"""simple docstring"""






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_A   					=    {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[Any]:
				assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
				assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
				if args.mlm:
								assert os.path.isfile(args.token_counts       )
								assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
				else:
								assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

				assert args.teacher_type == args.student_type or (
				    args.student_type == "distilbert" and args.teacher_type == "bert"
				)
				assert os.path.isfile(args.student_config       )
				if args.student_pretrained_weights is not None:
								assert os.path.isfile(args.student_pretrained_weights       )

				if args.freeze_token_type_embds:
								assert args.student_type in ["roberta"]

				assert args.alpha_ce >= 0.0
				assert args.alpha_mlm >= 0.0
				assert args.alpha_clm >= 0.0
				assert args.alpha_mse >= 0.0
				assert args.alpha_cos >= 0.0
				assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if args.student_type == "roberta":
								SCREAMING_SNAKE_CASE__		  =				False
				elif args.student_type == "gpt2":
								SCREAMING_SNAKE_CASE__		  =				False




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				if args.student_type == "roberta":
								SCREAMING_SNAKE_CASE__		  =				False




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				argparse.ArgumentParser(description="Training"       )
				parser.add_argument("--force"  ,					action="store_true"  ,					help="Overwrite dump_path if it already exists."       )

				parser.add_argument(
				    "--dump_path"  ,					type=__UpperCAmelCase  ,					required=__UpperCAmelCase  ,					help="The output directory (log, checkpoints, parameters, etc.)"       )
				parser.add_argument(
				    "--data_file"  ,					type=__UpperCAmelCase  ,					required=__UpperCAmelCase  ,					help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."  ,					)

				parser.add_argument(
				    "--student_type"  ,					type=__UpperCAmelCase  ,					choices=["distilbert", "roberta", "gpt2"]  ,					required=__UpperCAmelCase  ,					help="The student type (DistilBERT, RoBERTa)."  ,					)
				parser.add_argument("--student_config"  ,					type=__UpperCAmelCase  ,					required=__UpperCAmelCase  ,					help="Path to the student configuration."       )
				parser.add_argument(
				    "--student_pretrained_weights"  ,					default=__UpperCAmelCase  ,					type=__UpperCAmelCase  ,					help="Load student initialization checkpoint."       )

				parser.add_argument(
				    "--teacher_type"  ,					choices=["bert", "roberta", "gpt2"]  ,					required=__UpperCAmelCase  ,					help="Teacher type (BERT, RoBERTa)."       )
				parser.add_argument("--teacher_name"  ,					type=__UpperCAmelCase  ,					required=__UpperCAmelCase  ,					help="The teacher model."       )

				parser.add_argument("--temperature"  ,					default=2.0  ,					type=__UpperCAmelCase  ,					help="Temperature for the softmax temperature."       )
				parser.add_argument(
				    "--alpha_ce"  ,					default=0.5  ,					type=__UpperCAmelCase  ,					help="Linear weight for the distillation loss. Must be >=0."       )
				parser.add_argument(
				    "--alpha_mlm"  ,					default=0.0  ,					type=__UpperCAmelCase  ,					help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."  ,					)
				parser.add_argument("--alpha_clm"  ,					default=0.5  ,					type=__UpperCAmelCase  ,					help="Linear weight for the CLM loss. Must be >=0."       )
				parser.add_argument("--alpha_mse"  ,					default=0.0  ,					type=__UpperCAmelCase  ,					help="Linear weight of the MSE loss. Must be >=0."       )
				parser.add_argument(
				    "--alpha_cos"  ,					default=0.0  ,					type=__UpperCAmelCase  ,					help="Linear weight of the cosine embedding loss. Must be >=0."       )

				parser.add_argument(
				    "--mlm"  ,					action="store_true"  ,					help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."       )
				parser.add_argument(
				    "--mlm_mask_prop"  ,					default=0.15  ,					type=__UpperCAmelCase  ,					help="Proportion of tokens for which we need to make a prediction."  ,					)
				parser.add_argument("--word_mask"  ,					default=0.8  ,					type=__UpperCAmelCase  ,					help="Proportion of tokens to mask out."       )
				parser.add_argument("--word_keep"  ,					default=0.1  ,					type=__UpperCAmelCase  ,					help="Proportion of tokens to keep."       )
				parser.add_argument("--word_rand"  ,					default=0.1  ,					type=__UpperCAmelCase  ,					help="Proportion of tokens to randomly replace."       )
				parser.add_argument(
				    "--mlm_smoothing"  ,					default=0.7  ,					type=__UpperCAmelCase  ,					help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."  ,					)
				parser.add_argument("--token_counts"  ,					type=__UpperCAmelCase  ,					help="The token counts in the data_file for MLM."       )

