


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {'vocab_file': 'spiece.model'}

lowerCamelCase											=       {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

lowerCamelCase											=       {
    'google/bigbird-roberta-base': 40_96,
    'google/bigbird-roberta-large': 40_96,
    'google/bigbird-base-trivia-itc': 40_96,
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  str      =VOCAB_FILES_NAMES
      UpperCamelCase__							:  Dict      =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__							:  List[Any]      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__							:  Optional[Any]      =['input_ids', 'attention_mask']
      UpperCamelCase__							:  List[int]      =[]






      def __init__(					self						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Union[str, Any]="<unk>"			,   lowercase_						:     Union[str, Any]="<s>"			,   lowercase_						:     Union[str, Any]="</s>"			,   lowercase_						:     List[Any]="<pad>"			,   lowercase_						:     Dict="[SEP]"			,   lowercase_						:     Optional[Any]="[MASK]"			,   lowercase_						:     Optional[Any]="[CLS]"			,   lowercase_						:     Optional[Dict[str, Any]] = None			,   **lowercase_						:     str			,   )					->						None:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=AddedToken(lowercase_			,   lstrip=lowercase_			,   rstrip=lowercase_ ) if isinstance(lowercase_			,   lowercase_ ) else bos_token
             _lowerCamelCase       :		Union[str, Any]		=AddedToken(lowercase_			,   lstrip=lowercase_			,   rstrip=lowercase_ ) if isinstance(lowercase_			,   lowercase_ ) else eos_token
             _lowerCamelCase       :		Optional[Any]		=AddedToken(lowercase_			,   lstrip=lowercase_			,   rstrip=lowercase_ ) if isinstance(lowercase_			,   lowercase_ ) else unk_token
             _lowerCamelCase       :		Union[str, Any]		=AddedToken(lowercase_			,   lstrip=lowercase_			,   rstrip=lowercase_ ) if isinstance(lowercase_			,   lowercase_ ) else pad_token
             _lowerCamelCase       :		Dict		=AddedToken(lowercase_			,   lstrip=lowercase_			,   rstrip=lowercase_ ) if isinstance(lowercase_			,   lowercase_ ) else cls_token
             _lowerCamelCase       :		Optional[int]		=AddedToken(lowercase_			,   lstrip=lowercase_			,   rstrip=lowercase_ ) if isinstance(lowercase_			,   lowercase_ ) else sep_token

             # Mask token behave like a normal word, i.e. include the space before it
             _lowerCamelCase       :		Dict		=AddedToken(lowercase_			,   lstrip=lowercase_			,   rstrip=lowercase_ ) if isinstance(lowercase_			,   lowercase_ ) else mask_token

             _lowerCamelCase       :		Optional[int]		={} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=lowercase_			,   eos_token=lowercase_			,   unk_token=lowercase_			,   pad_token=lowercase_			,   sep_token=lowercase_			,   mask_token=lowercase_			,   cls_token=lowercase_			,   sp_model_kwargs=self.sp_model_kwargs			,   **lowercase_			,   )

             _lowerCamelCase       :		List[Any]		=vocab_file

             _lowerCamelCase       :		Optional[int]		=spm.SentencePieceProcessor(**self.sp_model_kwargs )
             self.sp_model.Load(lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     str )					->						Union[str, Any]:
             """simple docstring"""




             return self.sp_model.get_piece_size()






      def        lowerCamelCase       (					self						:     Dict )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		={self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
             vocab.update(self.added_tokens_encoder )
             return vocab






      def __getstate__(					self						:     List[str] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.__dict__.copy()
             _lowerCamelCase       :		int		=None
             return state






      def __setstate__(					self						:     List[str]			,   lowercase_						:     List[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		int		=d

             # for backward compatibility
             if not hasattr(self			,   'sp_model_kwargs' ):
                    _lowerCamelCase       :		List[str]		={}

             _lowerCamelCase       :		Union[str, Any]		=spm.SentencePieceProcessor(**self.sp_model_kwargs )
             self.sp_model.Load(self.vocab_file )






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     str )					->						List[str]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     List[str] )					->						str:
             """simple docstring"""




             return self.sp_model.piece_to_id(lowercase_ )






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     Dict )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.sp_model.IdToPiece(lowercase_ )
             return token






