
'''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


__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""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}






@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     _lowerCAmelCase					(					UpperCamelCase__   ):







							'''simple docstring'''

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

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

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

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

														with open(lowerCAmelCase__						,						encoding="""utf-8"""						) as vocab_handle:
																					_snake_case     =						json.load(lowerCAmelCase__						)
														_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(lowerCAmelCase__						,						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(lowerCAmelCase__						,						range(len(lowerCAmelCase__						)						)						)						)
														_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  lowercase							(self						)  ->     Optional[int]:
														return len(self.encoder						)

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

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




														if token in self.cache:
																					return self.cache[token]
														_snake_case     =						tuple(lowerCAmelCase__						)
														_snake_case     =						get_pairs(lowerCAmelCase__						)

														if not pairs:
																					return token

														while True:
																					_snake_case     =						min(lowerCAmelCase__						,						key=lambda UpperCAmelCase						: self.bpe_ranks.get(lowerCAmelCase__						,						float("""inf"""						)						)						)
																					if bigram not in self.bpe_ranks:
																												break
																					_snake_case     =						bigram
																					_snake_case     =						[]
																					_snake_case     =						0
																					while i < len(lowerCAmelCase__						):
																												try:
																																			_snake_case     =						word.index(lowerCAmelCase__						,						lowerCAmelCase__						)
																												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(lowerCAmelCase__						) - 1 and word[i + 1] == second:
																																			new_word.append(first + second						)
																																			i += 2
																												else:
																																			new_word.append(word[i]						)
																																			i += 1
																					_snake_case     =						tuple(lowerCAmelCase__						)
																					_snake_case     =						new_word
																					if len(lowerCAmelCase__						) == 1:
																												break
																					else:
																												_snake_case     =						get_pairs(lowerCAmelCase__						)
														_snake_case     =						" ".join(lowerCAmelCase__						)
														_snake_case     =						word
														return word

							def  lowercase							(self						,						UpperCAmelCase						)  ->     int:
														_snake_case     =						[]
														for token in re.findall(self.pat						,						lowerCAmelCase__						):
																					_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(lowerCAmelCase__						).split(""" """						)						)
														return bpe_tokens

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Union[str, Any]:
														return self.encoder.get(lowerCAmelCase__						,						self.encoder.get(self.unk_token						)						)

							def  lowercase							(self						,						UpperCAmelCase						)  ->     int:
														return self.decoder.get(lowerCAmelCase__						)

							def  lowercase							(self						,						UpperCAmelCase						)  ->     int:
														_snake_case     =						"".join(lowerCAmelCase__						)
														_snake_case     =						bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""						,						errors=self.errors						)
														return text

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Tuple[str]:




														if not os.path.isdir(lowerCAmelCase__						):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
																					return
														_snake_case     =						os.path.join(
														    lowerCAmelCase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
														_snake_case     =						os.path.join(
														    lowerCAmelCase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

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

														_snake_case     =						0
														with open(lowerCAmelCase__						,						"""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(lowerCAmelCase__						) + """\n"""						)
																												index += 1

														return vocab_file, merge_file

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = 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  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						,						UpperCAmelCase = False						)  ->     List[int]:




														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCAmelCase__						,						token_ids_a=lowerCAmelCase__						,						already_has_special_tokens=lowerCAmelCase__						)

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

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = 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  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=False						,						**UpperCAmelCase						)  ->     int:
														_snake_case     =						kwargs.pop("""add_prefix_space"""						,						self.add_prefix_space						)
														if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__						) > 0 and not text[0].isspace()):
																					_snake_case     =						" " + text
														return (text, kwargs)




'''simple docstring'''
import math
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 KarrasDiffusionSchedulers, SchedulerMixin


__lowerCAmelCase  							= logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		42
							lowerCAmelCase_      		=		None






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     _lowerCAmelCase					(					__snake_case  ,							__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		[e.name for e in KarrasDiffusionSchedulers]
							lowerCAmelCase_      		=		1

							@register_to_config
							def __init__(self						,						UpperCAmelCase = 1000						,						UpperCAmelCase = 0.0001						,						UpperCAmelCase = 0.02						,						UpperCAmelCase = "linear"						,						UpperCAmelCase = None						,						UpperCAmelCase = "epsilon"						,						UpperCAmelCase = "linear"						,						UpperCAmelCase = False						,						UpperCAmelCase = "linspace"						,						UpperCAmelCase = 0						,						)  ->     Tuple:
														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						)

														_snake_case     =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
														_snake_case     =						np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
														_snake_case     =						torch.from_numpy(UpperCAmelCase						)

														# setable values
														_snake_case     =						None
														_snake_case     =						np.linspace(0						,						num_train_timesteps - 1						,						UpperCAmelCase						,						dtype=UpperCAmelCase						)[::-1].copy()
														_snake_case     =						torch.from_numpy(UpperCAmelCase						)
														_snake_case     =						False
														_snake_case     =						use_karras_sigmas

							@property
							def  lowercase							(self						)  ->     str:
														# 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  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     torch.FloatTensor:
														if isinstance(UpperCAmelCase						,						torch.Tensor						):
																					_snake_case     =						timestep.to(self.timesteps.device						)
														_snake_case     =						(self.timesteps == timestep).nonzero().item()
														_snake_case     =						self.sigmas[step_index]