				parser.add_argument(
				    "--restrict_ce_to_mask"  ,					action="store_true"  ,					help="If true, compute the distillation loss only the [MLM] prediction distribution."  ,					)
				parser.add_argument(
				    "--freeze_pos_embs"  ,					action="store_true"  ,					help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."  ,					)
				parser.add_argument(
				    "--freeze_token_type_embds"  ,					action="store_true"  ,					help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."  ,					)

				parser.add_argument("--n_epoch"  ,					type=__UpperCAmelCase  ,					default=3  ,					help="Number of pass on the whole dataset."       )
				parser.add_argument("--batch_size"  ,					type=__UpperCAmelCase  ,					default=5  ,					help="Batch size (for each process)."       )
				parser.add_argument(
				    "--group_by_size"  ,					action="store_false"  ,					help="If true, group sequences that have similar length into the same batch. Default is true."  ,					)

				parser.add_argument(
				    "--gradient_accumulation_steps"  ,					type=__UpperCAmelCase  ,					default=50  ,					help="Gradient accumulation for larger training batches."  ,					)
				parser.add_argument("--warmup_prop"  ,					default=0.05  ,					type=__UpperCAmelCase  ,					help="Linear warmup proportion."       )
				parser.add_argument("--weight_decay"  ,					default=0.0  ,					type=__UpperCAmelCase  ,					help="Weight decay if we apply some."       )
				parser.add_argument("--learning_rate"  ,					default=5e-4  ,					type=__UpperCAmelCase  ,					help="The initial learning rate for Adam."       )
				parser.add_argument("--adam_epsilon"  ,					default=1e-6  ,					type=__UpperCAmelCase  ,					help="Epsilon for Adam optimizer."       )
				parser.add_argument("--max_grad_norm"  ,					default=5.0  ,					type=__UpperCAmelCase  ,					help="Max gradient norm."       )
				parser.add_argument("--initializer_range"  ,					default=0.02  ,					type=__UpperCAmelCase  ,					help="Random initialization range."       )

				parser.add_argument(
				    "--fp16"  ,					action="store_true"  ,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"  ,					)
				parser.add_argument(
				    "--fp16_opt_level"  ,					type=__UpperCAmelCase  ,					default="O1"  ,					help=(
				        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
				        "See details at https://nvidia.github.io/apex/amp.html"
				    )  ,					)
				parser.add_argument("--n_gpu"  ,					type=__UpperCAmelCase  ,					default=1  ,					help="Number of GPUs in the node."       )
				parser.add_argument("--local_rank"  ,					type=__UpperCAmelCase  ,					default=-1  ,					help="Distributed training - Local rank"       )
				parser.add_argument("--seed"  ,					type=__UpperCAmelCase  ,					default=56  ,					help="Random seed"       )

				parser.add_argument("--log_interval"  ,					type=__UpperCAmelCase  ,					default=500  ,					help="Tensorboard logging interval."       )
				parser.add_argument("--checkpoint_interval"  ,					type=__UpperCAmelCase  ,					default=4_000  ,					help="Checkpoint interval."       )
				SCREAMING_SNAKE_CASE__		  =				parser.parse_args()
				sanity_checks(__UpperCAmelCase       )

				# ARGS #
				init_gpu_params(__UpperCAmelCase       )
				set_seed(__UpperCAmelCase       )
				if args.is_master:
								if os.path.exists(args.dump_path       ):
												if not args.force:
																raise ValueError(
																    F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																    " itUse `--force` if you want to overwrite it"       )
												else:
																shutil.rmtree(args.dump_path       )

								if not os.path.exists(args.dump_path       ):
												os.makedirs(args.dump_path       )
								logger.info(F"""Experiment will be dumped and logged in {args.dump_path}"""       )

								# SAVE PARAMS #
								logger.info(F"""Param: {args}"""       )
								with open(os.path.join(args.dump_path  ,					"parameters.json"       )  ,					"w"       ) as f:
												json.dump(vars(__UpperCAmelCase       )  ,					__UpperCAmelCase  ,					indent=4       )
								git_log(args.dump_path       )

				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				MODEL_CLASSES[args.student_type]
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				MODEL_CLASSES[args.teacher_type]

				# TOKENIZER #
				SCREAMING_SNAKE_CASE__		  =				teacher_tokenizer_class.from_pretrained(args.teacher_name       )
				SCREAMING_SNAKE_CASE__		  =				{}
				for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
								SCREAMING_SNAKE_CASE__		  =				tokenizer.all_special_tokens.index(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				tokenizer.all_special_ids[idx]
				logger.info(F"""Special tokens {special_tok_ids}"""       )
				SCREAMING_SNAKE_CASE__		  =				special_tok_ids
				SCREAMING_SNAKE_CASE__		  =				tokenizer.max_model_input_sizes[args.teacher_name]

