




"""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


_lowercase		 =      logging.get_logger(__name__)

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

_lowercase		 =      {
    """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"""
        ),
    },
}

_lowercase		 =      {
    """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 						lowerCAmelCase							(	):
	'''simple docstring'''


	snake_case__    :   Optional[int]							     =   (
	    list(range(ord('''!'''						) ,     ord('''~'''						) + 1						)						) + list(range(ord('''¡'''						) ,     ord('''¬'''						) + 1						)						) + list(range(ord('''®'''						) ,     ord('''ÿ'''						) + 1						)						)
	)
	snake_case__    :   Any							     =   bs[:]
	snake_case__    :   Tuple							     =   0
	for b in range(2**8						):
		if b not in bs:
			bs.append(lowerCamelCase						)
			cs.append(2**8 + n						)
			n += 1
	snake_case__    :   Any							     =   [chr(lowerCamelCase						) for n in cs]
	return dict(zip(lowerCamelCase ,     lowerCamelCase						)						)

def 						lowerCAmelCase							(	lowerCamelCase						):
	'''simple docstring'''


	snake_case__    :   Dict							     =   set()
	snake_case__    :   Tuple							     =   word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)						)
		snake_case__    :   Optional[Any]							     =   char
	return pairs

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =VOCAB_FILES_NAMES
				_lowerCAmelCase		     =PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase		     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase		     =["input_ids", "attention_mask"]

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

					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__    :   Optional[int]							     =   AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else mask_token

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

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

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

				@property
				def  _lowerCamelCase   (				self				:       List[str] ):
					return len(self.encoder )

				def  _lowerCamelCase   (				self				:       Dict ):
					return dict(self.encoder ,**self.added_tokens_encoder )

				def  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       int ):
					if token in self.cache:
						return self.cache[token]
					snake_case__    :   Tuple							     =   tuple(SCREAMING_SNAKE_CASE_ )
					snake_case__    :   List[str]							     =   get_pairs(SCREAMING_SNAKE_CASE_ )

					if not pairs:
						return token

					while True:
						snake_case__    :   Dict							     =   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
						snake_case__    :   List[Any]							     =   bigram
						snake_case__    :   str							     =   []
						snake_case__    :   int							     =   0
						while i < len(SCREAMING_SNAKE_CASE_ ):
							try:
								snake_case__    :   Union[str, Any]							     =   word.index(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
							except ValueError:
								new_word.extend(word[i:] )
								break
							else:
								new_word.extend(word[i:j] )
								snake_case__    :   List[str]							     =   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
						snake_case__    :   Any							     =   tuple(SCREAMING_SNAKE_CASE_ )
						snake_case__    :   List[Any]							     =   new_word
						if len(SCREAMING_SNAKE_CASE_ ) == 1:
							break
						else:
							snake_case__    :   Optional[int]							     =   get_pairs(SCREAMING_SNAKE_CASE_ )
					snake_case__    :   int							     =   ''' '''.join(SCREAMING_SNAKE_CASE_ )
					snake_case__    :   List[str]							     =   word
					return word

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):
					snake_case__    :   Any							     =   []
					for token in re.findall(self.pat ,SCREAMING_SNAKE_CASE_ ):
						snake_case__    :   Tuple							     =   ''''''.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(SCREAMING_SNAKE_CASE_ ).split(''' ''' ) )
					return bpe_tokens

				def  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
					return self.encoder.get(SCREAMING_SNAKE_CASE_ ,self.encoder.get(self.unk_token ) )

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):
					return self.decoder.get(SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       int ,SCREAMING_SNAKE_CASE_				:       List[Any] ):
					snake_case__    :   List[str]							     =   ''''''.join(SCREAMING_SNAKE_CASE_ )
					snake_case__    :   int							     =   bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' ,errors=self.errors )
					return text

