






'''simple docstring'''


import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


lowerCamelCase_				: Optional[Any]    	=							logging.get_logger(__name__)

lowerCamelCase_				: int    	=							{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

lowerCamelCase_				: Tuple    	=							{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

lowerCamelCase_				: Optional[Any]    	=							{
    '''abeja/gpt-neox-japanese-2.7b''': 2048,
}







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						with open(_lowercase						,      """r"""						,      encoding="""utf-8"""					) as f:
												UpperCamelCase__         = json.loads(f.read()					)

						UpperCamelCase__         = collections.OrderedDict()
						UpperCamelCase__         = collections.OrderedDict()
						UpperCamelCase__         = collections.OrderedDict()
						with open(_lowercase						,      """r"""						,      encoding="""utf-8"""					) as f:
												UpperCamelCase__         = f.readlines()
						UpperCamelCase__         = [[t.rstrip("""\n"""					)] if (t == ',' or ',' not in t) else t.rstrip("""\n"""					).split(""","""					) for t in token]
						for idx, b in enumerate(_lowercase					):
												UpperCamelCase__         = b
												UpperCamelCase__         = idx
												for wd in b:
																		UpperCamelCase__         = idx

						return vocab, raw_vocab, ids_to_tokens, emoji
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							List[str]					     =					VOCAB_FILES_NAMES
			__a							:							Any					     =					PRETRAINED_VOCAB_FILES_MAP
			__a							:							Union[str, Any]					     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a							:							Union[str, Any]					     =					["input_ids", "attention_mask"]






			def __init__(   self							:						List[Any] ,      lowercase							:						Tuple ,      lowercase							:						Optional[int] ,      lowercase							:						List[Any]="<|endoftext|>" ,      lowercase							:						Union[str, Any]="<|endoftext|>" ,      lowercase							:						Dict="<|startoftext|>" ,      lowercase							:						str="<|endoftext|>" ,      lowercase							:						int=False ,      **lowercase							:						str ,      )							->    Tuple:

									'''simple docstring'''







									super().__init__(
									    unk_token=UpperCamelCase_ ,      pad_token=UpperCamelCase_ ,      bos_token=UpperCamelCase_ ,      eos_token=UpperCamelCase_ ,      do_clean_text=UpperCamelCase_ ,      **UpperCamelCase_ ,      )
									if not os.path.isfile(UpperCamelCase_			):
															raise ValueError(
															    f"Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained"
															    """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""			)
									if not os.path.isfile(UpperCamelCase_			):
															raise ValueError(
															    f"Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google"
															    """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""			)
									UpperCamelCase__         = do_clean_text
									UpperCamelCase__         = load_vocab_and_emoji(UpperCamelCase_ ,      UpperCamelCase_			)
									UpperCamelCase__         = SubWordJapaneseTokenizer(
									    vocab=self.vocab ,      ids_to_tokens=self.ids_to_tokens ,      emoji=self.emoji			)






			@property
			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									return len(self.raw_vocab			)






			def 				A      (   self							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







									return dict(self.raw_vocab ,      **self.added_tokens_encoder			)






			def 				A      (   self							:						Dict ,      lowercase							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									return self.subword_tokenizer.tokenize(UpperCamelCase_ ,      clean=self.do_clean_text			)






			def 				A      (   self							:						List[str] ,      lowercase							:						Any			)							->    str:

									'''simple docstring'''







									return self.vocab.get(UpperCamelCase_ ,      self.vocab.get(self.unk_token			)			)






			def 				A      (   self							:						int ,      lowercase							:						Tuple			)							->    Union[str, Any]:

									'''simple docstring'''







									return self.subword_tokenizer.convert_id_to_token(UpperCamelCase_			)






			def 				A      (   self							:						Any ,      lowercase							:						List[Any]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = ''.join(UpperCamelCase_			).strip()
									return out_string






			def 				A      (   self							:						Optional[Any] ,      lowercase							:						"Conversation"			)							->    List[int]:

