





import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa






class 							_a(     __A     ):
		lowerCamelCase__  :List[Any] 					=		['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







		def __init__(     self			,    __snake_case					)   ->   List[str]:


							'''simple docstring'''

							assert Path(hparams.data_dir					).exists()
							_snake_case							:       Any												= Path(hparams.output_dir					)
							self.output_dir.mkdir(exist_ok=__snake_case					)

							_snake_case							:       List[str]												= self.output_dir.joinpath("student"					)

							_snake_case							:       List[str]												= str(__snake_case					)  # Tell lightning we are training the student
							_snake_case							:       Tuple												= AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher					).eval()
							use_task_specific_params(__snake_case			,    hparams.task					)  # We copy good generation parameters to student by default
							if hparams.student is not None:
												_snake_case							:       Dict												= AutoModelForSeqaSeqLM.from_pretrained(hparams.student					)
												use_task_specific_params(__snake_case			,    hparams.task					)
												_snake_case							,			_snake_case							:       Tuple												= None, None
							else:
												_snake_case							,			_snake_case							,			_snake_case							:       Optional[Any]												= create_student_by_copying_alternating_layers(
												    __snake_case			,    e=hparams.student_encoder_layers			,    d=hparams.student_decoder_layers			,    save_path=__snake_case					)

							if hparams.length_penalty != -1:
												_snake_case							:       int												= hparams.length_penalty
							_snake_case							:       Any												= hparams.teacher  # Use teacher's tokenizer
							super().__init__(__snake_case			,    model=__snake_case			,    config=student.config					)
							assert student.config.model_type == teacher.config.model_type, (
							    f"""teacher, student model types should be the same, got {student.config.model_type} !="""
							    f""" {teacher.config.model_type}"""
							)

							if student.config.model_type == "t5":
												_snake_case							:       Tuple												= len(student.get_encoder().block					)
												_snake_case							:       int												= len(student.get_decoder().block					)
												_snake_case							:       List[str]												= len(teacher.get_encoder().block					)
												_snake_case							:       Optional[int]												= len(teacher.get_decoder().block					)
							else:
												_snake_case							:       int												= student.config.encoder_layers
												_snake_case							:       str												= student.config.decoder_layers
												_snake_case							:       List[str]												= teacher.config.encoder_layers
												_snake_case							:       str												= teacher.config.decoder_layers

							_snake_case							:       Any												= not (hparams.student is None or hparams.teacher == hparams.student)
							_snake_case							:       str												= (not self.different_base_models) and hparams.alpha_hid > 0
							_snake_case							:       Tuple												= self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
							# self.different_encoder determines whether we need to run the teacher encoder
							_snake_case							:       List[str]												= teacher
							freeze_params(self.teacher					)

							if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
												try:
																	del self.teacher.model.encoder
												except AttributeError:  # T5
																	del self.teacher.encoder

							if e_layer_ids is None:
												_snake_case							:       List[Any]												= list(range(__snake_case					)					)
							if d_layer_ids is None:
												_snake_case							:       int												= list(range(__snake_case					)					)

							_snake_case							,			_snake_case							:       List[str]												= e_layer_ids, d_layer_ids  # type: List[int], List[int]

							if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
												if hparams.supervise_forward:
																	_snake_case							:       str												= get_layers_to_supervise(
																	    n_student=len(self.e_layer_ids					)			,    n_teacher=__snake_case					)
																	_snake_case							:       List[Any]												= get_layers_to_supervise(
																	    n_student=len(self.d_layer_ids					)			,    n_teacher=__snake_case					)
												else:  # student layer should emulate hidden states of the teacher layer it was copied from
																	_snake_case							:       Optional[Any]												= self.e_layer_ids
																	_snake_case							:       int												= self.d_layer_ids
							else:
												_snake_case							:       int												= None
												_snake_case							:       Any												= None

							_snake_case							:       List[str]												= nn.KLDivLoss(reduction="batchmean"					)
							_snake_case							:       Any												= 2.0
							_snake_case							:       int												= hparams.alpha_mlm
							_snake_case							:       Optional[Any]												= hparams.alpha_ce
							_snake_case							:       Optional[int]												= hparams.alpha_hid
							gc.collect()
							torch.cuda.empty_cache()







