import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowercase__    :       Dict						=			logging.get_logger(__name__)

lowercase__    :       Optional[int]						=			{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

lowercase__    :       str						=			{
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
}

lowercase__    :       List[Any]						=			{
    '''openai-gpt''': 5_1_2,
}

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Union[str, Any]:
							lowerCAmelCase				=	text.strip()
							if not text:
														return []
							lowerCAmelCase				=	text.split()
							return tokens




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=True	,    )		->str:
								if never_split is None:
															lowerCAmelCase				=	[]
								lowerCAmelCase				=	do_lower_case
								lowerCAmelCase				=	set(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	tokenize_chinese_chars
								lowerCAmelCase				=	strip_accents
								lowerCAmelCase				=	do_split_on_punc


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None       )		->Any:
								lowerCAmelCase				=	self.never_split.union(set(__SCREAMING_SNAKE_CASE       )       ) if never_split else self.never_split
								lowerCAmelCase				=	self._clean_text(__SCREAMING_SNAKE_CASE       )

								# This was added on November 1st, 2018 for the multilingual and Chinese
								# models. This is also applied to the English models now, but it doesn't
								# matter since the English models were not trained on any Chinese data
								# and generally don't have any Chinese data in them (there are Chinese
								# characters in the vocabulary because Wikipedia does have some Chinese
								# words in the English Wikipedia.).
								if self.tokenize_chinese_chars:
															lowerCAmelCase				=	self._tokenize_chinese_chars(__SCREAMING_SNAKE_CASE       )
								# prevents treating the same character with different unicode codepoints as different characters
								lowerCAmelCase				=	unicodedata.normalize('''NFC'''	,    __SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	whitespace_tokenize(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	[]
								for token in orig_tokens:
															if token not in never_split:
																						if self.do_lower_case:
																													lowerCAmelCase				=	token.lower()
																													if self.strip_accents is not False:
																																				lowerCAmelCase				=	self._run_strip_accents(__SCREAMING_SNAKE_CASE       )
																						elif self.strip_accents:
																													lowerCAmelCase				=	self._run_strip_accents(__SCREAMING_SNAKE_CASE       )
															split_tokens.extend(self._run_split_on_punc(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )       )

								lowerCAmelCase				=	whitespace_tokenize(''' '''.join(__SCREAMING_SNAKE_CASE       )       )
								return output_tokens


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->int:
								lowerCAmelCase				=	unicodedata.normalize('''NFD'''	,    __SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	[]
								for char in text:
															lowerCAmelCase				=	unicodedata.category(__SCREAMING_SNAKE_CASE       )
															if cat == "Mn":
																						continue
															output.append(__SCREAMING_SNAKE_CASE       )
								return "".join(__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None       )		->List[Any]:
								if not self.do_split_on_punc or (never_split is not None and text in never_split):
															return [text]
								lowerCAmelCase				=	list(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	0
								lowerCAmelCase				=	True
								lowerCAmelCase				=	[]
								while i < len(__SCREAMING_SNAKE_CASE       ):
															lowerCAmelCase				=	chars[i]
															if _is_punctuation(__SCREAMING_SNAKE_CASE       ):
																						output.append([char]       )
																						lowerCAmelCase				=	True
															else:
																						if start_new_word:
																													output.append([]       )
																						lowerCAmelCase				=	False
																						output[-1].append(__SCREAMING_SNAKE_CASE       )
															i += 1

								return ["".join(__SCREAMING_SNAKE_CASE       ) for x in output]


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Any:
								lowerCAmelCase				=	[]
								for char in text:
															lowerCAmelCase				=	ord(__SCREAMING_SNAKE_CASE       )
															if self._is_chinese_char(__SCREAMING_SNAKE_CASE       ):
																						output.append(''' '''       )
																						output.append(__SCREAMING_SNAKE_CASE       )
																						output.append(''' '''       )
															else:
																						output.append(__SCREAMING_SNAKE_CASE       )
								return "".join(__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								# This defines a "chinese character" as anything in the CJK Unicode block:
								#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
								#
								# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
								# despite its name. The modern Korean Hangul alphabet is a different block,
								# as is Japanese Hiragana and Katakana. Those alphabets are used to write
								# space-separated words, so they are not treated specially and handled
								# like the all of the other languages.
								if (
								    (cp >= 0X4_E00 and cp <= 0X9_FFF)
								    or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
								    or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
								    or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
								    or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
								    or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
								    or (cp >= 0XF_900 and cp <= 0XF_AFF)
								    or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
								):  #
															return True

