






import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCAmelCase     :			Optional[Any]      =logging.getLogger(__name__)


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		p * torch.log(__A				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		0
						return -plogp.sum(dim=-1				)


def 	_UpperCamelCase							(				lowercase__				):
						logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A				)				)				)				)
						for row in range(len(__A				)				):
												if tensor.dtype != torch.long:
																		logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data				)				)
												else:
																		logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data				)				)


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__=True   ,	lowercase__=True   ,	lowercase__=None   ,	lowercase__=False				):
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model.config.num_hidden_layers, model.config.num_attention_heads
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.zeros(__A   ,	__A				).to(args.device				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.zeros(__A   ,	__A				).to(args.device				)

						if head_mask is None:
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.ones(__A   ,	__A				).to(args.device				)

						head_mask.requires_grad_(requires_grad=__A				)
						# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
						if actually_pruned:
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		None

						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		None
						__SCREAMING_SNAKE_CASE						:					Dict		      =		None
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		0.0

						for step, inputs in enumerate(tqdm(__A   ,	desc='''Iteration'''   ,	disable=args.local_rank not in [-1, 0]				)				):
												for k, v in inputs.items():
																		__SCREAMING_SNAKE_CASE						:					int		      =		v.to(args.device				)

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model(**__A   ,	head_mask=__A				)
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Dict		      =		(
												    outputs[0],
												    outputs[1],
												    outputs[-1],
												)  # Loss and logits are the first, attention the last
												loss.backward()  # Backpropagate to populate the gradients in the head mask

												if compute_entropy:
																		for layer, attn in enumerate(__A				):
																								__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		entropy(attn.detach()				) * inputs['''attention_mask'''].float().unsqueeze(1				)
																								attn_entropy[layer] += masked_entropy.sum(-1				).sum(0				).detach()

												if compute_importance:
																		head_importance += head_mask.grad.abs().detach()

												# Also store our logits/labels if we want to compute metrics afterwards
												if preds is None:
																		__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		logits.detach().cpu().numpy()
																		__SCREAMING_SNAKE_CASE						:					str		      =		inputs['''labels'''].detach().cpu().numpy()
												else:
																		__SCREAMING_SNAKE_CASE						:					Any		      =		np.append(__A   ,	logits.detach().cpu().numpy()   ,	axis=0				)
																		__SCREAMING_SNAKE_CASE						:					Any		      =		np.append(__A   ,	inputs['''labels'''].detach().cpu().numpy()   ,	axis=0				)

												tot_tokens += inputs["attention_mask"].float().detach().sum().data

						# Normalize
						attn_entropy /= tot_tokens
						head_importance /= tot_tokens
						# Layerwise importance normalization
						if not args.dont_normalize_importance_by_layer:
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		2
												__SCREAMING_SNAKE_CASE						:					Any		      =		torch.pow(torch.pow(__A   ,	__A				).sum(-1				)   ,	1 / exponent				)
												head_importance /= norm_by_layer.unsqueeze(-1				) + 1e-20

						if not args.dont_normalize_global_importance:
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

						# Print/save matrices
						np.save(os.path.join(args.output_dir   ,	'''attn_entropy.npy'''				)   ,	attn_entropy.detach().cpu().numpy()				)
						np.save(os.path.join(args.output_dir   ,	'''head_importance.npy'''				)   ,	head_importance.detach().cpu().numpy()				)

						logger.info('''Attention entropies'''				)
						print_ad_tensor(__A				)
						logger.info('''Head importance scores'''				)
						print_ad_tensor(__A				)
						logger.info('''Head ranked by importance scores'''				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.zeros(head_importance.numel()   ,	dtype=torch.long   ,	device=args.device				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.arange(
						    head_importance.numel()   ,	device=args.device				)
						__SCREAMING_SNAKE_CASE						:					int		      =		head_ranks.view_as(__A				)
						print_ad_tensor(__A				)

						return attn_entropy, head_importance, preds, labels


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[str]		      =		compute_heads_importance(__A   ,	__A   ,	__A   ,	compute_entropy=__A				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.argmax(__A   ,	axis=1				) if args.output_mode == '''classification''' else np.squeeze(__A				)
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		glue_compute_metrics(args.task_name   ,	__A   ,	__A				)[args.metric_name]
						logger.info('''Pruning: original score: %f, threshold: %f'''   ,	__A   ,	original_score * args.masking_threshold				)