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[str] = None ):
					if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
					snake_case__    :   List[str]							     =   os.path.join(
					    SCREAMING_SNAKE_CASE_ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
					snake_case__    :   Dict							     =   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''' )

					snake_case__    :   List[Any]							     =   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!''' )
								snake_case__    :   Union[str, Any]							     =   token_index
							writer.write(''' '''.join(SCREAMING_SNAKE_CASE_ ) + '''\n''' )
							index += 1

					return vocab_file, merge_file

				def  _lowerCamelCase   (				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       List[int] ,SCREAMING_SNAKE_CASE_				:       Optional[List[int]] = None ):
					if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case__    :   Any							     =   [self.cls_token_id]
					snake_case__    :   List[Any]							     =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       List[int] ,SCREAMING_SNAKE_CASE_				:       Optional[List[int]] = None ,SCREAMING_SNAKE_CASE_				:       bool = False ):
					if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=SCREAMING_SNAKE_CASE_ ,token_ids_a=SCREAMING_SNAKE_CASE_ ,already_has_special_tokens=SCREAMING_SNAKE_CASE_ )

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

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








"""simple docstring"""





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 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase=0.999 ,     lowerCamelCase="cosine" ,     ):
						'''simple docstring'''


						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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

class  __lowercase    (     snake_case_  ,				snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =[e.name for e in KarrasDiffusionSchedulers]
				_lowerCAmelCase		     =2

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

										snake_case__    :   Tuple							     =   1.0 - self.betas
										snake_case__    :   List[Any]							     =   torch.cumprod(self.alphas ,dim=0 )

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

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       List[Any]=None ):
										if schedule_timesteps is None:
																snake_case__    :   Union[str, Any]							     =   self.timesteps

										snake_case__    :   Optional[int]							     =   (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__    :   List[str]							     =   1 if len(SCREAMING_SNAKE_CASE_ ) > 1 else 0
										else:
																snake_case__    :   Any							     =   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
																snake_case__    :   int							     =   self._index_counter[timestep_int]

										return indices[pos].item()

				@property
				def  _lowerCamelCase   (				self				:       List[Any] ):
										# 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  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       Union[float, torch.FloatTensor] ,):
										snake_case__    :   Optional[int]							     =   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

										if self.state_in_first_order:
																snake_case__    :   Optional[Any]							     =   self.sigmas[step_index]
										else:
																snake_case__    :   str							     =   self.sigmas_interpol[step_index - 1]

										snake_case__    :   Dict							     =   sample / ((sigma**2 + 1) ** 0.5)
										return sample

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Union[str, torch.device] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,):
										snake_case__    :   str							     =   num_inference_steps

										snake_case__    :   Union[str, Any]							     =   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__    :   Optional[Any]							     =   np.linspace(0 ,num_train_timesteps - 1 ,SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
																snake_case__    :   Optional[Any]							     =   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__    :   Dict							     =   (np.arange(0 ,SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_ )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																snake_case__    :   Union[str, Any]							     =   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__    :   List[Any]							     =   (np.arange(SCREAMING_SNAKE_CASE_ ,0 ,-step_ratio )).round().copy().astype(SCREAMING_SNAKE_CASE_ )
																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__    :   List[str]							     =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
										snake_case__    :   List[str]							     =   torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   List[str]							     =   np.interp(SCREAMING_SNAKE_CASE_ ,np.arange(0 ,len(SCREAMING_SNAKE_CASE_ ) ) ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   int							     =   np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
										snake_case__    :   Dict							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )

										# compute up and down sigmas
										snake_case__    :   str							     =   sigmas.roll(-1 )
										snake_case__    :   str							     =   0.0
										snake_case__    :   List[Any]							     =   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
										snake_case__    :   List[str]							     =   (sigmas_next**2 - sigmas_up**2) ** 0.5
										snake_case__    :   List[Any]							     =   0.0

										# compute interpolated sigmas
										snake_case__    :   Tuple							     =   sigmas.log().lerp(sigmas_down.log() ,0.5 ).exp()
										snake_case__    :   Optional[Any]							     =   0.0

										# set sigmas
										snake_case__    :   List[str]							     =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
										snake_case__    :   Optional[int]							     =   torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
										snake_case__    :   str							     =   torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
										snake_case__    :   Optional[Any]							     =   torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

										if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
																# mps does not support float64
																snake_case__    :   Tuple							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										else:
																snake_case__    :   int							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Tuple							     =   self.sigma_to_t(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ,dtype=timesteps.dtype )
										snake_case__    :   int							     =   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,dim=-1 ).flatten()