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     str )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=[]
             _lowerCamelCase       :		Optional[Any]		=''
             _lowerCamelCase       :		Optional[Any]		=False
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           if not prev_is_special:
                                  out_string += " "
                           out_string += self.sp_model.decode(lowercase_ ) + token
                           _lowerCamelCase       :		Any		=True
                           _lowerCamelCase       :		List[str]		=[]
                    else:
                           current_sub_tokens.append(lowercase_ )
                           _lowerCamelCase       :		int		=False
             out_string += self.sp_model.decode(lowercase_ )
             return out_string.strip()






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     List[int]			,   lowercase_						:     bool = False			,   lowercase_						:     bool = None			,   lowercase_						:     bool = True			,   **lowercase_						:     Any			,   )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=kwargs.pop('use_source_tokenizer'			,   lowercase_ )

             _lowerCamelCase       :		List[str]		=self.convert_ids_to_tokens(lowercase_			,   skip_special_tokens=lowercase_ )

             # To avoid mixing byte-level and unicode for byte-level BPT
             # we need to build string separately for added tokens and byte-level tokens
             # cf. https://github.com/huggingface/transformers/issues/1133
             _lowerCamelCase       :		Dict		=[]
             _lowerCamelCase       :		Tuple		=[]
             for token in filtered_tokens:
                    if skip_special_tokens and token in self.all_special_ids:
                           continue
                    if token in self.added_tokens_encoder:
                           if current_sub_text:
                                  sub_texts.append(self.convert_tokens_to_string(lowercase_ ) )
                                  _lowerCamelCase       :		str		=[]
                           sub_texts.append(lowercase_ )
                    else:
                           current_sub_text.append(lowercase_ )
             if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(lowercase_ ) )

             # Mimic the behavior of the Rust tokenizer:
             # No space before [MASK] and [SEP]
             if spaces_between_special_tokens:
                    _lowerCamelCase       :		List[Any]		=re.sub(R' (\[(MASK|SEP)\])'			,   R'\1'			,   ' '.join(lowercase_ ) )
             else:
                    _lowerCamelCase       :		Any		=''.join(lowercase_ )

             _lowerCamelCase       :		str		=(
                 clean_up_tokenization_spaces
                 if clean_up_tokenization_spaces is not None
                 else self.clean_up_tokenization_spaces
             )
             if clean_up_tokenization_spaces:
                    _lowerCamelCase       :		Union[str, Any]		=self.clean_up_tokenization(lowercase_ )
                    return clean_text
             else:
                    return text






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     str			,   lowercase_						:     Optional[str] = None )					->						Tuple[str]:
             """simple docstring"""




             if not os.path.isdir(lowercase_ ):
                    logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                    return
             _lowerCamelCase       :		Dict		=os.path.join(
                 lowercase_			,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

             if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
                    copyfile(self.vocab_file			,   lowercase_ )
             elif not os.path.isfile(self.vocab_file ):
                    with open(lowercase_			,   'wb' ) as fi:
                           _lowerCamelCase       :		Tuple		=self.sp_model.serialized_model_proto()
                           fi.write(lowercase_ )

             return (out_vocab_file,)






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     List[int]			,   lowercase_						:     Optional[List[int]] = None )					->						List[int]:
             """simple docstring"""




             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             _lowerCamelCase       :		Tuple		=[self.cls_token_id]
             _lowerCamelCase       :		List[Any]		=[self.sep_token_id]
             return cls + token_ids_a + sep + token_ids_a + sep






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[int]			,   lowercase_						:     Optional[List[int]] = None			,   lowercase_						:     bool = False )					->						List[int]:
             """simple docstring"""




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

             if token_ids_a is None:
                    return [1] + ([0] * len(lowercase_ )) + [1]
             return [1] + ([0] * len(lowercase_ )) + [1] + ([0] * len(lowercase_ )) + [1]






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[int]			,   lowercase_						:     Optional[List[int]] = None )					->						List[int]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=[self.sep_token_id]
             _lowerCamelCase       :		Any		=[self.cls_token_id]
             if token_ids_a is None:
                    return len(cls + token_ids_a + sep ) * [0]
             return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Any      =(DPMSolverSinglestepScheduler,)
      UpperCamelCase__							:  Tuple      =(('num_inference_steps', 25),)






      def        lowerCamelCase       (					self						:     Optional[Any]			,   **lowercase_						:     List[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'solver_order': 2,
                 'prediction_type': 'epsilon',
                 'thresholding': False,
                 'sample_max_value': 1.0,
                 'algorithm_type': 'dpmsolver++',
                 'solver_type': 'midpoint',
                 'lambda_min_clipped': -float('inf' ),
                 'variance_type': None,
             }

