduler(**self.get_scheduler_config() )
												SCREAMING_SNAKE_CASE           =    self.full_loop(scheduler=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

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

												SCREAMING_SNAKE_CASE           =    self.full_loop(scheduler=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




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



												'''simple docstring'''

												for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												self.check_over_configs(thresholding=_UpperCamelCase )
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							sample_max_value=_UpperCamelCase		,							solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Dict )   ->			int:



												'''simple docstring'''

												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							)
																											SCREAMING_SNAKE_CASE           =    self.full_loop(
																											    solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							)
																											assert not torch.isnan(_UpperCamelCase ).any(), "Samples have nan numbers"




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



												'''simple docstring'''

												self.check_over_configs(lower_order_final=_UpperCamelCase )
												self.check_over_configs(lower_order_final=_UpperCamelCase )




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



												'''simple docstring'''

												for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=_UpperCamelCase		,							time_step=0 )




							def 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop()
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(prediction_type="v_prediction" )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3




							def 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(thresholding=_UpperCamelCase		,							dynamic_thresholding_ratio=0 )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter.half()
												scheduler.set_timesteps(_UpperCamelCase )

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

												assert sample.dtype == torch.floataa





							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

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




