


import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :Any      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :List[str]=False      ,      lowerCAmelCase__      :int=1_024      ,      lowerCAmelCase__      :Tuple=1_024      ,      lowerCAmelCase__      :Optional[Any]=3.6      ,      lowerCAmelCase__      :Optional[Any]=False      ,      )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		tokenizer
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tokenizer.bos_token_id
										__SCREAMING_SNAKE_CASE						:					int		      =		dataset
										__SCREAMING_SNAKE_CASE						:					Any		      =		seq_length
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		0
										__SCREAMING_SNAKE_CASE						:					Any		      =		infinite
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		0
										__SCREAMING_SNAKE_CASE						:					Dict		      =		tokenized

										if self.tokenized:
																__SCREAMING_SNAKE_CASE						:					str		      =		seq_length * num_of_sequences
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		'''input_ids'''
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		seq_length * chars_per_token * num_of_sequences
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		'''content'''


				def __iter__(	self      :int     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		iter(self.dataset     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		True
										while more_examples:
																__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[Any]		      =		[], 0
																while True:
																						if buffer_len >= self.max_buffer_size:
																												break
																						try:
																												buffer.append(next(lowerCAmelCase__     )[self.content_field]     )
																												buffer_len += len(buffer[-1]     )
																						except StopIteration:
																												if self.infinite:
																																		__SCREAMING_SNAKE_CASE						:					Dict		      =		iter(self.dataset     )
																																		self.epoch += 1
																																		logger.info(f'''Dataset epoch: {self.epoch}'''     )
																												else:
																																		__SCREAMING_SNAKE_CASE						:					int		      =		False
																																		break
																if self.tokenized:
																						__SCREAMING_SNAKE_CASE						:					str		      =		buffer
																else:
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.tokenizer(lowerCAmelCase__      ,      truncation=lowerCAmelCase__     )['''input_ids''']
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		[]
																for tokenized_input in tokenized_inputs:
																						all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
																for i in range(0      ,      len(lowerCAmelCase__     )      ,      self.seq_length     ):
																						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		all_token_ids[i : i + self.seq_length]
																						if len(lowerCAmelCase__     ) == self.seq_length:
																												self.current_size += 1
																												yield torch.tensor(lowerCAmelCase__     )



				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Dict=1_000     )		->      List[str]:
										return ShufflerIterDataPipe(self      ,      buffer_size=lowerCAmelCase__     )




def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Any		      =		args.model_ckpt.split('''/'''				)[-1]
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		logging.getLogger(__name__				)
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		Path(args.save_dir				) / '''log/'''
						log_dir.mkdir(exist_ok=lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		F'''debug_{accelerator.process_index}.log'''
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,	datefmt='''%m/%d/%Y %H:%M:%S'''   ,	level=logging.INFO   ,	handlers=[logging.FileHandler(log_dir / filename				), logging.StreamHandler()]   ,	)
						if accelerator.is_main_process:  # we only want to setup logging once
												accelerator.init_trackers(lowercase__   ,	vars(lowercase__				)				)
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO				)
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		''''''
												logger.setLevel(logging.ERROR				)
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		{'''streaming''': True}
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		load_dataset(args.dataset_name_train   ,	split='''train'''   ,	**lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		train_data.shuffle(buffer_size=args.shuffle_buffer   ,	seed=args.seed				)
						__SCREAMING_SNAKE_CASE						:					Any		      =		load_dataset(args.dataset_name_valid   ,	split='''train'''   ,	**lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		ConstantLengthDataset(
						    lowercase__   ,	lowercase__   ,	infinite=lowercase__   ,	seq_length=args.seq_length   ,	tokenized=args.tokenized				)
						__SCREAMING_SNAKE_CASE						:					Dict		      =		ConstantLengthDataset(
						    lowercase__   ,	lowercase__   ,	infinite=lowercase__   ,	seq_length=args.seq_length   ,	tokenized=args.tokenized				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		DataLoader(lowercase__   ,	batch_size=args.train_batch_size   ,	shuffle=lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		DataLoader(lowercase__   ,	batch_size=args.valid_batch_size				)
						return train_dataloader, eval_dataloader


