





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')

lowerCAmelCase_    =    logging.getLogger(__name__)



@dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=128,					metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    },					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
			            '''value if set.'''
			        )
			    },					)




@dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					str	      =				field(
			    default=_a,					metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
			lowerCamelCase_					:					str	      =				field(
			    default=_a,					metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}					)
			lowerCamelCase_					:					Optional[str]	      =				field(
			    default=_a,					metadata={'''help''': '''Train language if it is different from the evaluation language.'''}					)
			lowerCamelCase_					:					Optional[str]	      =				field(
			    default=_a,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
			lowerCamelCase_					:					Optional[str]	      =				field(
			    default=_a,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
			lowerCamelCase_					:					Optional[str]	      =				field(
			    default=_a,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},					)
			lowerCamelCase_					:					Optional[bool]	      =				field(
			    default=_a,					metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''},					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''},					)
			lowerCamelCase_					:					str	      =				field(
			    default='''main''',					metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''},					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    },					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''},					)






def 					lowerCamelCase_						(    )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
				snake_case_      ,							snake_case_      ,							snake_case_						:   Tuple           =  parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('''run_xnli'''			,    _UpperCamelCase			)

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,    datefmt='''%m/%d/%Y %H:%M:%S'''			,    handlers=[logging.StreamHandler(sys.stdout			)]			,    )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				snake_case_						:   List[Any]           =  training_args.get_process_log_level()
				logger.setLevel(_UpperCamelCase			)
				datasets.utils.logging.set_verbosity(_UpperCamelCase			)
				transformers.utils.logging.set_verbosity(_UpperCamelCase			)
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + f'''distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'''			)
				logger.info(f'''Training/evaluation parameters {training_args}'''			)

				# Detecting last checkpoint.
				snake_case_						:   str           =  None
				if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
								snake_case_						:   Optional[Any]           =  get_last_checkpoint(training_args.output_dir			)
								if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    '''Use --overwrite_output_dir to overcome.'''			)
								elif last_checkpoint is not None:
												logger.info(
												    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''			)

    # Set seed before initializing model.
				set_seed(training_args.seed			)

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												snake_case_						:   Union[str, Any]           =  load_dataset(
												    '''xnli'''			,    model_args.language			,    split='''train'''			,    cache_dir=model_args.cache_dir			,    use_auth_token=True if model_args.use_auth_token else None			,    )
								else:
												snake_case_						:   str           =  load_dataset(
												    '''xnli'''			,    model_args.train_language			,    split='''train'''			,    cache_dir=model_args.cache_dir			,    use_auth_token=True if model_args.use_auth_token else None			,    )
								snake_case_						:   Optional[int]           =  train_dataset.features['''label'''].names

				if training_args.do_eval:
								snake_case_						:   Dict           =  load_dataset(
								    '''xnli'''			,    model_args.language			,    split='''validation'''			,    cache_dir=model_args.cache_dir			,    use_auth_token=True if model_args.use_auth_token else None			,    )
								snake_case_						:   Tuple           =  eval_dataset.features['''label'''].names

				if training_args.do_predict:
								snake_case_						:   int           =  load_dataset(
								    '''xnli'''			,    model_args.language			,    split='''test'''			,    cache_dir=model_args.cache_dir			,    use_auth_token=True if model_args.use_auth_token else None			,    )
								snake_case_						:   Optional[int]           =  predict_dataset.features['''label'''].names

				# Labels
				snake_case_						:   int           =  len(_UpperCamelCase			)

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				snake_case_						:   Any           =  AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,    num_labels=_UpperCamelCase			,    idalabel={str(_UpperCamelCase			): label for i, label in enumerate(_UpperCamelCase			)}			,    labelaid={label: i for i, label in enumerate(_UpperCamelCase			)}			,    finetuning_task='''xnli'''			,    cache_dir=model_args.cache_dir			,    revision=model_args.model_revision			,    use_auth_token=True if model_args.use_auth_token else None			,    )
				snake_case_						:   Dict           =  AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,    do_lower_case=model_args.do_lower_case			,    cache_dir=model_args.cache_dir			,    use_fast=model_args.use_fast_tokenizer			,    revision=model_args.model_revision			,    use_auth_token=True if model_args.use_auth_token else None			,    )
				snake_case_						:   Union[str, Any]           =  AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path			,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)			,    config=_UpperCamelCase			,    cache_dir=model_args.cache_dir			,    revision=model_args.model_revision			,    use_auth_token=True if model_args.use_auth_token else None			,    ignore_mismatched_sizes=model_args.ignore_mismatched_sizes			,    )

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								snake_case_						:   Dict           =  '''max_length'''
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								snake_case_						:   str           =  False

				def preprocess_function(_UpperCamelCase			):
								# Tokenize the texts
								return tokenizer(
								    examples['''premise''']			,    examples['''hypothesis''']			,    padding=_UpperCamelCase			,    max_length=data_args.max_seq_length			,    truncation=_UpperCamelCase			,    )

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												snake_case_						:   List[Any]           =  min(len(_UpperCamelCase			)			,    data_args.max_train_samples			)
												snake_case_						:   int           =  train_dataset.select(range(_UpperCamelCase			)			)
								with training_args.main_process_first(desc='''train dataset map pre-processing'''			):
												snake_case_						:   Optional[int]           =  train_dataset.map(
												    _UpperCamelCase			,    batched=_UpperCamelCase			,    load_from_cache_file=not data_args.overwrite_cache			,    desc='''Running tokenizer on train dataset'''			,    )
								# Log a few random samples from the training set:
								for index in random.sample(range(len(_UpperCamelCase			)			)			,    3			):
												logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''			)

