



"""simple docstring"""


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

import regex as re

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


snake_case_    					=	logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
snake_case_    					=	{
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

snake_case_    					=	{
    """facebook/bart-base""": 1024,
    """facebook/bart-large""": 1024,
    """facebook/bart-large-mnli""": 1024,
    """facebook/bart-large-cnn""": 1024,
    """facebook/bart-large-xsum""": 1024,
    """yjernite/bart_eli5""": 1024,
}

@lru_cache()
def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			(
						    list(range(ord('!'						)		,     ord('~'						) + 1						)						) + list(range(ord('¡'						)		,     ord('¬'						) + 1						)						) + list(range(ord('®'						)		,     ord('ÿ'						) + 1						)						)
						)
						UpperCAmelCase					=			bs[:]
						UpperCAmelCase					=			0
						for b in range(2**8						):
												if b not in bs:
																		bs.append(lowercase_						)
																		cs.append(2**8 + n						)
																		n += 1
						UpperCAmelCase					=			[chr(lowercase_						) for n in cs]
						return dict(zip(lowercase_		,     lowercase_						)						)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			set()
						UpperCAmelCase					=			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)						)
												UpperCAmelCase					=			char
						return pairs







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							["""input_ids""", """attention_mask"""]






						def __init__( self			:str       ,  lowercase_			:List[Any]       ,  lowercase_			:Optional[int]       ,  lowercase_			:Dict="replace"       ,  lowercase_			:int="<s>"       ,  lowercase_			:int="</s>"       ,  lowercase_			:str="</s>"       ,  lowercase_			:Any="<s>"       ,  lowercase_			:str="<unk>"       ,  lowercase_			:Optional[int]="<pad>"       ,  lowercase_			:Dict="<mask>"       ,  lowercase_			:Union[str, Any]=False       ,  **lowercase_			:Optional[int]       ,  )    ->      List[Any]:
												UpperCAmelCase					=			AddedToken(lowercase_       ,  lstrip=lowercase_       ,  rstrip=lowercase_ ) if isinstance(lowercase_       ,  lowercase_ ) else bos_token
												UpperCAmelCase					=			AddedToken(lowercase_       ,  lstrip=lowercase_       ,  rstrip=lowercase_ ) if isinstance(lowercase_       ,  lowercase_ ) else eos_token
												UpperCAmelCase					=			AddedToken(lowercase_       ,  lstrip=lowercase_       ,  rstrip=lowercase_ ) if isinstance(lowercase_       ,  lowercase_ ) else sep_token
												UpperCAmelCase					=			AddedToken(lowercase_       ,  lstrip=lowercase_       ,  rstrip=lowercase_ ) if isinstance(lowercase_       ,  lowercase_ ) else cls_token
												UpperCAmelCase					=			AddedToken(lowercase_       ,  lstrip=lowercase_       ,  rstrip=lowercase_ ) if isinstance(lowercase_       ,  lowercase_ ) else unk_token
												UpperCAmelCase					=			AddedToken(lowercase_       ,  lstrip=lowercase_       ,  rstrip=lowercase_ ) if isinstance(lowercase_       ,  lowercase_ ) else pad_token

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

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

												with open(lowercase_       ,  encoding='utf-8' ) as vocab_handle:
																		UpperCAmelCase					=			json.load(lowercase_ )
												UpperCAmelCase					=			{v: k for k, v in self.encoder.items()}
												UpperCAmelCase					=			errors  # how to handle errors in decoding
												UpperCAmelCase					=			bytes_to_unicode()
												UpperCAmelCase					=			{v: k for k, v in self.byte_encoder.items()}
												with open(lowercase_       ,  encoding='utf-8' ) as merges_handle:
																		UpperCAmelCase					=			merges_handle.read().split('\n' )[1:-1]
												UpperCAmelCase					=			[tuple(merge.split() ) for merge in bpe_merges]
												UpperCAmelCase					=			dict(zip(lowercase_       ,  range(len(lowercase_ ) ) ) )
												UpperCAmelCase					=			{}
												UpperCAmelCase					=			add_prefix_space

												# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
												UpperCAmelCase					=			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )






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






						def      UpperCAmelCase__					( self			:str )    ->      Tuple:
												return dict(self.encoder       ,  **self.added_tokens_encoder )






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:Dict )    ->      List[str]:
												if token in self.cache:
																		return self.cache[token]
												UpperCAmelCase					=			tuple(lowercase_ )
												UpperCAmelCase					=			get_pairs(lowercase_ )

												if not pairs:
																		return token

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

																								if word[i] == first and i < len(lowercase_ ) - 1 and word[i + 1] == second:
																														new_word.append(first + second )
																														i += 2
																								else:
																														new_word.append(word[i] )
																														i += 1
																		UpperCAmelCase					=			tuple(lowercase_ )
																		UpperCAmelCase					=			new_word
																		if len(lowercase_ ) == 1:
																								break
																		else:
																								UpperCAmelCase					=			get_pairs(lowercase_ )
												UpperCAmelCase					=			' '.join(lowercase_ )
												UpperCAmelCase					=			word
												return word






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:List[Any] )    ->      Tuple:
												UpperCAmelCase					=			[]
												for token in re.findall(self.pat       ,  lowercase_ ):
																		UpperCAmelCase					=			''.join(
																		    self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																		bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_ ).split(' ' ) )
												return bpe_tokens






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:Dict )    ->      Dict:
												return self.encoder.get(lowercase_       ,  self.encoder.get(self.unk_token ) )






						def      UpperCAmelCase__					( self			:str       ,  lowercase_			:str )    ->      Any:
												return self.decoder.get(lowercase_ )






						def      UpperCAmelCase__					( self			:Any       ,  lowercase_			:Any )    ->      Union[str, Any]:
												UpperCAmelCase					=			''.join(lowercase_ )
												UpperCAmelCase					=			bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'       ,  errors=self.errors )
												return text






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:str       ,  lowercase_			:Optional[str] = None )    ->      Tuple[str]:
												if not os.path.isdir(lowercase_ ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
																		return
												UpperCAmelCase					=			os.path.join(
												    lowercase_       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
												UpperCAmelCase					=			os.path.join(
												    lowercase_       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

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

												UpperCAmelCase					=			0
												with open(lowercase_       ,  'w'       ,  encoding='utf-8' ) as writer:
																		writer.write('#version: 0.2\n' )
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,  key=lambda lowercase_ : 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!' )
																														UpperCAmelCase					=			token_index
																								writer.write(' '.join(lowercase_ ) + '\n' )
																								index += 1

												return vocab_file, merge_file






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:List[int]       ,  lowercase_			:Optional[List[int]] = None )    ->      List[int]:
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												UpperCAmelCase					=			[self.cls_token_id]
												UpperCAmelCase					=			[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:List[int]       ,  lowercase_			:Optional[List[int]] = None       ,  lowercase_			:bool = False )    ->      List[int]:
												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, 1] + ([0] * len(lowercase_ )) + [1]






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:List[int]       ,  lowercase_			:Optional[List[int]] = None )    ->      List[int]:
												UpperCAmelCase					=			[self.sep_token_id]
												UpperCAmelCase					=			[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 + sep + token_ids_a + sep ) * [0]






						def      UpperCAmelCase__					( self			:Any       ,  lowercase_			:List[str]       ,  lowercase_			:Optional[Any]=False       ,  **lowercase_			:List[Any] )    ->      Any:
												UpperCAmelCase					=			kwargs.pop('add_prefix_space'       ,  self.add_prefix_space )
												if (is_split_into_words or add_prefix_space) and (len(lowercase_ ) > 0 and not text[0].isspace()):
																		UpperCAmelCase					=			' ' + text
												return (text, kwargs)