									'''simple docstring'''







									UpperCamelCase__         = []
									for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(UpperCamelCase_ ,      add_special_tokens=UpperCamelCase_			) + [self.eos_token_id]			)

									if len(UpperCamelCase_			) > self.model_max_length:
															UpperCamelCase__         = input_ids[-self.model_max_length :]
									return input_ids







			def 				A      (   self							:						Optional[Any] ,      lowercase							:						str ,      lowercase							:						Optional[str] = None			)							->    Tuple[str]:

									'''simple docstring'''







									UpperCamelCase__         = 0
									if os.path.isdir(UpperCamelCase_			):
															UpperCamelCase__         = os.path.join(
															    UpperCamelCase_ ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)
															UpperCamelCase__         = os.path.join(
															    UpperCamelCase_ ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]			)
									else:
															UpperCamelCase__         = (
															    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
															)
															UpperCamelCase__         = (
															    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
															)
									with open(UpperCamelCase_ ,      """w""" ,      encoding="""utf-8"""			) as writer:
															for token_index, token in self.ids_to_tokens.items():
																					if index != token_index:
																											logger.warning(
																											    f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
																											    """ Please check that the vocabulary is not corrupted!"""			)
																											UpperCamelCase__         = token_index
																					writer.write(""",""".join(UpperCamelCase_			) + """\n"""			)
																					index += 1
									with open(UpperCamelCase_ ,      """w""" ,      encoding="""utf-8"""			) as writer:
															json.dump(self.emoji ,      UpperCamelCase_			)
									return vocab_file, emoji_file
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						int ,      lowercase							:						List[str] ,      lowercase							:						str ,      lowercase							:						Tuple			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = vocab  # same as swe
									UpperCamelCase__         = ids_to_tokens  # same as bpe
									UpperCamelCase__         = emoji
									UpperCamelCase__         = np.max([len(UpperCamelCase_			) for w in self.vocab.keys()]			)
									UpperCamelCase__         = re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"""			)
									UpperCamelCase__         = re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"""			)
									UpperCamelCase__         = re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"""			)
									UpperCamelCase__         = re.compile(
									    r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""			)
									UpperCamelCase__         = re.compile(
									    r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""			)
									UpperCamelCase__         = re.compile(
									    r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"""			)
									UpperCamelCase__         = '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
									UpperCamelCase__         = '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
									UpperCamelCase__         = str.maketrans({k: """<BLOCK>""" for k in keisen + blocks}			)






			def __len__(   self							:						str			)							->    str:

									'''simple docstring'''







									return len(self.ids_to_tokens			)






			def 				A      (   self							:						List[Any] ,      lowercase							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = self.content_repattera.sub("""<URL>""" ,      UpperCamelCase_			)
									UpperCamelCase__         = self.content_repattera.sub("""<EMAIL>""" ,      UpperCamelCase_			)
									UpperCamelCase__         = self.content_repattera.sub("""<TEL>""" ,      UpperCamelCase_			)
									UpperCamelCase__         = self.content_repattera.sub("""<DATE>""" ,      UpperCamelCase_			)
									UpperCamelCase__         = self.content_repattera.sub("""<DATE>""" ,      UpperCamelCase_			)
									UpperCamelCase__         = self.content_repattera.sub("""<PRICE>""" ,      UpperCamelCase_			)
									UpperCamelCase__         = content.translate(self.content_transa			)
									while "<BLOCK><BLOCK>" in content:
															UpperCamelCase__         = content.replace("""<BLOCK><BLOCK>""" ,      """<BLOCK>"""			)
									return content