								return False




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->List[Any]:
								lowerCAmelCase				=	[]
								for char in text:
															lowerCAmelCase				=	ord(__SCREAMING_SNAKE_CASE       )
															if cp == 0 or cp == 0XF_FFD or _is_control(__SCREAMING_SNAKE_CASE       ):
																						continue
															if _is_whitespace(__SCREAMING_SNAKE_CASE       ):
																						output.append(''' '''       )
															else:
																						output.append(__SCREAMING_SNAKE_CASE       )
								return "".join(__SCREAMING_SNAKE_CASE       )

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			List[Any]:
							lowerCAmelCase				=	set()
							lowerCAmelCase				=	word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)							)
														lowerCAmelCase				=	char
							return pairs

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			str:
							lowerCAmelCase				=	text.replace('''—'''     ,						'''-'''							)
							lowerCAmelCase				=	text.replace('''–'''     ,						'''-'''							)
							lowerCAmelCase				=	text.replace('''―'''     ,						'''-'''							)
							lowerCAmelCase				=	text.replace('''…'''     ,						'''...'''							)
							lowerCAmelCase				=	text.replace('''´'''     ,						'''\''''							)
							lowerCAmelCase				=	re.sub(R'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'''     ,						R''' \1 '''     ,						snake_case__							)
							lowerCAmelCase				=	re.sub(R'''\s*\n\s*'''     ,						''' \n '''     ,						snake_case__							)
							lowerCAmelCase				=	re.sub(R'''[^\S\n]+'''     ,						''' '''     ,						snake_case__							)
							return text.strip()






class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Optional[Any]     =       VOCAB_FILES_NAMES
	UpperCAmelCase_						:      Optional[int]     =       PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase_						:      int     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase_						:      str     =       ["""input_ids""", """attention_mask"""]


	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE="<unk>"	,    **__SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								super().__init__(unk_token=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )

								try:
															import ftfy
															from spacy.lang.en import English

															lowerCAmelCase				=	English()
															lowerCAmelCase				=	_nlp.tokenizer
															lowerCAmelCase				=	ftfy.fix_text
								except ImportError:
															logger.warning('''ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'''       )
															lowerCAmelCase				=	BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       )
															lowerCAmelCase				=	None

								with open(__SCREAMING_SNAKE_CASE	,    encoding='''utf-8'''       ) as vocab_handle:
															lowerCAmelCase				=	json.load(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	{v: k for k, v in self.encoder.items()}
								with open(__SCREAMING_SNAKE_CASE	,    encoding='''utf-8'''       ) as merges_handle:
															lowerCAmelCase				=	merges_handle.read().split('''\n'''       )[1:-1]
								lowerCAmelCase				=	[tuple(merge.split()       ) for merge in merges]
								lowerCAmelCase				=	dict(zip(__SCREAMING_SNAKE_CASE	,    range(len(__SCREAMING_SNAKE_CASE       )       )       )       )
								lowerCAmelCase				=	{}


	@property
	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
								return True


	@property
	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
								return len(self.encoder       )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
								return dict(self.encoder	,    **self.added_tokens_encoder       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->str:
								lowerCAmelCase				=	tuple(token[:-1]       ) + (token[-1] + '''</w>''',)
								if token in self.cache:
															return self.cache[token]
								lowerCAmelCase				=	get_pairs(__SCREAMING_SNAKE_CASE       )

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

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

																						if word[i] == first and i < len(__SCREAMING_SNAKE_CASE       ) - 1 and word[i + 1] == second:
																													new_word.append(first + second       )
																													i += 2
																						else:
																													new_word.append(word[i]       )
																													i += 1
															lowerCAmelCase				=	tuple(__SCREAMING_SNAKE_CASE       )
															lowerCAmelCase				=	new_word
															if len(__SCREAMING_SNAKE_CASE       ) == 1:
																						break
															else:
																						lowerCAmelCase				=	get_pairs(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	''' '''.join(__SCREAMING_SNAKE_CASE       )
								if word == "\n  </w>":
															lowerCAmelCase				=	'''\n</w>'''
								lowerCAmelCase				=	word
								return word