"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							(UniPCMultistepScheduler,)
						__UpperCamelCase		    =							(("""num_inference_steps""", 25),)






						def      UpperCAmelCase__					( self			:Any       ,  **lowercase_			:Optional[Any] )    ->      Dict:
												UpperCAmelCase					=			{
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'solver_order': 2,
												    'solver_type': 'bh2',
												}

												config.update(**lowercase_ )
												return config






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Optional[int]=0       ,  **lowercase_			:Optional[Any] )    ->      List[str]:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )
												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )
												UpperCAmelCase					=			self.dummy_sample
												UpperCAmelCase					=			0.1 * sample
												UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

																		UpperCAmelCase				,   UpperCAmelCase					=			sample, sample
																		for t in range(lowercase_       ,  time_step + scheduler.config.solver_order + 1 ):
																								UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																								UpperCAmelCase					=			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      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Dict=0       ,  **lowercase_			:Optional[int] )    ->      List[str]:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )
												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )
												UpperCAmelCase					=			self.dummy_sample
												UpperCAmelCase					=			0.1 * sample
												UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config()
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )
																		scheduler.set_timesteps(lowercase_ )

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

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(lowercase_ )
																								UpperCAmelCase					=			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)
																								UpperCAmelCase					=			dummy_past_residuals[: new_scheduler.config.solver_order]

																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																		UpperCAmelCase					=			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      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Optional[int]=None       ,  **lowercase_			:str )    ->      Dict:
												if scheduler is None:
																		UpperCAmelCase					=			self.scheduler_classes[0]
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			self.scheduler_classes[0]
												UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
												UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			10
												UpperCAmelCase					=			self.dummy_model()
												UpperCAmelCase					=			self.dummy_sample_deter
												scheduler.set_timesteps(lowercase_ )

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

												return sample






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )

												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config()
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

																		UpperCAmelCase					=			self.dummy_sample
																		UpperCAmelCase					=			0.1 * sample

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

																		# copy over dummy past residuals (must be done after set_timesteps)
																		UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

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

																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												UpperCAmelCase					=			UniPCMultistepScheduler(**self.get_scheduler_config() )
												UpperCAmelCase					=			self.full_loop(scheduler=lowercase_ )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3

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

												UpperCAmelCase					=			self.full_loop(scheduler=lowercase_ )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																		self.check_over_configs(num_train_timesteps=lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      str:
												self.check_over_configs(thresholding=lowercase_ )
												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=lowercase_       ,  prediction_type=lowercase_       ,  sample_max_value=lowercase_       ,  solver_order=lowercase_       ,  solver_type=lowercase_       ,  )






						def      UpperCAmelCase__					( self			:Any )    ->      str:
												for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Optional[Any]:
												for solver_type in ["bh1", "bh2"]:
																		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_       ,  )
																														UpperCAmelCase					=			self.full_loop(
																														    solver_order=lowercase_       ,  solver_type=lowercase_       ,  prediction_type=lowercase_       ,  )
																														assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"






						def      UpperCAmelCase__					( self			:Dict )    ->      List[Any]:
												self.check_over_configs(lower_order_final=lowercase_ )
												self.check_over_configs(lower_order_final=lowercase_ )






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																		self.check_over_forward(num_inference_steps=lowercase_       ,  time_step=0 )






						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.full_loop()
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      str:
												UpperCAmelCase					=			self.full_loop(prediction_type='v_prediction' )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.1014 ) < 1E-3






						def      UpperCAmelCase__					( self			:Tuple )    ->      Tuple:
												UpperCAmelCase					=			self.scheduler_classes[0]
												UpperCAmelCase					=			self.get_scheduler_config(thresholding=lowercase_       ,  dynamic_thresholding_ratio=0 )
												UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			10
												UpperCAmelCase					=			self.dummy_model()
												UpperCAmelCase					=			self.dummy_sample_deter.half()
												scheduler.set_timesteps(lowercase_ )

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

												assert sample.dtype == torch.floataa






						def      UpperCAmelCase__					( self			:List[Any]       ,  **lowercase_			:Optional[Any] )    ->      Dict:
												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

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