						__SCREAMING_SNAKE_CASE						:					str		      =		torch.ones_like(__A				)
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		max(1   ,	int(new_head_mask.numel() * args.masking_amount				)				)

						__SCREAMING_SNAKE_CASE						:					str		      =		original_score
						while current_score >= original_score * args.masking_threshold:
												__SCREAMING_SNAKE_CASE						:					Tuple		      =		new_head_mask.clone()  # save current head mask
												# heads from least important to most - keep only not-masked heads
												__SCREAMING_SNAKE_CASE						:					str		      =		float('''Inf'''				)
												__SCREAMING_SNAKE_CASE						:					int		      =		head_importance.view(-1				).sort()[1]

												if len(__A				) <= num_to_mask:
																		break

												# mask heads
												__SCREAMING_SNAKE_CASE						:					str		      =		current_heads_to_mask[:num_to_mask]
												logger.info('''Heads to mask: %s'''   ,	str(current_heads_to_mask.tolist()				)				)
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		new_head_mask.view(-1				)
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0.0
												__SCREAMING_SNAKE_CASE						:					Dict		      =		new_head_mask.view_as(__A				)
												__SCREAMING_SNAKE_CASE						:					Dict		      =		new_head_mask.clone().detach()
												print_ad_tensor(__A				)

												# Compute metric and head importance again
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Dict		      =		compute_heads_importance(
												    __A   ,	__A   ,	__A   ,	compute_entropy=__A   ,	head_mask=__A				)
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		np.argmax(__A   ,	axis=1				) if args.output_mode == '''classification''' else np.squeeze(__A				)
												__SCREAMING_SNAKE_CASE						:					Dict		      =		glue_compute_metrics(args.task_name   ,	__A   ,	__A				)[args.metric_name]
												logger.info(
												    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''   ,	__A   ,	new_head_mask.sum()   ,	new_head_mask.sum() / new_head_mask.numel() * 100   ,	)

						logger.info('''Final head mask'''				)
						print_ad_tensor(__A				)
						np.save(os.path.join(args.output_dir   ,	'''head_mask.npy'''				)   ,	head_mask.detach().cpu().numpy()				)

						return head_mask


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		datetime.now()
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					int		      =		compute_heads_importance(
						    __A   ,	__A   ,	__A   ,	compute_entropy=__A   ,	compute_importance=__A   ,	head_mask=__A				)
						__SCREAMING_SNAKE_CASE						:					Dict		      =		np.argmax(__A   ,	axis=1				) if args.output_mode == '''classification''' else np.squeeze(__A				)
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		glue_compute_metrics(args.task_name   ,	__A   ,	__A				)[args.metric_name]
						__SCREAMING_SNAKE_CASE						:					Dict		      =		datetime.now() - before_time

						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sum(p.numel() for p in model.parameters()				)
						__SCREAMING_SNAKE_CASE						:					str		      =		{
						    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A				)				)
						}

						assert sum(len(__A				) for h in heads_to_prune.values()				) == (1 - head_mask.long()).sum().item()
						model.prune_heads(__A				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		sum(p.numel() for p in model.parameters()				)