             config.update(**lowercase_ )
             return config






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     Optional[Any]=0			,   **lowercase_						:     Any )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Optional[Any]		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Dict		=0.1 * sample
             _lowerCamelCase       :		Optional[int]		=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                    _lowerCamelCase	,     _lowerCamelCase       :		Any		=sample, sample
                    for t in range(lowercase_			,   time_step + scheduler.config.solver_order + 1 ):
                           _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                           _lowerCamelCase       :		Optional[Any]		=new_scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample

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






      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             pass






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str=0			,   **lowercase_						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		int		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Dict		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Any		=0.1 * sample
             _lowerCamelCase       :		int		=[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config()
                    _lowerCamelCase       :		Any		=scheduler_class(**lowercase_ )
                    scheduler.set_timesteps(lowercase_ )

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

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_ )
                           _lowerCamelCase       :		Optional[int]		=scheduler_class.from_pretrained(lowercase_ )
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(lowercase_ )

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

                    _lowerCamelCase       :		Dict		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                    _lowerCamelCase       :		Dict		=new_scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample

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






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any]=None			,   **lowercase_						:     List[str] )					->						str:
             """simple docstring"""




             if scheduler is None:
                    _lowerCamelCase       :		Tuple		=self.scheduler_classes[0]
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config(**lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		List[Any]		=self.scheduler_classes[0]
             _lowerCamelCase       :		Optional[Any]		=self.get_scheduler_config(**lowercase_ )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		str		=10
             _lowerCamelCase       :		Union[str, Any]		=self.dummy_model()
             _lowerCamelCase       :		Dict		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             for i, t in enumerate(scheduler.timesteps ):
                    _lowerCamelCase       :		List[str]		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Optional[int]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             return sample






      def        lowerCamelCase       (					self						:     Dict )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		int		=50
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		Tuple		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             # make sure that the first t is uneven
             for i, t in enumerate(scheduler.timesteps[3:] ):
                    _lowerCamelCase       :		Any		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             _lowerCamelCase       :		int		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2574 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						List[str]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		Optional[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		Tuple		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3

             _lowerCamelCase       :		Dict		=DEISMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Optional[int]		=DPMSolverMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Union[str, Any]		=UniPCMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		List[str]		=DPMSolverSinglestepScheduler.from_config(scheduler.config )

             _lowerCamelCase       :		List[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             self.check_over_configs(thresholding=lowercase_ )
             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=lowercase_			,   prediction_type=lowercase_			,   sample_max_value=lowercase_			,   algorithm_type='dpmsolver++'			,   solver_order=lowercase_			,   solver_type=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """simple docstring"""




             for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                    for solver_type in ["midpoint", "heun"]:
                           for order in [1, 2, 3]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             solver_order=lowercase_			,   solver_type=lowercase_			,   prediction_type=lowercase_			,   algorithm_type=lowercase_			,   )
                                         _lowerCamelCase       :		Any		=self.full_loop(
                                             solver_order=lowercase_			,   solver_type=lowercase_			,   prediction_type=lowercase_			,   algorithm_type=lowercase_			,   )
                                         assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[Any]:
             """simple docstring"""




             self.check_over_configs(lower_order_final=lowercase_ )
             self.check_over_configs(lower_order_final=lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




             self.check_over_configs(lambda_min_clipped=-float('inf' ) )
             self.check_over_configs(lambda_min_clipped=-5.1 )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             self.check_over_configs(variance_type=lowercase_ )
             self.check_over_configs(variance_type='learned_range' )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     str )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.full_loop()
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2248 ) < 1E-3






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.full_loop(prediction_type='v_prediction' )
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.1453 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(prediction_type='v_prediction'			,   use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.0649 ) < 1E-3






      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.scheduler_classes[0]
             _lowerCamelCase       :		List[str]		=self.get_scheduler_config(thresholding=lowercase_			,   dynamic_thresholding_ratio=0 )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		Optional[Any]		=10
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		List[Any]		=self.dummy_sample_deter.half()
             scheduler.set_timesteps(lowercase_ )

             for i, t in enumerate(scheduler.timesteps ):
                    _lowerCamelCase       :		str		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Any		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             assert sample.dtype == torch.floataa