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	[]
								if self.fix_text is None:
															# Using BERT's BasicTokenizer
															lowerCAmelCase				=	self.nlp.tokenize(__SCREAMING_SNAKE_CASE       )
															for token in text:
																						split_tokens.extend(list(self.bpe(__SCREAMING_SNAKE_CASE       ).split(''' '''       )       )       )
								else:
															# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
															lowerCAmelCase				=	self.nlp(text_standardize(self.fix_text(__SCREAMING_SNAKE_CASE       )       )       )
															for token in text:
																						split_tokens.extend(list(self.bpe(token.text.lower()       ).split(''' '''       )       )       )
								return split_tokens


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Optional[Any]:
								return self.encoder.get(__SCREAMING_SNAKE_CASE	,    self.encoder.get(self.unk_token       )       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Any:
								return self.decoder.get(__SCREAMING_SNAKE_CASE	,    self.unk_token       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	''''''.join(__SCREAMING_SNAKE_CASE       ).replace('''</w>'''	,    ''' '''       ).strip()
								return out_string




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None       )		->Tuple[str]:
								if not os.path.isdir(__SCREAMING_SNAKE_CASE       ):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
															return
								lowerCAmelCase				=	os.path.join(
								    __SCREAMING_SNAKE_CASE	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
								lowerCAmelCase				=	os.path.join(
								    __SCREAMING_SNAKE_CASE	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

								lowerCAmelCase				=	0
								with open(__SCREAMING_SNAKE_CASE	,    '''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 __SCREAMING_SNAKE_CASE       : kv[1]       ):
																						if index != token_index:
																													logger.warning(
																													    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																													    ''' Please check that the tokenizer is not corrupted!'''       )
																													lowerCAmelCase				=	token_index
																						writer.write(''' '''.join(__SCREAMING_SNAKE_CASE       ) + '''\n'''       )
																						index += 1

								return vocab_file, merge_file




from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


lowercase__    :       List[Any]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      torch.FloatTensor




class      lowercase_						(						UpperCamelCase_   ,  UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Tuple     =       1


	@register_to_config
	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 40	,    __SCREAMING_SNAKE_CASE = 0.0_0_2	,    __SCREAMING_SNAKE_CASE = 8_0.0	,    __SCREAMING_SNAKE_CASE = 0.5	,    __SCREAMING_SNAKE_CASE = 1.0	,    __SCREAMING_SNAKE_CASE = 7.0	,    __SCREAMING_SNAKE_CASE = True	,    )		->Tuple:
								# standard deviation of the initial noise distribution
								lowerCAmelCase				=	sigma_max

								lowerCAmelCase				=	np.linspace(0	,    1	,    __SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

								# setable values
								lowerCAmelCase				=	None
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	False
								lowerCAmelCase				=	False


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								if schedule_timesteps is None:
															lowerCAmelCase				=	self.timesteps

								lowerCAmelCase				=	(schedule_timesteps == timestep).nonzero()
								return indices.item()


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->torch.FloatTensor:
								# Get sigma corresponding to timestep
								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )
								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigmas[step_idx]

								lowerCAmelCase				=	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

								lowerCAmelCase				=	True
								return sample


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								if not isinstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       ):
															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.floataa       )

								lowerCAmelCase				=	1000 * 0.2_5 * np.log(sigmas + 1e-44       )

								return timesteps


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    )		->str:
								if num_inference_steps is None and timesteps is None:
															raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''       )

								if num_inference_steps is not None and timesteps is not None:
															raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''       )

								# Follow DDPMScheduler custom timesteps logic
								if timesteps is not None:
															for i in range(1	,    len(__SCREAMING_SNAKE_CASE       )       ):
																						if timesteps[i] >= timesteps[i - 1]:
																													raise ValueError('''`timesteps` must be in descending order.'''       )

															if timesteps[0] >= self.config.num_train_timesteps:
																						raise ValueError(
																						    F"`timesteps` must start before `self.config.train_timesteps`:"
																						    F" {self.config.num_train_timesteps}."       )