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				):
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[Any]		      =		[], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay				):
																		params_without_wd.append(lowercase__				)
												else:
																		params_with_wd.append(lowercase__				)
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						logger.info(F'''Step {step}: {metrics}'''				)
						if accelerator.is_main_process:
												accelerator.log(lowercase__   ,	lowercase__				)


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		accelerator.unwrap_model(lowercase__				).config
						__SCREAMING_SNAKE_CASE						:					Dict		      =		4 if args.gradient_checkpointing else 3
						__SCREAMING_SNAKE_CASE						:					Dict		      =		args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		factor * (
						    1.0
						    + (args.seq_length / (6.0 * config_model.n_embd))
						    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
						)
						__SCREAMING_SNAKE_CASE						:					Dict		      =		flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops


def 	_UpperCamelCase							(				lowercase__				):
						model.eval()
						__SCREAMING_SNAKE_CASE						:					int		      =		[]
						for step, batch in enumerate(lowercase__				):
												with torch.no_grad():
																		__SCREAMING_SNAKE_CASE						:					List[str]		      =		model(lowercase__   ,	labels=lowercase__				)
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		outputs.loss.repeat(args.valid_batch_size				)
												losses.append(accelerator.gather(lowercase__				)				)
												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.cat(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Dict		      =		losses[: eval_dataloader.dataset.current_size].mean()
						try:
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.exp(lowercase__				)
						except OverflowError:
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		float('''inf'''				)
						return loss.item(), perplexity.item()


# Settings
__lowerCAmelCase     :			List[Any]      =HfArgumentParser(TrainingArguments)
__lowerCAmelCase     :			Union[str, Any]      =parser.parse_args()

# Accelerator
__lowerCAmelCase     :			List[Any]      =ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
__lowerCAmelCase     :			Tuple      =Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
__lowerCAmelCase     :			str      ={str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__lowerCAmelCase     :			Tuple      =Namespace(**vars(args), **acc_state)
__lowerCAmelCase     :			List[str]      =accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
						__lowerCAmelCase     :			Optional[int]      =Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowerCAmelCase    ,__lowerCAmelCase     :			str      =setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
						hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__lowerCAmelCase     :			Optional[int]      =AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
__lowerCAmelCase     :			Optional[Any]      =AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowerCAmelCase    ,__lowerCAmelCase     :			List[str]      =create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowerCAmelCase     :			Dict      =AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCAmelCase     :			Dict      =get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)


def 	_UpperCamelCase							(				):
						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowerCAmelCase    ,__lowerCAmelCase    ,__lowerCAmelCase    ,__lowerCAmelCase     :			int      =accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}""")
												accelerator.load_state(args.resume_from_checkpoint)
												__lowerCAmelCase     :			Union[str, Any]      =os.path.basename(args.resume_from_checkpoint)
						else:
												# Get the most recent checkpoint
												__lowerCAmelCase     :			Dict      =[f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
												dirs.sort(key=os.path.getctime)
												__lowerCAmelCase     :			Any      =dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract the step of the checkpoint to continue from there
						__lowerCAmelCase     :			Dict      =os.path.splitext(path)[0]
						__lowerCAmelCase     :			Dict      =int(training_difference.replace('step_', ''))

