

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


lowerCAmelCase_ 			=			logging.get_logger(__name__)

lowerCAmelCase_ 			=			{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

lowerCAmelCase_ 			=			{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
}

lowerCAmelCase_ 			=			{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}


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


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			set()
						snake_case_     =			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char) )
												snake_case_     =			char
						return pairs





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE  :     Any		    =       PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE  :     List[Any]		    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE  :     Tuple		    =       ["input_ids", "attention_mask"]







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

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

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

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

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







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







			def        snake_case__(  self				:       Any							)   ->int:
									return dict(self.encoder							,     **self.added_tokens_encoder							)







			def        snake_case__(  self				:       Any							,     _UpperCamelCase				:       List[Any]							)   ->Tuple:
									if token in self.cache:
															return self.cache[token]
									snake_case_     =			tuple(_UpperCamelCase							)
									snake_case_     =			get_pairs(_UpperCamelCase							)

									if not pairs:
															return token

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

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







			def        snake_case__(  self				:       List[str]							,     _UpperCamelCase				:       int							)   ->Any:
									snake_case_     =			[]
									for token in re.findall(self.pat							,     _UpperCamelCase							):
															snake_case_     =			''''''.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(_UpperCamelCase							).split(''' '''							)							)
									return bpe_tokens







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       Dict							)   ->List[str]:
									return self.encoder.get(_UpperCamelCase							,     self.encoder.get(self.unk_token							)							)







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       int							)   ->int:
									return self.decoder.get(_UpperCamelCase							)







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       List[Any]							)   ->Dict:
									snake_case_     =			''''''.join(_UpperCamelCase							)
									snake_case_     =			bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''							,     errors=self.errors							)
									return text







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

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

									snake_case_     =			0
									with open(_UpperCamelCase							,     '''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 _UpperCamelCase							: 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!'''							)
																											snake_case_     =			token_index
																					writer.write(''' '''.join(_UpperCamelCase							) + '''\n'''							)
																					index += 1

									return vocab_file, merge_file







			def        snake_case__(  self				:       List[str]							,     _UpperCamelCase				:       List[int]							,     _UpperCamelCase				:       Optional[List[int]] = None							)   ->List[int]:
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_     =			[self.cls_token_id]
									snake_case_     =			[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       List[int]							,     _UpperCamelCase				:       Optional[List[int]] = None							,     _UpperCamelCase				:       bool = False							)   ->List[int]:
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=_UpperCamelCase							,     token_ids_a=_UpperCamelCase							,     already_has_special_tokens=_UpperCamelCase							)

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







			def        snake_case__(  self				:       List[str]							,     _UpperCamelCase				:       List[int]							,     _UpperCamelCase				:       Optional[List[int]] = None							)   ->List[int]:
									snake_case_     =			[self.sep_token_id]
									snake_case_     =			[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        snake_case__(  self				:       int							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       Optional[int]=False							,     **_UpperCamelCase				:       Any							)   ->Dict:
									snake_case_     =			kwargs.pop('''add_prefix_space'''							,     self.add_prefix_space							)
									if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase							) > 0 and not text[0].isspace()):
															snake_case_     =			''' ''' + text
									return (text, kwargs)


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=0.999						,	SCREAMING_SNAKE_CASE__="cosine"						,	):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(SCREAMING_SNAKE_CASE__ ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(SCREAMING_SNAKE_CASE__ ):
																		return math.exp(t * -12.0 )

						else:
												raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

						snake_case_     =			[]
						for i in range(SCREAMING_SNAKE_CASE__ ):
												snake_case_     =			i / num_diffusion_timesteps
												snake_case_     =			(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__ ) / alpha_bar_fn(SCREAMING_SNAKE_CASE__ )						,	SCREAMING_SNAKE_CASE__ ) )
						return torch.tensor(SCREAMING_SNAKE_CASE__						,	dtype=torch.floataa )





class 			snake_case_     (	__A			, __A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Tuple		    =       [e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       2