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Dict												= mask[:, :, None].expand_as(__snake_case					)
							_snake_case							:       int												= s_logits.size(-1					)
							_snake_case							:       List[str]												= torch.masked_select(__snake_case			,    __snake_case					)  # (bs * seq_length * voc_size) modulo the 1s in mask
							_snake_case							:       Optional[int]												= torch.masked_select(__snake_case			,    __snake_case					)  # (bs * seq_length * voc_size) modulo the 1s in mask
							_snake_case							:       str												= s_logits_slct.view(-1			,    __snake_case					)  # (bs * seq_length, voc_size) modulo the 1s in mask
							_snake_case							:       Union[str, Any]												= t_logits_slct.view(-1			,    __snake_case					)  # (bs * seq_length, voc_size) modulo the 1s in mask
							assert t_logits_slct.size() == s_logits_slct.size()
							_snake_case							:       List[str]												= (
							    self.ce_loss_fct(
							        nn.functional.log_softmax(s_logits_slct / self.temperature			,    dim=-1					)			,    nn.functional.softmax(t_logits_slct / self.temperature			,    dim=-1					)			,    )
							    * (self.temperature) ** 2
							)
							return loss_ce







		@staticmethod
		def 							lowercase							(     __snake_case			,    __snake_case					)   ->   List[Any]:


							'''simple docstring'''

							SummarizationModule.add_model_specific_args(__snake_case			,    __snake_case					)
							add_distill_args(__snake_case					)
							return parser







		def 							lowercase							(     self			,    __snake_case					)   ->   tuple:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= self.tokenizer.pad_token_id
							_snake_case							,			_snake_case							,			_snake_case							:       int												= batch["input_ids"], batch["attention_mask"], batch["labels"]
							if isinstance(self.model			,    __snake_case					):
												_snake_case							:       Optional[Any]												= self.model._shift_right(__snake_case					)
							else:
												_snake_case							:       Dict												= shift_tokens_right(__snake_case			,    __snake_case					)

							# noinspection PyCallingNonCallable
							_snake_case							:       List[Any]												= self(
							    __snake_case			,    attention_mask=__snake_case			,    decoder_input_ids=__snake_case			,    output_hidden_states=self.do_calc_hidden_loss			,    output_attentions=__snake_case			,    use_cache=__snake_case			,    )
							_snake_case							:       Dict												= student_outputs["logits"]

							# Same cross entropy vs. label smoothing logic as finetune.py
							assert lm_logits.shape[-1] == self.model.config.vocab_size
							if self.hparams.label_smoothing == 0:
												# Same behavior as modeling_bart.py, besides ignoring pad_token_id
												_snake_case							:       Union[str, Any]												= nn.CrossEntropyLoss(ignore_index=__snake_case					)
												_snake_case							:       int												= loss_fct(lm_logits.view(-1			,    lm_logits.shape[-1]					)			,    labels.view(-1					)					)
							else:
												_snake_case							:       Optional[Any]												= nn.functional.log_softmax(__snake_case			,    dim=-1					)
												_snake_case							,			_snake_case							:       Optional[int]												= label_smoothed_nll_loss(
												    __snake_case			,    __snake_case			,    self.hparams.label_smoothing			,    ignore_index=__snake_case					)

							def zero_tensor():
												return torch.tensor(0.0					).type_as(__snake_case					)

							_snake_case							:       List[str]												= student_outputs[
							    "encoder_last_hidden_state"
							]  # use this unless self.different_base_models
							_snake_case							,			_snake_case							:       str												= zero_tensor(), zero_tensor()
							if self.different_encoder:  # compute encoder hidden state loss
												_snake_case							:       Any												= self.teacher.get_encoder()(
												    __snake_case			,    attention_mask=__snake_case			,    output_hidden_states=self.do_calc_hidden_loss			,    )
												if self.different_base_models:
																	_snake_case							:       Optional[Any]												= all_teacher_encoder_outputs["last_hidden_state"]
												elif self.do_calc_hidden_loss:
																	_snake_case							:       List[Any]												= self.calc_hidden_loss(
																	    __snake_case			,    student_outputs["encoder_hidden_states"]			,    all_teacher_encoder_outputs["hidden_states"]			,    self.e_matches			,    normalize_hidden=self.hparams.normalize_hidden			,    )