			def 				A      (   self							:						Tuple ,      lowercase							:						Tuple ,      lowercase							:						Any=False			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = text.replace(""" """ ,      """<SP>"""			)
									UpperCamelCase__         = text.replace("""　""" ,      """<SP>"""			)
									UpperCamelCase__         = text.replace("""\r\n""" ,      """<BR>"""			)
									UpperCamelCase__         = text.replace("""\n""" ,      """<BR>"""			)
									UpperCamelCase__         = text.replace("""\r""" ,      """<BR>"""			)
									UpperCamelCase__         = text.replace("""\t""" ,      """<TAB>"""			)
									UpperCamelCase__         = text.replace("""—""" ,      """ー"""			)
									UpperCamelCase__         = text.replace("""−""" ,      """ー"""			)
									for k, v in self.emoji["emoji"].items():
															if k in text:
																					UpperCamelCase__         = text.replace(UpperCamelCase_ ,      UpperCamelCase_			)
									if clean:
															UpperCamelCase__         = self.clean_text(UpperCamelCase_			)

									def check_simbol(lowercase							:						Dict			):
															UpperCamelCase__         = x.encode()
															if len(UpperCamelCase_			) == 1 and len(UpperCamelCase_			) == 2:
																					UpperCamelCase__         = (int(e[0]			) << 8) + int(e[1]			)
																					if (
																					    (c >= 0xC2A1 and c <= 0xC2BF)
																					    or (c >= 0xC780 and c <= 0xC783)
																					    or (c >= 0xCAB9 and c <= 0xCBBF)
																					    or (c >= 0xCC80 and c <= 0xCDA2)
																					):
																											return True
															return False

									def checkuae(lowercase							:						Any			):
															UpperCamelCase__         = x.encode()
															if len(UpperCamelCase_			) == 1 and len(UpperCamelCase_			) == 3:
																					UpperCamelCase__         = (int(e[0]			) << 1_6) + (int(e[1]			) << 8) + int(e[2]			)
																					if c >= 0xE28080 and c <= 0xE2B07F:
																											return True
															return False

									UpperCamelCase__         = 0
									UpperCamelCase__         = []
									while pos < len(UpperCamelCase_			):
															UpperCamelCase__         = min(len(UpperCamelCase_			) ,      pos + self.maxlen + 1			) if text[pos] == '<' else pos + 3
															UpperCamelCase__         = []  # (token_id, token, pos)
															for e in range(UpperCamelCase_ ,      UpperCamelCase_ ,      -1			):
																					UpperCamelCase__         = text[pos:e]
																					if wd in self.vocab:
																											if wd[0] == "<" and len(UpperCamelCase_			) > 2:
																																	UpperCamelCase__         = [(self.vocab[wd], wd, e)]
																																	break
																											else:
																																	candidates.append((self.vocab[wd], wd, e)			)
															if len(UpperCamelCase_			) > 0:
																					# the smallest token_id is adopted
																					UpperCamelCase__         = sorted(UpperCamelCase_ ,      key=lambda lowercase			: x[0]			)[0]
																					result.append(UpperCamelCase_			)
																					UpperCamelCase__         = e
															else:
																					UpperCamelCase__         = pos + 1
																					UpperCamelCase__         = text[pos:end]
																					if check_simbol(UpperCamelCase_			):
																											result.append("""<KIGOU>"""			)
																					elif checkuae(UpperCamelCase_			):
																											result.append("""<U2000U2BFF>"""			)
																					else:
																											for i in wd.encode("""utf-8"""			):
																																	result.append("""<|byte%d|>""" % i			)
																					UpperCamelCase__         = end
									return result