			@register_to_config
			def __init__(  self				:       Dict							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = 0.00085							,     _UpperCamelCase				:       float = 0.012							,     _UpperCamelCase				:       str = "linear"							,     _UpperCamelCase				:       Optional[Union[np.ndarray, List[float]]] = None							,     _UpperCamelCase				:       str = "epsilon"							,     _UpperCamelCase				:       str = "linspace"							,     _UpperCamelCase				:       int = 0							,     )   ->Any:
									if trained_betas is not None:
															snake_case_     =			torch.tensor(_UpperCamelCase							,     dtype=torch.floataa							)
									elif beta_schedule == "linear":
															snake_case_     =			torch.linspace(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     dtype=torch.floataa							)
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case_     =			(
															    torch.linspace(beta_start**0.5							,     beta_end**0.5							,     _UpperCamelCase							,     dtype=torch.floataa							) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case_     =			betas_for_alpha_bar(_UpperCamelCase							)
									else:
															raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''							)

									snake_case_     =			1.0 - self.betas
									snake_case_     =			torch.cumprod(self.alphas							,     dim=0							)

									#  set all values
									self.set_timesteps(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       Dict=None							)   ->Dict:
									if schedule_timesteps is None:
															snake_case_     =			self.timesteps

									snake_case_     =			(schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter							) == 0:
															snake_case_     =			1 if len(_UpperCamelCase							) > 1 else 0
									else:
															snake_case_     =			timestep.cpu().item() if torch.is_tensor(_UpperCamelCase							) else timestep
															snake_case_     =			self._index_counter[timestep_int]

									return indices[pos].item()







			@property
			def        snake_case__(  self				:       int							)   ->Optional[int]:
									# standard deviation of the initial noise distribution
									if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       torch.FloatTensor							,     _UpperCamelCase				:       Union[float, torch.FloatTensor]							,     )   ->torch.FloatTensor:
									snake_case_     =			self.index_for_timestep(_UpperCamelCase							)

									if self.state_in_first_order:
															snake_case_     =			self.sigmas[step_index]
									else:
															snake_case_     =			self.sigmas_interpol[step_index - 1]

									snake_case_     =			sample / ((sigma**2 + 1) ** 0.5)
									return sample







			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       Union[str, torch.device] = None							,     _UpperCamelCase				:       Optional[int] = None							,     )   ->str:
									snake_case_     =			num_inference_steps

									snake_case_     =			num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
															snake_case_     =			np.linspace(0							,     num_train_timesteps - 1							,     _UpperCamelCase							,     dtype=_UpperCamelCase							)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
															snake_case_     =			num_train_timesteps // self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															snake_case_     =			(np.arange(0							,     _UpperCamelCase							) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase							)
															timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
															snake_case_     =			num_train_timesteps / self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															snake_case_     =			(np.arange(_UpperCamelCase							,     0							,     -step_ratio							)).round().copy().astype(_UpperCamelCase							)
															timesteps -= 1
									else:
															raise ValueError(
															    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''							)

									snake_case_     =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
									snake_case_     =			torch.from_numpy(np.log(_UpperCamelCase							)							).to(_UpperCamelCase							)

									snake_case_     =			np.interp(_UpperCamelCase							,     np.arange(0							,     len(_UpperCamelCase							)							)							,     _UpperCamelCase							)
									snake_case_     =			np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
									snake_case_     =			torch.from_numpy(_UpperCamelCase							).to(device=_UpperCamelCase							)

									# compute up and down sigmas
									snake_case_     =			sigmas.roll(-1							)
									snake_case_     =			0.0
									snake_case_     =			(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									snake_case_     =			(sigmas_next**2 - sigmas_up**2) ** 0.5
									snake_case_     =			0.0

									# compute interpolated sigmas
									snake_case_     =			sigmas.log().lerp(sigmas_down.log()							,     0.5							).exp()
									snake_case_     =			0.0

									# set sigmas
									snake_case_     =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
									snake_case_     =			torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)
									snake_case_     =			torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2							), sigmas_up[-1:]]							)
									snake_case_     =			torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2							), sigmas_down[-1:]]							)

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

									snake_case_     =			self.sigma_to_t(_UpperCamelCase							).to(_UpperCamelCase							,     dtype=timesteps.dtype							)
									snake_case_     =			torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])							,     dim=-1							).flatten()

									snake_case_     =			torch.cat([timesteps[:1], interleaved_timesteps]							)

									snake_case_     =			None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									snake_case_     =			defaultdict(_UpperCamelCase							)