				# DATA LOADER #
				logger.info(F"""Loading data from {args.data_file}"""       )
				with open(args.data_file  ,					"rb"       ) as fp:
								SCREAMING_SNAKE_CASE__		  =				pickle.load(__UpperCAmelCase       )

				if args.mlm:
								logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""       )
								with open(args.token_counts  ,					"rb"       ) as fp:
												SCREAMING_SNAKE_CASE__		  =				pickle.load(__UpperCAmelCase       )

								SCREAMING_SNAKE_CASE__		  =				np.maximum(__UpperCAmelCase  ,					1       ) ** -args.mlm_smoothing
								for idx in special_tok_ids.values():
												SCREAMING_SNAKE_CASE__		  =				0.0  # do not predict special tokens
								SCREAMING_SNAKE_CASE__		  =				torch.from_numpy(__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				None

				SCREAMING_SNAKE_CASE__		  =				LmSeqsDataset(params=__UpperCAmelCase  ,					data=__UpperCAmelCase       )
				logger.info("Data loader created."       )

				# STUDENT #
				logger.info(F"""Loading student config from {args.student_config}"""       )
				SCREAMING_SNAKE_CASE__		  =				student_config_class.from_pretrained(args.student_config       )
				SCREAMING_SNAKE_CASE__		  =				True

				if args.student_pretrained_weights is not None:
								logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""       )
								SCREAMING_SNAKE_CASE__		  =				student_model_class.from_pretrained(args.student_pretrained_weights  ,					config=__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				student_model_class(__UpperCAmelCase       )

				if args.n_gpu > 0:
								student.to(F"""cuda:{args.local_rank}"""       )
				logger.info("Student loaded."       )

				# TEACHER #
				SCREAMING_SNAKE_CASE__		  =				teacher_model_class.from_pretrained(args.teacher_name  ,					output_hidden_states=__UpperCAmelCase       )
				if args.n_gpu > 0:
								teacher.to(F"""cuda:{args.local_rank}"""       )
				logger.info(F"""Teacher loaded from {args.teacher_name}."""       )

				# FREEZING #
				if args.freeze_pos_embs:
								freeze_pos_embeddings(__UpperCAmelCase  ,					__UpperCAmelCase       )
				if args.freeze_token_type_embds:
								freeze_token_type_embeddings(__UpperCAmelCase  ,					__UpperCAmelCase       )

				# SANITY CHECKS #
				assert student.config.vocab_size == teacher.config.vocab_size
				assert student.config.hidden_size == teacher.config.hidden_size
				assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
				if args.mlm:
								assert token_probs.size(0       ) == stu_architecture_config.vocab_size

				# DISTILLER #
				torch.cuda.empty_cache()
				SCREAMING_SNAKE_CASE__		  =				Distiller(
				    params=__UpperCAmelCase  ,					dataset=__UpperCAmelCase  ,					token_probs=__UpperCAmelCase  ,					student=__UpperCAmelCase  ,					teacher=__UpperCAmelCase       )
				distiller.train()
				logger.info("Let's go get some drinks."       )


if __name__ == "__main__":
						main()





"""simple docstring"""






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_A   					=    8




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=BITS       )							->   Union[str, Any]:
				SCREAMING_SNAKE_CASE__		  =				x.device

				SCREAMING_SNAKE_CASE__		  =				(x * 255).int().clamp(0  ,					255       )

				SCREAMING_SNAKE_CASE__		  =				2 ** torch.arange(bits - 1  ,					-1  ,					-1  ,					device=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"d -> d 1 1"       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b c h w -> b c 1 h w"       )

				SCREAMING_SNAKE_CASE__		  =				((x & mask) != 0).float()
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b c d h w -> b (c d) h w"       )
				SCREAMING_SNAKE_CASE__		  =				bits * 2 - 1
				return bits




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=BITS       )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				x.device

				SCREAMING_SNAKE_CASE__		  =				(x > 0).int()
				SCREAMING_SNAKE_CASE__		  =				2 ** torch.arange(bits - 1  ,					-1  ,					-1  ,					device=__UpperCAmelCase  ,					dtype=torch.intaa       )

				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"d -> d 1 1"       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b (c d) h w -> b c d h w"  ,					d=8       )
				SCREAMING_SNAKE_CASE__		  =				reduce(x * mask  ,					"b c d h w -> b c h w"  ,					"sum"       )
				return (dec / 255).clamp(0.0  ,					1.0       )




def 						SCREAMING_SNAKE_CASE					(  self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 0.0  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase = True  ,					)							->   Union[DDIMSchedulerOutput, Tuple]:
				if self.num_inference_steps is None:
								raise ValueError(
								    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"       )