														_snake_case     =						sample / ((sigma**2 + 1) ** 0.5)

														_snake_case     =						True
														return sample

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Any:
														_snake_case     =						num_inference_steps

														# "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						,						self.config.num_train_timesteps - 1						,						UpperCAmelCase						,						dtype=UpperCAmelCase						)[
																					    ::-1
																					].copy()
														elif self.config.timestep_spacing == "leading":
																					_snake_case     =						self.config.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     =						self.config.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(self.config.num_train_timesteps						,						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     =						np.log(UpperCAmelCase						)

														if self.config.interpolation_type == "linear":
																					_snake_case     =						np.interp(UpperCAmelCase						,						np.arange(0						,						len(UpperCAmelCase						)						)						,						UpperCAmelCase						)
														elif self.config.interpolation_type == "log_linear":
																					_snake_case     =						torch.linspace(np.log(sigmas[-1]						)						,						np.log(sigmas[0]						)						,						num_inference_steps + 1						).exp()
														else:
																					raise ValueError(
																					    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
																					    """ 'linear' or 'log_linear'"""						)

														if self.use_karras_sigmas:
																					_snake_case     =						self._convert_to_karras(in_sigmas=UpperCAmelCase						,						num_inference_steps=self.num_inference_steps						)
																					_snake_case     =						np.array([self._sigma_to_t(UpperCAmelCase						,						UpperCAmelCase						) for sigma in sigmas]						)

														_snake_case     =						np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
														_snake_case     =						torch.from_numpy(UpperCAmelCase						).to(device=UpperCAmelCase						)
														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(device=UpperCAmelCase						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     str:
														# get log sigma
														_snake_case     =						np.log(UpperCAmelCase						)

														# get distribution
														_snake_case     =						log_sigma - log_sigmas[:, np.newaxis]

														# get sigmas range
														_snake_case     =						np.cumsum((dists >= 0)						,						axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
														_snake_case     =						low_idx + 1

														_snake_case     =						log_sigmas[low_idx]
														_snake_case     =						log_sigmas[high_idx]

														# interpolate sigmas
														_snake_case     =						(low - log_sigma) / (low - high)
														_snake_case     =						np.clip(UpperCAmelCase						,						0						,						1						)

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

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     torch.FloatTensor:
														_snake_case     =						in_sigmas[-1].item()
														_snake_case     =						in_sigmas[0].item()

														_snake_case     =						7.0  # 7.0 is the value used in the paper
														_snake_case     =						np.linspace(0						,						1						,						UpperCAmelCase						)
														_snake_case     =						sigma_min ** (1 / rho)
														_snake_case     =						sigma_max ** (1 / rho)
														_snake_case     =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
														return sigmas

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase = 0.0						,						UpperCAmelCase = 0.0						,						UpperCAmelCase = float("""inf"""						)						,						UpperCAmelCase = 1.0						,						UpperCAmelCase = None						,						UpperCAmelCase = True						,						)  ->     Union[EulerDiscreteSchedulerOutput, Tuple]:

														if (
														    isinstance(UpperCAmelCase						,						UpperCAmelCase						)
														    or isinstance(UpperCAmelCase						,						torch.IntTensor						)
														    or isinstance(UpperCAmelCase						,						torch.LongTensor						)
														):
																					raise ValueError(
																					    (
																					        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
																					        """ `EulerDiscreteScheduler.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(UpperCAmelCase						,						torch.Tensor						):
																					_snake_case     =						timestep.to(self.timesteps.device						)

														_snake_case     =						(self.timesteps == timestep).nonzero().item()
														_snake_case     =						self.sigmas[step_index]

														_snake_case     =						min(s_churn / (len(self.sigmas						) - 1)						,						2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

														_snake_case     =						randn_tensor(
														    model_output.shape						,						dtype=model_output.dtype						,						device=model_output.device						,						generator=UpperCAmelCase						)

														_snake_case     =						noise * s_noise
														_snake_case     =						sigma * (gamma + 1)

														if gamma > 0:
																					_snake_case     =						sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														# NOTE: "original_sample" should not be an expected prediction_type but is left in for
														# backwards compatibility
														if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																					_snake_case     =						model_output
														elif self.config.prediction_type == "epsilon":
																					_snake_case     =						sample - sigma_hat * model_output
														elif self.config.prediction_type == "v_prediction":
																					# * c_out + input * c_skip
																					_snake_case     =						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
														else:
																					raise ValueError(
																					    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""						)

														# 2. Convert to an ODE derivative
														_snake_case     =						(sample - pred_original_sample) / sigma_hat

														_snake_case     =						self.sigmas[step_index + 1] - sigma_hat

														_snake_case     =						sample + derivative * dt

														if not return_dict:
																					return (prev_sample,)

														return EulerDiscreteSchedulerOutput(prev_sample=UpperCAmelCase						,						pred_original_sample=UpperCAmelCase						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						)  ->     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     =						[(schedule_timesteps == t).nonzero().item() 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						)  ->     Optional[int]:
														return self.config.num_train_timesteps