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       Any							)   ->Optional[Any]:
									# get log sigma
									snake_case_     =			sigma.log()

									# get distribution
									snake_case_     =			log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									snake_case_     =			dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
									snake_case_     =			low_idx + 1

									snake_case_     =			self.log_sigmas[low_idx]
									snake_case_     =			self.log_sigmas[high_idx]

									# interpolate sigmas
									snake_case_     =			(low - log_sigma) / (low - high)
									snake_case_     =			w.clamp(0							,     1							)

									# transform interpolation to time range
									snake_case_     =			(1 - w) * low_idx + w * high_idx
									snake_case_     =			t.view(sigma.shape							)
									return t







			@property
			def        snake_case__(  self				:       Any							)   ->List[str]:
									return self.sample is None







			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Union[torch.FloatTensor, np.ndarray]							,     _UpperCamelCase				:       Union[float, torch.FloatTensor]							,     _UpperCamelCase				:       Union[torch.FloatTensor, np.ndarray]							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       bool = True							,     )   ->Union[SchedulerOutput, Tuple]:
									snake_case_     =			self.index_for_timestep(_UpperCamelCase							)

									# advance index counter by 1
									snake_case_     =			timestep.cpu().item() if torch.is_tensor(_UpperCamelCase							) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
															snake_case_     =			self.sigmas[step_index]
															snake_case_     =			self.sigmas_interpol[step_index]
															snake_case_     =			self.sigmas_up[step_index]
															snake_case_     =			self.sigmas_down[step_index - 1]
									else:
															# 2nd order / KPDM2's method
															snake_case_     =			self.sigmas[step_index - 1]
															snake_case_     =			self.sigmas_interpol[step_index - 1]
															snake_case_     =			self.sigmas_up[step_index - 1]
															snake_case_     =			self.sigmas_down[step_index - 1]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									snake_case_     =			0
									snake_case_     =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									snake_case_     =			model_output.device
									snake_case_     =			randn_tensor(model_output.shape							,     dtype=model_output.dtype							,     device=_UpperCamelCase							,     generator=_UpperCamelCase							)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
															snake_case_     =			sigma_hat if self.state_in_first_order else sigma_interpol
															snake_case_     =			sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
															snake_case_     =			sigma_hat if self.state_in_first_order else sigma_interpol
															snake_case_     =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
															    sample / (sigma_input**2 + 1)
															)
									elif self.config.prediction_type == "sample":
															raise NotImplementedError('''prediction_type not implemented yet: sample'''							)
									else:
															raise ValueError(
															    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''							)

									if self.state_in_first_order:
															# 2. Convert to an ODE derivative for 1st order
															snake_case_     =			(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															snake_case_     =			sigma_interpol - sigma_hat

															# store for 2nd order step
															snake_case_     =			sample
															snake_case_     =			dt
															snake_case_     =			sample + derivative * dt
									else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															snake_case_     =			(sample - pred_original_sample) / sigma_interpol
															# 3. delta timestep
															snake_case_     =			sigma_down - sigma_hat

															snake_case_     =			self.sample
															snake_case_     =			None

															snake_case_     =			sample + derivative * dt
															snake_case_     =			prev_sample + noise * sigma_up

									if not return_dict:
															return (prev_sample,)

									return SchedulerOutput(prev_sample=_UpperCamelCase							)







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       torch.FloatTensor							,     _UpperCamelCase				:       torch.FloatTensor							,     _UpperCamelCase				:       torch.FloatTensor							,     )   ->torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									snake_case_     =			self.sigmas.to(device=original_samples.device							,     dtype=original_samples.dtype							)
									if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase							):
															# mps does not support float64
															snake_case_     =			self.timesteps.to(original_samples.device							,     dtype=torch.floataa							)
															snake_case_     =			timesteps.to(original_samples.device							,     dtype=torch.floataa							)
									else:
															snake_case_     =			self.timesteps.to(original_samples.device							)
															snake_case_     =			timesteps.to(original_samples.device							)

									snake_case_     =			[self.index_for_timestep(_UpperCamelCase							,     _UpperCamelCase							) for t in timesteps]

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

									snake_case_     =			original_samples + noise * sigma
									return noisy_samples







			def __len__(  self				:       Tuple							)   ->List[str]:
									return self.config.num_train_timesteps