															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.intaa       )
															lowerCAmelCase				=	True
								else:
															if num_inference_steps > self.config.num_train_timesteps:
																						raise ValueError(
																						    F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																						    F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																						    F" maximal {self.config.num_train_timesteps} timesteps."       )

															lowerCAmelCase				=	num_inference_steps

															lowerCAmelCase				=	self.config.num_train_timesteps // self.num_inference_steps
															lowerCAmelCase				=	(np.arange(0	,    __SCREAMING_SNAKE_CASE       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
															lowerCAmelCase				=	False

								# Map timesteps to Karras sigmas directly for multistep sampling
								# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
								lowerCAmelCase				=	self.config.num_train_timesteps
								lowerCAmelCase				=	timesteps[::-1].copy()
								lowerCAmelCase				=	ramp / (num_train_timesteps - 1)
								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

								lowerCAmelCase				=	np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )

								if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
															# mps does not support float64
															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE	,    dtype=torch.floataa       )
								else:
															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.config.rho
								lowerCAmelCase				=	sigma_min ** (1 / rho)
								lowerCAmelCase				=	sigma_max ** (1 / rho)
								lowerCAmelCase				=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_data

								lowerCAmelCase				=	sigma_data**2 / (sigma**2 + sigma_data**2)
								lowerCAmelCase				=	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->List[str]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_data

								lowerCAmelCase				=	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
								lowerCAmelCase				=	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    )		->Union[CMStochasticIterativeSchedulerOutput, Tuple]:

								if (
								    isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.IntTensor       )
								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.LongTensor       )
								):
															raise ValueError(
															    (
															        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
															        F" `{self.__class__}.step()` is not supported. Make sure to pass"
															        ''' one of the `scheduler.timesteps` as a timestep.'''
															    )	,    )

								if not self.is_scale_input_called:
															logger.warning(
															    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
															    '''See `StableDiffusionPipeline` for a usage example.'''       )

								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )

								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )

								# sigma_next corresponds to next_t in original implementation
								lowerCAmelCase				=	self.sigmas[step_index]
								if step_index + 1 < self.config.num_train_timesteps:
															lowerCAmelCase				=	self.sigmas[step_index + 1]
								else:
															# Set sigma_next to sigma_min
															lowerCAmelCase				=	self.sigmas[-1]

								# Get scalings for boundary conditions
								lowerCAmelCase	,   lowerCAmelCase				=	self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE       )

								# 1. Denoise model output using boundary conditions
								lowerCAmelCase				=	c_out * model_output + c_skip * sample
								if self.config.clip_denoised:
															lowerCAmelCase				=	denoised.clamp(-1	,    1       )

								# 2. Sample z ~ N(0, s_noise^2 * I)
								# Noise is not used for onestep sampling.
								if len(self.timesteps       ) > 1:
															lowerCAmelCase				=	randn_tensor(
															    model_output.shape	,    dtype=model_output.dtype	,    device=model_output.device	,    generator=__SCREAMING_SNAKE_CASE       )
								else:
															lowerCAmelCase				=	torch.zeros_like(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	noise * self.config.s_noise

								lowerCAmelCase				=	sigma_next.clamp(min=__SCREAMING_SNAKE_CASE	,    max=__SCREAMING_SNAKE_CASE       )

								# 3. Return noisy sample
								# tau = sigma_hat, eps = sigma_min
								lowerCAmelCase				=	denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

								if not return_dict:
															return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    )		->torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCAmelCase				=	self.sigmas.to(device=original_samples.device	,    dtype=original_samples.dtype       )
								if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE       ):
															# mps does not support float64
															lowerCAmelCase				=	self.timesteps.to(original_samples.device	,    dtype=torch.floataa       )
															lowerCAmelCase				=	timesteps.to(original_samples.device	,    dtype=torch.floataa       )
								else:
															lowerCAmelCase				=	self.timesteps.to(original_samples.device       )
															lowerCAmelCase				=	timesteps.to(original_samples.device       )

								lowerCAmelCase				=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								lowerCAmelCase				=	sigmas[step_indices].flatten()
								while len(sigma.shape       ) < len(original_samples.shape       ):
															lowerCAmelCase				=	sigma.unsqueeze(-1       )

								lowerCAmelCase				=	original_samples + noise * sigma
								return noisy_samples




	def __len__(							self       )		->int:
								return self.config.num_train_timesteps