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				SCREAMING_SNAKE_CASE__		  =				timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[timestep]
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				SCREAMING_SNAKE_CASE__		  =				1 - alpha_prod_t

				# 3. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				SCREAMING_SNAKE_CASE__		  =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				SCREAMING_SNAKE_CASE__		  =				self.bit_scale
				if self.config.clip_sample:
								SCREAMING_SNAKE_CASE__		  =				torch.clamp(__UpperCAmelCase  ,					-scale  ,					__UpperCAmelCase       )

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				SCREAMING_SNAKE_CASE__		  =				self._get_variance(__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								SCREAMING_SNAKE_CASE__		  =				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

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

				# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				SCREAMING_SNAKE_CASE__		  =				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								SCREAMING_SNAKE_CASE__		  =				model_output.device if torch.is_tensor(__UpperCAmelCase       ) else "cpu"
								SCREAMING_SNAKE_CASE__		  =				torch.randn(model_output.shape  ,					dtype=model_output.dtype  ,					generator=__UpperCAmelCase       ).to(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				self._get_variance(__UpperCAmelCase  ,					__UpperCAmelCase       ) ** 0.5 * eta * noise

								SCREAMING_SNAKE_CASE__		  =				prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=__UpperCAmelCase  ,					pred_original_sample=__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase="epsilon"  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase = True  ,					)							->   Union[DDPMSchedulerOutput, Tuple]:
				SCREAMING_SNAKE_CASE__		  =				timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				torch.split(__UpperCAmelCase  ,					sample.shape[1]  ,					dim=1       )
				else:
								SCREAMING_SNAKE_CASE__		  =				None

				# 1. compute alphas, betas
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[t - 1] if t > 0 else self.one
				SCREAMING_SNAKE_CASE__		  =				1 - alpha_prod_t
				SCREAMING_SNAKE_CASE__		  =				1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								SCREAMING_SNAKE_CASE__		  =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								SCREAMING_SNAKE_CASE__		  =				model_output
				else:
								raise ValueError(F"""Unsupported prediction_type {prediction_type}."""       )

				# 3. Clip "predicted x_0"
				SCREAMING_SNAKE_CASE__		  =				self.bit_scale
				if self.config.clip_sample:
								SCREAMING_SNAKE_CASE__		  =				torch.clamp(__UpperCAmelCase  ,					-scale  ,					__UpperCAmelCase       )

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE__		  =				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				SCREAMING_SNAKE_CASE__		  =				self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE__		  =				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				SCREAMING_SNAKE_CASE__		  =				0
				if t > 0:
								SCREAMING_SNAKE_CASE__		  =				torch.randn(
								    model_output.size()  ,					dtype=model_output.dtype  ,					layout=model_output.layout  ,					generator=__UpperCAmelCase       ).to(model_output.device       )
								SCREAMING_SNAKE_CASE__		  =				(self._get_variance(__UpperCAmelCase  ,					predicted_variance=__UpperCAmelCase       ) ** 0.5) * noise

				SCREAMING_SNAKE_CASE__		  =				pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=__UpperCAmelCase  ,					pred_original_sample=__UpperCAmelCase       )

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				Optional[int]   , _snake_case			:				UNetaDConditionModel   , _snake_case			:				Union[DDIMScheduler, DDPMScheduler]   , _snake_case			:				Optional[float] = 1.0   , )							->       Optional[int]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				bit_scale
									SCREAMING_SNAKE_CASE__		  =				(
									    ddim_bit_scheduler_step if isinstance(_snake_case   , _snake_case		) else ddpm_bit_scheduler_step
									)

									self.register_modules(unet=_snake_case   , scheduler=_snake_case		)



					@torch.no_grad()
					def __call__(						self			:				Any   , _snake_case			:				Optional[int] = 256   , _snake_case			:				Optional[int] = 256   , _snake_case			:				Optional[int] = 50   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				Optional[int] = 1   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , **_snake_case			:				Tuple   , )							->       Union[Tuple, ImagePipelineOutput]:
									SCREAMING_SNAKE_CASE__		  =				torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width)   , generator=_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				decimal_to_bits(_snake_case		) * self.bit_scale
									SCREAMING_SNAKE_CASE__		  =				latents.to(self.device		)

									self.scheduler.set_timesteps(_snake_case		)

									for t in self.progress_bar(self.scheduler.timesteps		):
													# predict the noise residual
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _snake_case		).sample

													# compute the previous noisy sample x_t -> x_t-1
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(_snake_case   , _snake_case   , _snake_case		).prev_sample

									SCREAMING_SNAKE_CASE__		  =				bits_to_decimal(_snake_case		)

									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