# Train model
model.train()
__lowerCAmelCase     :			int      =0
__lowerCAmelCase     :			int      =time.time()
__lowerCAmelCase     :			Dict      =0
for step, batch in enumerate(train_dataloader, start=1):
						if args.resume_from_checkpoint and step < resume_step:
												continue  # we need to skip steps until we reach the resumed step
						__lowerCAmelCase     :			str      =model(batch, labels=batch, use_cache=False).loss
						__lowerCAmelCase     :			List[str]      =accelerator.gather(loss.repeat(args.train_batch_size)).mean()
						loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
						log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
						__lowerCAmelCase     :			Any      =loss / args.gradient_accumulation_steps
						if step % args.gradient_accumulation_steps != 0:
												# Prevent backward from doing gradient all_reduce in every step
												if accelerator.distributed_type == DistributedType.MULTI_GPU:
																		with model.no_sync():
																								accelerator.backward(loss)
												else:
																		accelerator.backward(loss)
						else:
												__lowerCAmelCase     :			Union[str, Any]      =get_lr()
												accelerator.backward(loss)
												accelerator.clip_grad_norm_(model.parameters(), 1.0)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
												__lowerCAmelCase     :			Union[str, Any]      =time.time() - t_start
												__lowerCAmelCase     :			Any      =compute_tflops(elapsed_time, accelerator, args)
												log_metrics(
												    step,
												    {
												        'steps': completed_steps,
												        'loss/train': loss_tracking,
												        'lr': lr,
												        'tflops': tflops,
												        'time_per_iteration': elapsed_time,
												    },
												)
												__lowerCAmelCase     :			str      =time.time()
												__lowerCAmelCase     :			Optional[Any]      =0
												completed_steps += 1
						if step % args.save_checkpoint_steps == 0:
												logger.info('Evaluating and saving model checkpoint')
												__lowerCAmelCase    ,__lowerCAmelCase     :			List[str]      =evaluate(args)
												log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
												accelerator.wait_for_everyone()
												__lowerCAmelCase     :			List[Any]      =os.path.join(args.save_dir, f"""step_{step}""")
												accelerator.save_state(save_dir)
												if accelerator.is_main_process:
																		hf_repo.push_to_hub(commit_message=f"""step {step}""")
												model.train()
						if completed_steps >= args.max_train_steps:
												break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__lowerCAmelCase    ,__lowerCAmelCase     :			Tuple      =evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
__lowerCAmelCase     :			int      =accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowerCAmelCase     :			str      =os.path.join(args.save_dir, f"""step_{step}""")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message='final model')




import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


__lowerCAmelCase     :			Any      =logging.getLogger(__name__)
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :List[Any]=None      ,      lowerCAmelCase__      :Optional[Any]=None     )		->      List[Any]:
										if current_layer == 0:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.embedding_hidden_mapping_in(lowerCAmelCase__     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		hidden_states[0]

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		int(self.config.num_hidden_layers / self.config.num_hidden_groups     )

										# Index of the hidden group
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.albert_layer_groups[group_idx](
										    lowerCAmelCase__      ,      lowerCAmelCase__      ,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]      ,      )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		layer_group_output[0]

										return (hidden_states,)




@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''						,      A__						,      )
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :int      ,      lowerCAmelCase__      :List[str]     )		->      List[Any]:
										super().__init__(lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		AlbertTransformerWithPabee(lowerCAmelCase__     )

										self.init_weights()
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		0

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0


				def 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :Optional[int]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		threshold


				def 		__magic_name__(	self      :List[Any]      ,      lowerCAmelCase__      :Optional[int]     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		patience


				def 		__magic_name__(	self      :Union[str, Any]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Any		      =		0
										__SCREAMING_SNAKE_CASE						:					int		      =		0


				def 		__magic_name__(	self      :Optional[int]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.inference_layers_num / self.inference_instances_num
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(
										    f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
										    f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
										)
										print(lowerCAmelCase__     )



				@add_start_docstrings_to_model_forward(lowerCAmelCase__     )
				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :Optional[Any]=None      ,      lowerCAmelCase__      :Any=None      ,      lowerCAmelCase__      :Dict=None      ,      lowerCAmelCase__      :Optional[int]=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :str=False      ,      )		->      Union[str, Any]:

										if input_ids is not None and inputs_embeds is not None:
																raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''     )
										elif input_ids is not None:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		input_ids.size()
										elif inputs_embeds is not None:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.ones(lowerCAmelCase__      ,      device=lowerCAmelCase__     )
										if token_type_ids is None:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.zeros(lowerCAmelCase__      ,      dtype=torch.long      ,      device=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		attention_mask.unsqueeze(1     ).unsqueeze(2     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		extended_attention_mask.to(dtype=self.dtype     )  # fp16 compatibility
										__SCREAMING_SNAKE_CASE						:					Dict		      =		(1.0 - extended_attention_mask) * -1_0000.0
										__SCREAMING_SNAKE_CASE						:					str		      =		self.get_head_mask(lowerCAmelCase__      ,      self.config.num_hidden_layers     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.embeddings(
										    lowerCAmelCase__      ,      position_ids=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      inputs_embeds=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		embedding_output

										if self.training:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		[]
																for i in range(self.config.num_hidden_layers     ):
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.encoder.adaptive_forward(
																						    lowerCAmelCase__      ,      current_layer=lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      head_mask=lowerCAmelCase__      ,      )

