

import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

lowerCAmelCase							:						Tuple     			=		logging.get_logger(__name__)

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

lowerCAmelCase							:						List[str]     			=		{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
}

lowerCAmelCase							:						Union[str, Any]     			=		{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}







@lru_cache()
def     A_   (					):
						SCREAMING_SNAKE_CASE_:				List[str]							= (
						    list(range(ord("!"					)       ,			ord("~"					) + 1					)					) + list(range(ord("¡"					)       ,			ord("¬"					) + 1					)					) + list(range(ord("®"					)       ,			ord("ÿ"					) + 1					)					)
						)
						SCREAMING_SNAKE_CASE_:				Any							= bs[:]
						SCREAMING_SNAKE_CASE_:				List[str]							= 0
						for b in range(2**8					):
												if b not in bs:
																		bs.append(_UpperCAmelCase					)
																		cs.append(2**8 + n					)
																		n += 1
						SCREAMING_SNAKE_CASE_:				List[str]							= [chr(_UpperCAmelCase					) for n in cs]
						return dict(zip(_UpperCAmelCase       ,			_UpperCAmelCase					)					)







def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Any							= set()
						SCREAMING_SNAKE_CASE_:				Optional[int]							= word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)					)
												SCREAMING_SNAKE_CASE_:				str							= char
						return pairs



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							str    							=  VOCAB_FILES_NAMES
			_UpperCAmelCase     :							Tuple    							=  PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase     :							Optional[Any]    							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase     :							str    							=  ['''input_ids''', '''attention_mask''']



			def __init__(				self						:  Tuple     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Optional[Any]="replace"     ,		lowerCAmelCase__						:  Optional[Any]="<|endoftext|>"     ,		lowerCAmelCase__						:  List[Any]="<|endoftext|>"     ,		lowerCAmelCase__						:  List[Any]="<|endoftext|>"     ,		lowerCAmelCase__						:  Optional[Any]=None     ,		lowerCAmelCase__						:  Dict=False     ,		lowerCAmelCase__						:  str=False     ,		**lowerCAmelCase__						:  int     ,		):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= AddedToken(lowerCAmelCase__     ,		lstrip=lowerCAmelCase__     ,		rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_:				str							= AddedToken(lowerCAmelCase__     ,		lstrip=lowerCAmelCase__     ,		rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_:				str							= AddedToken(lowerCAmelCase__     ,		lstrip=lowerCAmelCase__     ,		rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__) else unk_token
									SCREAMING_SNAKE_CASE_:				List[str]							= AddedToken(lowerCAmelCase__     ,		lstrip=lowerCAmelCase__     ,		rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__) else pad_token
									super().__init__(
									    errors=lowerCAmelCase__     ,		unk_token=lowerCAmelCase__     ,		bos_token=lowerCAmelCase__     ,		eos_token=lowerCAmelCase__     ,		pad_token=lowerCAmelCase__     ,		add_prefix_space=lowerCAmelCase__     ,		add_bos_token=lowerCAmelCase__     ,		**lowerCAmelCase__     ,		)
									SCREAMING_SNAKE_CASE_:				List[Any]							= add_bos_token

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

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



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									return len(self.encoder)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									return dict(self.encoder     ,		**self.added_tokens_encoder)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  Union[str, Any]):
									if token in self.cache:
															return self.cache[token]
									SCREAMING_SNAKE_CASE_:				List[Any]							= tuple(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= get_pairs(lowerCAmelCase__)

									if not pairs:
															return token

									while True:
															SCREAMING_SNAKE_CASE_:				Optional[int]							= min(lowerCAmelCase__     ,		key=lambda lowerCAmelCase__: self.bpe_ranks.get(lowerCAmelCase__     ,		float("inf")))
															if bigram not in self.bpe_ranks:
																					break
															SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Dict							= bigram
															SCREAMING_SNAKE_CASE_:				List[str]							= []
															SCREAMING_SNAKE_CASE_:				int							= 0
															while i < len(lowerCAmelCase__):
																					try:
																											SCREAMING_SNAKE_CASE_:				Any							= word.index(lowerCAmelCase__     ,		lowerCAmelCase__)
																					except ValueError:
																											new_word.extend(word[i:])
																											break
																					else:
																											new_word.extend(word[i:j])
																											SCREAMING_SNAKE_CASE_:				Any							= 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
															SCREAMING_SNAKE_CASE_:				str							= tuple(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				int							= new_word
															if len(lowerCAmelCase__) == 1:
																					break
															else:
																					SCREAMING_SNAKE_CASE_:				Dict							= get_pairs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= " ".join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				int							= word
									return word