						__SCREAMING_SNAKE_CASE						:					str		      =		datetime.now()
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[int]		      =		compute_heads_importance(
						    __A   ,	__A   ,	__A   ,	compute_entropy=__A   ,	compute_importance=__A   ,	head_mask=__A   ,	actually_pruned=__A   ,	)
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		np.argmax(__A   ,	axis=1				) if args.output_mode == '''classification''' else np.squeeze(__A				)
						__SCREAMING_SNAKE_CASE						:					Any		      =		glue_compute_metrics(args.task_name   ,	__A   ,	__A				)[args.metric_name]
						__SCREAMING_SNAKE_CASE						:					Any		      =		datetime.now() - before_time

						logger.info(
						    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''   ,	__A   ,	__A   ,	pruned_num_params / original_num_params * 100   ,	)
						logger.info('''Pruning: score with masking: %f score with pruning: %f'''   ,	__A   ,	__A				)
						logger.info('''Pruning: speed ratio (new timing / original timing): %f percents'''   ,	original_time / new_time * 100				)


def 	_UpperCamelCase							(				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--data_dir'''   ,	default=__A   ,	type=__A   ,	required=__A   ,	help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''   ,	)
						parser.add_argument(
						    '''--model_name_or_path'''   ,	default=__A   ,	type=__A   ,	required=__A   ,	help='''Path to pretrained model or model identifier from huggingface.co/models'''   ,	)
						parser.add_argument(
						    '''--task_name'''   ,	default=__A   ,	type=__A   ,	required=__A   ,	help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys()				)   ,	)
						parser.add_argument(
						    '''--output_dir'''   ,	default=__A   ,	type=__A   ,	required=__A   ,	help='''The output directory where the model predictions and checkpoints will be written.'''   ,	)

						# Other parameters
						parser.add_argument(
						    '''--config_name'''   ,	default=''''''   ,	type=__A   ,	help='''Pretrained config name or path if not the same as model_name_or_path'''   ,	)
						parser.add_argument(
						    '''--tokenizer_name'''   ,	default=''''''   ,	type=__A   ,	help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''   ,	)
						parser.add_argument(
						    '''--cache_dir'''   ,	default=__A   ,	type=__A   ,	help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''   ,	)
						parser.add_argument(
						    '''--data_subset'''   ,	type=__A   ,	default=-1   ,	help='''If > 0: limit the data to a subset of data_subset instances.'''				)
						parser.add_argument(
						    '''--overwrite_output_dir'''   ,	action='''store_true'''   ,	help='''Whether to overwrite data in output directory'''				)
						parser.add_argument(
						    '''--overwrite_cache'''   ,	action='''store_true'''   ,	help='''Overwrite the cached training and evaluation sets'''				)

						parser.add_argument(
						    '''--dont_normalize_importance_by_layer'''   ,	action='''store_true'''   ,	help='''Don\'t normalize importance score by layers'''				)
						parser.add_argument(
						    '''--dont_normalize_global_importance'''   ,	action='''store_true'''   ,	help='''Don\'t normalize all importance scores between 0 and 1'''   ,	)

						parser.add_argument(
						    '''--try_masking'''   ,	action='''store_true'''   ,	help='''Whether to try to mask head until a threshold of accuracy.'''				)
						parser.add_argument(
						    '''--masking_threshold'''   ,	default=0.9   ,	type=__A   ,	help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''   ,	)
						parser.add_argument(
						    '''--masking_amount'''   ,	default=0.1   ,	type=__A   ,	help='''Amount to heads to masking at each masking step.'''				)
						parser.add_argument('''--metric_name'''   ,	default='''acc'''   ,	type=__A   ,	help='''Metric to use for head masking.'''				)

						parser.add_argument(
						    '''--max_seq_length'''   ,	default=128   ,	type=__A   ,	help=(
						        '''The maximum total input sequence length after WordPiece tokenization. \n'''
						        '''Sequences longer than this will be truncated, sequences shorter padded.'''
						    )   ,	)
						parser.add_argument('''--batch_size'''   ,	default=1   ,	type=__A   ,	help='''Batch size.'''				)

						parser.add_argument('''--seed'''   ,	type=__A   ,	default=42				)
						parser.add_argument('''--local_rank'''   ,	type=__A   ,	default=-1   ,	help='''local_rank for distributed training on gpus'''				)
						parser.add_argument('''--no_cuda'''   ,	action='''store_true'''   ,	help='''Whether not to use CUDA when available'''				)
						parser.add_argument('''--server_ip'''   ,	type=__A   ,	default=''''''   ,	help='''Can be used for distant debugging.'''				)
						parser.add_argument('''--server_port'''   ,	type=__A   ,	default=''''''   ,	help='''Can be used for distant debugging.'''				)
						__SCREAMING_SNAKE_CASE						:					Dict		      =		parser.parse_args()

						if args.server_ip and args.server_port:
												# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
												import ptvsd

												print('''Waiting for debugger attach'''				)
												ptvsd.enable_attach(address=(args.server_ip, args.server_port)   ,	redirect_output=__A				)
												ptvsd.wait_for_attach()