			def 				A      (   self							:						Tuple ,      lowercase							:						int ,      lowercase							:						List[str]="\n"			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = []
									UpperCamelCase__         = []
									UpperCamelCase__         = self.ids_to_tokens[index][0]
									if word[:6] == "<|byte" and word[-2:] == "|>":
															byte_tokens.append(int(word[6:-2]			)			)
									else:
															if len(UpperCamelCase_			) > 0:
																					words.append(bytearray(UpperCamelCase_			).decode("""utf-8""" ,      errors="""replace"""			)			)
																					UpperCamelCase__         = []
															if word[:7] == "<|emoji" and word[-2:] == "|>":
																					words.append(self.emoji["""emoji_inv"""][word]			)
															elif word == "<SP>":
																					words.append(""" """			)
															elif word == "<BR>":
																					words.append(UpperCamelCase_			)
															elif word == "<TAB>":
																					words.append("""\t"""			)
															elif word == "<BLOCK>":
																					words.append("""▀"""			)
															elif word == "<KIGOU>":
																					words.append("""ǀ"""			)
															elif word == "<U2000U2BFF>":
																					words.append("""‖"""			)
															else:
																					words.append(UpperCamelCase_			)
									if len(UpperCamelCase_			) > 0:
															words.append(bytearray(UpperCamelCase_			).decode("""utf-8""" ,      errors="""replace"""			)			)
									UpperCamelCase__         = ''.join(UpperCamelCase_			)
									return text












'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Dict					     =					(DPMSolverSinglestepScheduler,)
			__a							:							List[Any]					     =					(("num_inference_steps", 25),)






			def 				A      (   self							:						List[Any] ,      **lowercase							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = {
									    """num_train_timesteps""": 1_0_0_0,
									    """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""",
									    """lambda_min_clipped""": -float("""inf"""			),
									    """variance_type""": None,
									}

									config.update(**lowercase			)
									return config






			def 				A      (   self							:						List[Any] ,      lowercase							:						Tuple=0 ,      **lowercase							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									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 + 0.1_5, residual + 0.1_0]

									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 				A      (   self							:						List[str]			)							->    Any:

									'''simple docstring'''







									pass






			def 				A      (   self							:						Any ,      lowercase							:						List[Any]=0 ,      **lowercase							:						Optional[int]			)							->    Optional[int]:

									'''simple docstring'''







									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 + 0.1_5, residual + 0.1_0]

									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 				A      (   self							:						Union[str, Any] ,      lowercase							:						Any=None ,      **lowercase							:						List[Any]			)							->    Dict:

									'''simple docstring'''







									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__         = 1_0
									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 				A      (   self							:						Any			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
									UpperCamelCase__         = 5_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter
									scheduler.set_timesteps(lowercase			)

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_5_7_4			) < 1e-3






			def 				A      (   self							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







									for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=lowercase			)






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

									'''simple docstring'''







									UpperCamelCase__         = DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
									UpperCamelCase__         = self.full_loop(scheduler=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3

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

									UpperCamelCase__         = self.full_loop(scheduler=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3






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

									'''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 				A      (   self							:						Optional[int]			)							->    Tuple:

									'''simple docstring'''







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






			def 				A      (   self							:						Optional[int]			)							->    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 ,      )
																																	UpperCamelCase__         = 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 				A      (   self							:						List[str]			)							->    Union[str, Any]:

									'''simple docstring'''







									self.check_over_configs(lower_order_final=lowercase			)
									self.check_over_configs(lower_order_final=lowercase			)






			def 				A      (   self							:						Tuple			)							->    Optional[int]:

									'''simple docstring'''







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






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

									'''simple docstring'''







									self.check_over_configs(variance_type=lowercase			)
									self.check_over_configs(variance_type="""learned_range"""			)






			def 				A      (   self							:						Tuple			)							->    int:

									'''simple docstring'''







									for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_forward(num_inference_steps=lowercase ,      time_step=0			)






			def 				A      (   self							:						List[str]			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop()
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(use_karras_sigmas=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_2_4_8			) < 1e-3






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

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(prediction_type="""v_prediction"""			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.1_4_5_3			) < 1e-3






			def 				A      (   self							:						int			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(prediction_type="""v_prediction""" ,      use_karras_sigmas=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.0_6_4_9			) < 1e-3







			def 				A      (   self							:						Optional[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = self.scheduler_classes[0]
									UpperCamelCase__         = self.get_scheduler_config(thresholding=lowercase ,      dynamic_thresholding_ratio=0			)
									UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = 1_0
									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