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

										snake_case__    :   Optional[int]							     =   None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										snake_case__    :   Optional[int]							     =   defaultdict(SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
										# get log sigma
										snake_case__    :   Dict							     =   sigma.log()

										# get distribution
										snake_case__    :   Union[str, Any]							     =   log_sigma - self.log_sigmas[:, None]

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

										snake_case__    :   Any							     =   self.log_sigmas[low_idx]
										snake_case__    :   str							     =   self.log_sigmas[high_idx]

										# interpolate sigmas
										snake_case__    :   int							     =   (low - log_sigma) / (low - high)
										snake_case__    :   List[Any]							     =   w.clamp(0 ,1 )

										# transform interpolation to time range
										snake_case__    :   Optional[int]							     =   (1 - w) * low_idx + w * high_idx
										snake_case__    :   int							     =   t.view(sigma.shape )
										return t

				@property
				def  _lowerCamelCase   (				self				:       List[Any] ):
										return self.sample is None

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Union[torch.FloatTensor, np.ndarray] ,SCREAMING_SNAKE_CASE_				:       Union[float, torch.FloatTensor] ,SCREAMING_SNAKE_CASE_				:       Union[torch.FloatTensor, np.ndarray] ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										snake_case__    :   str							     =   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

										# advance index counter by 1
										snake_case__    :   Optional[int]							     =   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
																snake_case__    :   List[Any]							     =   self.sigmas[step_index]
																snake_case__    :   str							     =   self.sigmas_interpol[step_index]
																snake_case__    :   str							     =   self.sigmas_up[step_index]
																snake_case__    :   Optional[int]							     =   self.sigmas_down[step_index - 1]
										else:
																# 2nd order / KPDM2's method
																snake_case__    :   int							     =   self.sigmas[step_index - 1]
																snake_case__    :   Tuple							     =   self.sigmas_interpol[step_index - 1]
																snake_case__    :   Optional[Any]							     =   self.sigmas_up[step_index - 1]
																snake_case__    :   Dict							     =   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__    :   Optional[int]							     =   0
										snake_case__    :   List[Any]							     =   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										snake_case__    :   Dict							     =   model_output.device
										snake_case__    :   Tuple							     =   randn_tensor(model_output.shape ,dtype=model_output.dtype ,device=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ )

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																snake_case__    :   List[str]							     =   sigma_hat if self.state_in_first_order else sigma_interpol
																snake_case__    :   Any							     =   sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
																snake_case__    :   Tuple							     =   sigma_hat if self.state_in_first_order else sigma_interpol
																snake_case__    :   List[Any]							     =   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__    :   List[str]							     =   (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																snake_case__    :   Tuple							     =   sigma_interpol - sigma_hat

																# store for 2nd order step
																snake_case__    :   int							     =   sample
																snake_case__    :   Tuple							     =   dt
																snake_case__    :   Tuple							     =   sample + derivative * dt
										else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																snake_case__    :   Tuple							     =   (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																snake_case__    :   Union[str, Any]							     =   sigma_down - sigma_hat

																snake_case__    :   int							     =   self.sample
																snake_case__    :   Tuple							     =   None

																snake_case__    :   Dict							     =   sample + derivative * dt
																snake_case__    :   Any							     =   prev_sample + noise * sigma_up

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										snake_case__    :   Optional[int]							     =   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
																snake_case__    :   List[str]							     =   self.timesteps.to(original_samples.device ,dtype=torch.floataa )
																snake_case__    :   int							     =   timesteps.to(original_samples.device ,dtype=torch.floataa )
										else:
																snake_case__    :   Any							     =   self.timesteps.to(original_samples.device )
																snake_case__    :   List[str]							     =   timesteps.to(original_samples.device )

										snake_case__    :   List[Any]							     =   [self.index_for_timestep(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for t in timesteps]

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

										snake_case__    :   List[str]							     =   original_samples + noise * sigma
										return noisy_samples





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