						# Setup devices and distributed training
						if args.local_rank == -1 or args.no_cuda:
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''				)
												__SCREAMING_SNAKE_CASE						:					Any		      =		0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank				)
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.device('''cuda'''   ,	args.local_rank				)
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		1
												torch.distributed.init_process_group(backend='''nccl'''				)  # Initializes the distributed backend

						# Setup logging
						logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN				)
						logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device   ,	args.n_gpu   ,	bool(args.local_rank != -1				)				)				)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(args.local_rank				):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()

						# Set seeds
						set_seed(args.seed				)

						# Prepare GLUE task
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		args.task_name.lower()
						if args.task_name not in glue_processors:
												raise ValueError('''Task not found: %s''' % (args.task_name)				)
						__SCREAMING_SNAKE_CASE						:					str		      =		glue_processors[args.task_name]()
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		glue_output_modes[args.task_name]
						__SCREAMING_SNAKE_CASE						:					str		      =		processor.get_labels()
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		len(__A				)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		AutoConfig.from_pretrained(
						    args.config_name if args.config_name else args.model_name_or_path   ,	num_labels=__A   ,	finetuning_task=args.task_name   ,	output_attentions=__A   ,	cache_dir=args.cache_dir   ,	)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		AutoTokenizer.from_pretrained(
						    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path   ,	cache_dir=args.cache_dir   ,	)
						__SCREAMING_SNAKE_CASE						:					Dict		      =		AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path   ,	from_tf=bool('''.ckpt''' in args.model_name_or_path				)   ,	config=__A   ,	cache_dir=args.cache_dir   ,	)

						# Distributed and parallel training
						model.to(args.device				)
						if args.local_rank != -1:
												__SCREAMING_SNAKE_CASE						:					str		      =		nn.parallel.DistributedDataParallel(
												    __A   ,	device_ids=[args.local_rank]   ,	output_device=args.local_rank   ,	find_unused_parameters=__A				)
						elif args.n_gpu > 1:
												__SCREAMING_SNAKE_CASE						:					Any		      =		nn.DataParallel(__A				)

						# Print/save training arguments
						os.makedirs(args.output_dir   ,	exist_ok=__A				)
						torch.save(__A   ,	os.path.join(args.output_dir   ,	'''run_args.bin'''				)				)
						logger.info('''Training/evaluation parameters %s'''   ,	__A				)

						# Prepare dataset for the GLUE task
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		GlueDataset(__A   ,	tokenizer=__A   ,	mode='''dev'''				)
						if args.data_subset > 0:
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		Subset(__A   ,	list(range(min(args.data_subset   ,	len(__A				)				)				)				)				)
						__SCREAMING_SNAKE_CASE						:					str		      =		SequentialSampler(__A				) if args.local_rank == -1 else DistributedSampler(__A				)
						__SCREAMING_SNAKE_CASE						:					int		      =		DataLoader(
						    __A   ,	sampler=__A   ,	batch_size=args.batch_size   ,	collate_fn=__A				)

						# Compute head entropy and importance score
						compute_heads_importance(__A   ,	__A   ,	__A				)

						# Try head masking (set heads to zero until the score goes under a threshole)
						# and head pruning (remove masked heads and see the effect on the network)
						if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
												__SCREAMING_SNAKE_CASE						:					int		      =		mask_heads(__A   ,	__A   ,	__A				)
												prune_heads(__A   ,	__A   ,	__A   ,	__A				)


if __name__ == "__main__":
						main()




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