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  List[Any]=None):
									if self.add_bos_token:
															SCREAMING_SNAKE_CASE_:				Optional[int]							= [self.bos_token_id]
									else:
															SCREAMING_SNAKE_CASE_:				Optional[int]							= []

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= bos_token_ids + token_ids_a

									if token_ids_a is None:
															return output

									return output + bos_token_ids + token_ids_a



			def  _SCREAMING_SNAKE_CASE    (				self						:  str     ,		lowerCAmelCase__						:  List[int]     ,		lowerCAmelCase__						:  Optional[List[int]] = None     ,		lowerCAmelCase__						:  bool = False):
									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 not self.add_bos_token:
															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__))
									return [1] + ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__))



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]     ,		lowerCAmelCase__						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Tuple							= []
									for token in re.findall(self.pat     ,		lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:				Dict							= "".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  _SCREAMING_SNAKE_CASE    (				self						:  Tuple     ,		lowerCAmelCase__						:  Any):
									return self.encoder.get(lowerCAmelCase__     ,		self.encoder.get(self.unk_token))



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  str):
									return self.decoder.get(lowerCAmelCase__)



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



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

									SCREAMING_SNAKE_CASE_:				Tuple							= 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 lowerCAmelCase__: 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!")
																											SCREAMING_SNAKE_CASE_:				str							= token_index
																					writer.write(" ".join(lowerCAmelCase__) + "\n")
																					index += 1

									return vocab_file, merge_file



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Any=False     ,		**lowerCAmelCase__						:  str):
									SCREAMING_SNAKE_CASE_:				List[str]							= kwargs.pop("add_prefix_space"     ,		self.add_prefix_space)
									if is_split_into_words or add_prefix_space:
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= " " + text
									return (text, kwargs)




			def  _SCREAMING_SNAKE_CASE    (				self						:  str     ,		lowerCAmelCase__						:  "Conversation"):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= []
									for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(lowerCAmelCase__     ,		add_special_tokens=lowerCAmelCase__) + [self.eos_token_id])
									if len(lowerCAmelCase__) > self.model_max_length:
															SCREAMING_SNAKE_CASE_:				Dict							= input_ids[-self.model_max_length :]
									return input_ids









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							:						Union[str, Any]     			=		logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							torch.FloatTensor
			_UpperCAmelCase     :							Optional[torch.FloatTensor]    							=  None







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=0.9_9_9       ,			_UpperCAmelCase="cosine"       ,			):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_UpperCAmelCase					):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_UpperCAmelCase					):
																		return math.exp(t * -1_2.0					)

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

						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= []
						for i in range(_UpperCAmelCase					):
												SCREAMING_SNAKE_CASE_:				List[str]							= i / num_diffusion_timesteps
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase					) / alpha_bar_fn(_UpperCAmelCase					)       ,			_UpperCAmelCase					)					)
						return torch.tensor(_UpperCAmelCase       ,			dtype=torch.floataa					)



class 						__lowercase       (				UpperCAmelCase_	, UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							Tuple    							=  [e.name for e in KarrasDiffusionSchedulers]
			_UpperCAmelCase     :							Optional[Any]    							=  1



			@register_to_config
			def __init__(				self						:  Optional[Any]     ,		lowerCAmelCase__						:  int = 1000     ,		lowerCAmelCase__						:  float = 0.0001     ,		lowerCAmelCase__						:  float = 0.02     ,		lowerCAmelCase__						:  str = "linear"     ,		lowerCAmelCase__						:  Optional[Union[np.ndarray, List[float]]] = None     ,		lowerCAmelCase__						:  str = "epsilon"     ,		lowerCAmelCase__						:  str = "linspace"     ,		lowerCAmelCase__						:  int = 0     ,		):
									if trained_betas is not None:
															SCREAMING_SNAKE_CASE_:				Tuple							= torch.tensor(lowerCAmelCase__     ,		dtype=torch.floataa)
									elif beta_schedule == "linear":
															SCREAMING_SNAKE_CASE_:				List[str]							= torch.linspace(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		dtype=torch.floataa)
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															SCREAMING_SNAKE_CASE_:				str							= (
															    torch.linspace(beta_start**0.5     ,		beta_end**0.5     ,		lowerCAmelCase__     ,		dtype=torch.floataa) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															SCREAMING_SNAKE_CASE_:				int							= betas_for_alpha_bar(lowerCAmelCase__)
									else:
															raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}")

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

									SCREAMING_SNAKE_CASE_:				Optional[int]							= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
									SCREAMING_SNAKE_CASE_:				str							= torch.from_numpy(lowerCAmelCase__)

									# setable values
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= None
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.linspace(0     ,		num_train_timesteps - 1     ,		lowerCAmelCase__     ,		dtype=lowerCAmelCase__)[::-1].copy()
									SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.from_numpy(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				str							= False