							_snake_case							:       List[Any]												= self.teacher(
							    __snake_case			,    attention_mask=__snake_case			,    encoder_outputs=(teacher_enc_outputs,)			,    decoder_input_ids=__snake_case			,    output_hidden_states=self.do_calc_hidden_loss			,    use_cache=__snake_case			,    )
							_snake_case							:       int												= decoder_input_ids.ne(__snake_case					)
							_snake_case							:       List[Any]												= self.calc_ce_loss(__snake_case			,    __snake_case			,    teacher_outputs["logits"]					)
							if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
												_snake_case							:       str												= self.calc_hidden_loss(
												    __snake_case			,    student_outputs["decoder_hidden_states"]			,    teacher_outputs["decoder_hidden_states"]			,    self.d_matches			,    normalize_hidden=self.hparams.normalize_hidden			,    )

							_snake_case							:       Tuple												= (
							    self.alpha_ce * loss_ce
							    + self.alpha_mlm * student_lm_loss
							    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
							)
							return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







		@staticmethod
		def 							lowercase							(     __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case					)   ->   Union[str, Any]:


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= "expected list or tuple for hidden_states, got tensor of shape: "
							assert not isinstance(__snake_case			,    torch.Tensor					), f"""{msg}{hidden_states.shape}"""
							assert not isinstance(__snake_case			,    torch.Tensor					), f"""{msg}{hidden_states_T.shape}"""
							_snake_case							:       Optional[int]												= attention_mask.to(hidden_states[0]					)
							_snake_case							:       Optional[int]												= mask.sum() * hidden_states[0].size(-1					)
							_snake_case							:       Tuple												= torch.stack([hidden_states[i] for i in range(len(__snake_case					)					)]					)
							_snake_case							:       Optional[int]												= torch.stack([hidden_states_T[j] for j in matches]					)
							assert student_states.shape == teacher_states.shape, f"""{student_states.shape} != {teacher_states.shape}"""
							if normalize_hidden:
												_snake_case							:       int												= nn.functional.layer_norm(__snake_case			,    student_states.shape[1:]					)
												_snake_case							:       List[Any]												= nn.functional.layer_norm(__snake_case			,    teacher_states.shape[1:]					)
							_snake_case							:       List[str]												= nn.functional.mse_loss(__snake_case			,    __snake_case			,    reduction="none"					)
							_snake_case							:       Optional[int]												= (mse * mask.unsqueeze(0					).unsqueeze(-1					)).sum() / valid_count
							return masked_mse







def  A					(				UpperCAmelCase					):
					# NOTE: if --student argument was specified and the teacher and student base models
					# are different, the models still have to have the same tokenizer, specified by
					# --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
					# from bart to t5. This s because if the tokenizers are different, the output space
					# for the two models is also different and their logits are not comparable.
					parser.add_argument("--teacher"	,	type=UpperCAmelCase					)
					parser.add_argument("--alpha_ce"	,	default=0.8	,	type=UpperCAmelCase					)
					parser.add_argument("--alpha_mlm"	,	default=0.2	,	type=UpperCAmelCase					)
					parser.add_argument("--alpha_hid"	,	default=0.0	,	type=UpperCAmelCase	,	required=UpperCAmelCase					)
					parser.add_argument("--student"	,	type=UpperCAmelCase	,	required=UpperCAmelCase					)
					parser.add_argument("--student_decoder_layers"	,	default=12	,	type=UpperCAmelCase	,	required=UpperCAmelCase					)
					parser.add_argument("--student_encoder_layers"	,	default=12	,	type=UpperCAmelCase	,	required=UpperCAmelCase					)
					parser.add_argument("--no_teacher"	,	action="store_true"	,	default=UpperCAmelCase					)
					parser.add_argument("--length_penalty"	,	type=UpperCAmelCase	,	default=-1					)
					parser.add_argument("--supervise_forward"	,	action="store_true"	,	default=UpperCAmelCase					)
					parser.add_argument("--normalize_hidden"	,	action="store_true"	,	default=UpperCAmelCase					)






class 							_a(     __A     ):
		lowerCamelCase__  :str 					=		'translation'
		lowerCamelCase__  :Tuple 					=		['bleu']
		lowerCamelCase__  :Optional[Any] 					=		'bleu'







		def __init__(     self			,    __snake_case			,    **__snake_case					)   ->   str:


							'''simple docstring'''

							super().__init__(__snake_case			,    **__snake_case					)
							assert hparams.src_lang is not None
							assert hparams.tgt_lang is not None
							_snake_case							:       Optional[int]												= hparams.src_lang
							_snake_case							:       int												= hparams.tgt_lang
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,    __snake_case					):
												_snake_case							:       Union[str, Any]												= self.tokenizer.lang_code_to_id[hparams.tgt_lang]







		def 							lowercase							(     self			,    __snake_case			,    __snake_case					)   ->   dict:


							'''simple docstring'''

							return calculate_bleu(__snake_case			,    __snake_case					)







		@staticmethod
		def 							lowercase							(     __snake_case			,    __snake_case					)   ->   Any:


							'''simple docstring'''

							TranslationModule.add_model_specific_args(__snake_case			,    __snake_case					)
							add_distill_args(__snake_case					)
							return parser







def  A					(				UpperCAmelCase					):
					if args.no_teacher:
										_snake_case							:       List[Any]												= TranslationModule if "translation" in args.task else SummarizationModule
					else:  # DISTILL WITH TEACHER
										_snake_case							:       Any												= TranslationDistiller if "translation" in args.task else SummarizationDistiller
					_snake_case							:       str												= module_cls.__name__
					print(F"""using module {args.setup_cls}"""					)
					_snake_case							:       Any												= module_cls(UpperCAmelCase					)
					return model







def  A					(				UpperCAmelCase					):
					Path(args.output_dir					).mkdir(exist_ok=UpperCAmelCase					)
					check_output_dir(UpperCAmelCase	,	expected_items=3					)

					_snake_case							:       Dict												= create_module(UpperCAmelCase					)
					return ft_main(UpperCAmelCase	,	model=UpperCAmelCase					)


if __name__ == "__main__":
							__lowerCAmelCase			:List[Any]	 =     argparse.ArgumentParser()
							__lowerCAmelCase			:Optional[int]	 =     pl.Trainer.add_argparse_args(parser)
							__lowerCAmelCase			:Tuple	 =     SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
							__lowerCAmelCase			:List[Any]	 =     parser.parse_args()

							distill_main(args)






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

import numpy as np
import torch

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






@dataclass
class 							_a(     __A     ):
		lowerCamelCase__  :torch.FloatTensor
		lowerCamelCase__  :torch.FloatTensor







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}"""					)

					_snake_case							:       Optional[int]												= []
					for i in range(UpperCAmelCase					):
										_snake_case							:       Optional[int]												= i / num_diffusion_timesteps
										_snake_case							:       int												= (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 							_a(     __A						, __A     ):
		lowerCamelCase__  :Any 					=		1







		@register_to_config
		def __init__(     self			,    __snake_case = 1_0_0_0			,    __snake_case = 0.00_01			,    __snake_case = 0.02			,    __snake_case = "linear"			,    __snake_case = 0.0			,    __snake_case = None			,    __snake_case = True			,    )   ->   Tuple:


							'''simple docstring'''

							if trained_betas is not None:
												_snake_case							:       Any												= torch.from_numpy(__snake_case					)
							elif beta_schedule == "linear":
												_snake_case							:       List[Any]												= torch.linspace(__snake_case			,    __snake_case			,    __snake_case			,    dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												_snake_case							:       Tuple												= (
												    torch.linspace(beta_start**0.5			,    beta_end**0.5			,    __snake_case			,    dtype=torch.floataa					) ** 2
												)
							elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												_snake_case							:       Optional[int]												= betas_for_alpha_bar(__snake_case					)
							elif beta_schedule == "sigmoid":
												# GeoDiff sigmoid schedule
												_snake_case							:       Union[str, Any]												= torch.linspace(-6			,    6			,    __snake_case					)
												_snake_case							:       Optional[Any]												= torch.sigmoid(__snake_case					) * (beta_end - beta_start) + beta_start
							else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

							_snake_case							:       Optional[int]												= 1.0 - self.betas
							_snake_case							:       Tuple												= torch.cumprod(self.alphas			,    dim=0					)
							_snake_case							:       List[str]												= torch.tensor(1.0					)