																						__SCREAMING_SNAKE_CASE						:					Any		      =		self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
																						__SCREAMING_SNAKE_CASE						:					Tuple		      =		output_layers[i](output_dropout(lowerCAmelCase__     )     )
																						res.append(lowerCAmelCase__     )
										elif self.patience == 0:  # Use all layers for inference
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.encoder(lowerCAmelCase__      ,      lowerCAmelCase__      ,      head_mask=lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		[output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase__     )]
										else:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		0
																__SCREAMING_SNAKE_CASE						:					Any		      =		None
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		0
																for i in range(self.config.num_hidden_layers     ):
																						calculated_layer_num += 1
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.encoder.adaptive_forward(
																						    lowerCAmelCase__      ,      current_layer=lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      head_mask=lowerCAmelCase__      ,      )

																						__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
																						__SCREAMING_SNAKE_CASE						:					str		      =		output_layers[i](lowerCAmelCase__     )
																						if regression:
																												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		logits.detach()
																												if patient_result is not None:
																																		__SCREAMING_SNAKE_CASE						:					int		      =		patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels     ) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		__SCREAMING_SNAKE_CASE						:					List[str]		      =		0
																						else:
																												__SCREAMING_SNAKE_CASE						:					List[str]		      =		logits.detach().argmax(dim=1     )
																												if patient_result is not None:
																																		__SCREAMING_SNAKE_CASE						:					List[str]		      =		patient_result.detach().argmax(dim=1     )
																												if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase__     )     ):
																																		patient_counter += 1
																												else:
																																		__SCREAMING_SNAKE_CASE						:					str		      =		0

																						__SCREAMING_SNAKE_CASE						:					str		      =		logits
																						if patient_counter == self.patience:
																												break
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		[patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

										return res




@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''						,      A__						,      )
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :List[Any]      ,      lowerCAmelCase__      :int     )		->      Optional[int]:
										super().__init__(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		config.num_labels

										__SCREAMING_SNAKE_CASE						:					str		      =		AlbertModelWithPabee(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		nn.Dropout(config.classifier_dropout_prob     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		nn.ModuleList(
										    [nn.Linear(config.hidden_size      ,      self.config.num_labels     ) for _ in range(config.num_hidden_layers     )]     )

										self.init_weights()



				@add_start_docstrings_to_model_forward(lowerCAmelCase__     )
				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :Dict=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :Tuple=None      ,      lowerCAmelCase__      :int=None      ,      lowerCAmelCase__      :Any=None      ,      )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.albert(
										    input_ids=lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      position_ids=lowerCAmelCase__      ,      head_mask=lowerCAmelCase__      ,      inputs_embeds=lowerCAmelCase__      ,      output_dropout=self.dropout      ,      output_layers=self.classifiers      ,      regression=self.num_labels == 1      ,      )

										__SCREAMING_SNAKE_CASE						:					int		      =		(logits[-1],)

										if labels is not None:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		None
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0
																for ix, logits_item in enumerate(lowerCAmelCase__     ):
																						if self.num_labels == 1:
																												#  We are doing regression
																												__SCREAMING_SNAKE_CASE						:					str		      =		MSELoss()
																												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		loss_fct(logits_item.view(-1     )      ,      labels.view(-1     )     )
																						else:
																												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		CrossEntropyLoss()
																												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		loss_fct(logits_item.view(-1      ,      self.num_labels     )      ,      labels.view(-1     )     )
																						if total_loss is None:
																												__SCREAMING_SNAKE_CASE						:					Dict		      =		loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																__SCREAMING_SNAKE_CASE						:					Any		      =		(total_loss / total_weights,) + outputs

										return outputs