			@property
			def  _SCREAMING_SNAKE_CASE    (				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  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		lowerCAmelCase__						:  Union[float, torch.FloatTensor]):
									if isinstance(lowerCAmelCase__     ,		torch.Tensor):
															SCREAMING_SNAKE_CASE_:				Dict							= timestep.to(self.timesteps.device)
									SCREAMING_SNAKE_CASE_:				List[Any]							= (self.timesteps == timestep).nonzero().item()
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.sigmas[step_index]
									SCREAMING_SNAKE_CASE_:				List[str]							= sample / ((sigma**2 + 1) ** 0.5)
									SCREAMING_SNAKE_CASE_:				int							= True
									return sample



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Union[str, torch.device] = None):
									SCREAMING_SNAKE_CASE_:				Dict							= 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":
															SCREAMING_SNAKE_CASE_:				str							= np.linspace(0     ,		self.config.num_train_timesteps - 1     ,		lowerCAmelCase__     ,		dtype=lowerCAmelCase__)[
															    ::-1
															].copy()
									elif self.config.timestep_spacing == "leading":
															SCREAMING_SNAKE_CASE_:				List[Any]							= 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
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= (np.arange(0     ,		lowerCAmelCase__) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__)
															timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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
															SCREAMING_SNAKE_CASE_:				int							= (np.arange(self.config.num_train_timesteps     ,		0     ,		-step_ratio)).round().copy().astype(lowerCAmelCase__)
															timesteps -= 1
									else:
															raise ValueError(
															    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.")

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									SCREAMING_SNAKE_CASE_:				Any							= np.interp(lowerCAmelCase__     ,		np.arange(0     ,		len(lowerCAmelCase__))     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.concatenate([sigmas, [0.0]]).astype(np.floataa)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= torch.from_numpy(lowerCAmelCase__).to(device=lowerCAmelCase__)
									if str(lowerCAmelCase__).startswith("mps"):
															# mps does not support float64
															SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.from_numpy(lowerCAmelCase__).to(lowerCAmelCase__     ,		dtype=torch.floataa)
									else:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= torch.from_numpy(lowerCAmelCase__).to(device=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		lowerCAmelCase__						:  Union[float, torch.FloatTensor]     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		lowerCAmelCase__						:  Optional[torch.Generator] = None     ,		lowerCAmelCase__						:  bool = True     ,		):

									if (
									    isinstance(lowerCAmelCase__     ,		lowerCAmelCase__)
									    or isinstance(lowerCAmelCase__     ,		torch.IntTensor)
									    or isinstance(lowerCAmelCase__     ,		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(lowerCAmelCase__     ,		torch.Tensor):
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= timestep.to(self.timesteps.device)

									SCREAMING_SNAKE_CASE_:				int							= (self.timesteps == timestep).nonzero().item()
									SCREAMING_SNAKE_CASE_:				str							= self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
															SCREAMING_SNAKE_CASE_:				List[Any]							= sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`")

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.sigmas[step_index]
									SCREAMING_SNAKE_CASE_:				Tuple							= self.sigmas[step_index + 1]
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
									SCREAMING_SNAKE_CASE_:				List[str]							= (sigma_to**2 - sigma_up**2) ** 0.5

									# 2. Convert to an ODE derivative
									SCREAMING_SNAKE_CASE_:				int							= (sample - pred_original_sample) / sigma

									SCREAMING_SNAKE_CASE_:				List[str]							= sigma_down - sigma

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= sample + derivative * dt

									SCREAMING_SNAKE_CASE_:				Any							= model_output.device
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= randn_tensor(model_output.shape     ,		dtype=model_output.dtype     ,		device=lowerCAmelCase__     ,		generator=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Dict							= prev_sample + noise * sigma_up

									if not return_dict:
															return (prev_sample,)

									return EulerAncestralDiscreteSchedulerOutput(
									    prev_sample=lowerCAmelCase__     ,		pred_original_sample=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		):
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									SCREAMING_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(lowerCAmelCase__):
															# mps does not support float64
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.timesteps.to(original_samples.device     ,		dtype=torch.floataa)
															SCREAMING_SNAKE_CASE_:				List[str]							= timesteps.to(original_samples.device     ,		dtype=torch.floataa)
									else:
															SCREAMING_SNAKE_CASE_:				int							= self.timesteps.to(original_samples.device)
															SCREAMING_SNAKE_CASE_:				Dict							= timesteps.to(original_samples.device)

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

									SCREAMING_SNAKE_CASE_:				str							= original_samples + noise * sigma
									return noisy_samples




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