							_snake_case							:       Any												= torch.tensor(1.0					)

							# standard deviation of the initial noise distribution
							_snake_case							:       int												= 1.0

							# setable values
							_snake_case							:       Optional[Any]												= None
							_snake_case							:       str												= torch.from_numpy(np.arange(0			,    __snake_case					)[::-1].copy()					)

							_snake_case							:       Any												= eta







		def 							lowercase							(     self			,    __snake_case			,    __snake_case = None					)   ->   torch.FloatTensor:


							'''simple docstring'''

							return sample







		def 							lowercase							(     self			,    __snake_case			,    __snake_case = 1_0			,    __snake_case = 1_0			,    __snake_case = None			,    )   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= min(self.config.num_train_timesteps			,    __snake_case					)
							_snake_case							:       Tuple												= num_inference_steps

							_snake_case							:       List[str]												= []

							_snake_case							:       str												= {}
							for j in range(0			,    num_inference_steps - jump_length			,    __snake_case					):
												_snake_case							:       Tuple												= jump_n_sample - 1

							_snake_case							:       int												= num_inference_steps
							while t >= 1:
												_snake_case							:       Union[str, Any]												= t - 1
												timesteps.append(__snake_case					)

												if jumps.get(__snake_case			,    0					) > 0:
																	_snake_case							:       Any												= jumps[t] - 1
																	for _ in range(__snake_case					):
																						_snake_case							:       List[str]												= t + 1
																						timesteps.append(__snake_case					)

							_snake_case							:       Optional[Any]												= np.array(__snake_case					) * (self.config.num_train_timesteps // self.num_inference_steps)
							_snake_case							:       int												= torch.from_numpy(__snake_case					).to(__snake_case					)







		def 							lowercase							(     self			,    __snake_case					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= t - self.config.num_train_timesteps // self.num_inference_steps

							_snake_case							:       Optional[int]												= self.alphas_cumprod[t]
							_snake_case							:       Union[str, Any]												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case							:       Optional[Any]												= 1 - alpha_prod_t
							_snake_case							:       str												= 1 - alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from
							# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
							# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
							# variance to pred_sample
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							_snake_case							:       Dict												= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case = None			,    __snake_case = True			,    )   ->   Union[RePaintSchedulerOutput, Tuple]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= timestep
							_snake_case							:       Optional[int]												= timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							_snake_case							:       Optional[Any]												= self.alphas_cumprod[t]
							_snake_case							:       Any												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case							:       Dict												= 1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							_snake_case							:       List[str]												= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
												_snake_case							:       str												= torch.clamp(__snake_case			,    -1			,    1					)

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							_snake_case							:       Tuple												= model_output.device
							_snake_case							:       Any												= randn_tensor(model_output.shape			,    generator=__snake_case			,    device=__snake_case			,    dtype=model_output.dtype					)
							_snake_case							:       Tuple												= self.eta * self._get_variance(__snake_case					) ** 0.5

							_snake_case							:       str												= 0
							if t > 0 and self.eta > 0:
												_snake_case							:       List[str]												= std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case							:       List[str]												= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case							:       int												= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case							:       Tuple												= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case							:       int												= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
												return (
												    pred_prev_sample,
												    pred_original_sample,
												)

							return RePaintSchedulerOutput(prev_sample=__snake_case			,    pred_original_sample=__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case=None					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.config.num_train_timesteps // self.num_inference_steps

							for i in range(__snake_case					):
												_snake_case							:       List[Any]												= self.betas[timestep + i]
												if sample.device.type == "mps":
																	# randn does not work reproducibly on mps
																	_snake_case							:       List[Any]												= randn_tensor(sample.shape			,    dtype=sample.dtype			,    generator=__snake_case					)
																	_snake_case							:       int												= noise.to(sample.device					)
												else:
																	_snake_case							:       Any												= randn_tensor(sample.shape			,    generator=__snake_case			,    device=sample.device			,    dtype=sample.dtype					)

												# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
												_snake_case							:       List[Any]												= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    )   ->   torch.FloatTensor:


							'''simple docstring'''

							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."					)







		def __len__(     self					)   ->   Optional[Any]:


							'''simple docstring'''

							return self.config.num_train_timesteps