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												snake_case_						:   List[str]           =  min(len(_UpperCamelCase			)			,    data_args.max_eval_samples			)
												snake_case_						:   List[str]           =  eval_dataset.select(range(_UpperCamelCase			)			)
								with training_args.main_process_first(desc='''validation dataset map pre-processing'''			):
												snake_case_						:   List[str]           =  eval_dataset.map(
												    _UpperCamelCase			,    batched=_UpperCamelCase			,    load_from_cache_file=not data_args.overwrite_cache			,    desc='''Running tokenizer on validation dataset'''			,    )

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												snake_case_						:   Union[str, Any]           =  min(len(_UpperCamelCase			)			,    data_args.max_predict_samples			)
												snake_case_						:   Dict           =  predict_dataset.select(range(_UpperCamelCase			)			)
								with training_args.main_process_first(desc='''prediction dataset map pre-processing'''			):
												snake_case_						:   List[str]           =  predict_dataset.map(
												    _UpperCamelCase			,    batched=_UpperCamelCase			,    load_from_cache_file=not data_args.overwrite_cache			,    desc='''Running tokenizer on prediction dataset'''			,    )

    # Get the metric function
				snake_case_						:   int           =  evaluate.load('''xnli'''			)

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(_UpperCamelCase			):
								snake_case_						:   List[str]           =  p.predictions[0] if isinstance(p.predictions			,    _UpperCamelCase			) else p.predictions
								snake_case_						:   Tuple           =  np.argmax(_UpperCamelCase			,    axis=1			)
								return metric.compute(predictions=_UpperCamelCase			,    references=p.label_ids			)

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								snake_case_						:   Optional[int]           =  default_data_collator
				elif training_args.fpaa:
								snake_case_						:   Any           =  DataCollatorWithPadding(_UpperCamelCase			,    pad_to_multiple_of=8			)
				else:
								snake_case_						:   Any           =  None

				# Initialize our Trainer
				snake_case_						:   Any           =  Trainer(
				    model=_UpperCamelCase			,    args=_UpperCamelCase			,    train_dataset=train_dataset if training_args.do_train else None			,    eval_dataset=eval_dataset if training_args.do_eval else None			,    compute_metrics=_UpperCamelCase			,    tokenizer=_UpperCamelCase			,    data_collator=_UpperCamelCase			,    )

				# Training
				if training_args.do_train:
								snake_case_						:   int           =  None
								if training_args.resume_from_checkpoint is not None:
												snake_case_						:   Dict           =  training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												snake_case_						:   Dict           =  last_checkpoint
								snake_case_						:   int           =  trainer.train(resume_from_checkpoint=_UpperCamelCase			)
								snake_case_						:   Union[str, Any]           =  train_result.metrics
								snake_case_						:   Union[str, Any]           =  (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCamelCase			)
								)
								snake_case_						:   Dict           =  min(_UpperCamelCase			,    len(_UpperCamelCase			)			)

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics('''train'''			,    _UpperCamelCase			)
								trainer.save_metrics('''train'''			,    _UpperCamelCase			)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''			)
								snake_case_						:   Any           =  trainer.evaluate(eval_dataset=_UpperCamelCase			)

								snake_case_						:   int           =  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCamelCase			)
								snake_case_						:   str           =  min(_UpperCamelCase			,    len(_UpperCamelCase			)			)

								trainer.log_metrics('''eval'''			,    _UpperCamelCase			)
								trainer.save_metrics('''eval'''			,    _UpperCamelCase			)

				# Prediction
				if training_args.do_predict:
								logger.info('''*** Predict ***'''			)
								snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[int]           =  trainer.predict(_UpperCamelCase			,    metric_key_prefix='''predict'''			)

								snake_case_						:   Union[str, Any]           =  (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCamelCase			)
								)
								snake_case_						:   Optional[int]           =  min(_UpperCamelCase			,    len(_UpperCamelCase			)			)

								trainer.log_metrics('''predict'''			,    _UpperCamelCase			)
								trainer.save_metrics('''predict'''			,    _UpperCamelCase			)

								snake_case_						:   List[Any]           =  np.argmax(_UpperCamelCase			,    axis=1			)
								snake_case_						:   Optional[Any]           =  os.path.join(training_args.output_dir			,    '''predictions.txt'''			)
								if trainer.is_world_process_zero():
												with open(_UpperCamelCase			,    '''w'''			) as writer:
																writer.write('''index\tprediction\n'''			)
																for index, item in enumerate(_UpperCamelCase			):
																				snake_case_						:   List[str]           =  label_list[item]
																				writer.write(f'''{index}\t{item}\n'''			)


if __name__ == "__main__":
	main()







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, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


lowerCAmelCase_    =    logging.getLogger(__name__)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if os.path.exists(_UpperCamelCase			):
								if os.path.exists(os.path.join(_UpperCamelCase			,    '''config.json'''			)			) and os.path.isfile(
								    os.path.join(_UpperCamelCase			,    '''config.json'''			)			):
												os.remove(os.path.join(_UpperCamelCase			,    '''config.json'''			)			)
								if os.path.exists(os.path.join(_UpperCamelCase			,    '''pytorch_model.bin'''			)			) and os.path.isfile(
								    os.path.join(_UpperCamelCase			,    '''pytorch_model.bin'''			)			):
												os.remove(os.path.join(_UpperCamelCase			,    '''pytorch_model.bin'''			)			)
				else:
								os.makedirs(_UpperCamelCase			)
				model.save_pretrained(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  2
				if unlogit:
								snake_case_						:   Any           =  torch.pow(_UpperCamelCase			,    _UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  p * torch.log(_UpperCamelCase			)
				snake_case_						:   Dict           =  0
				return -plogp.sum(dim=-1			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				logger.info('''lv, h >\t''' + '''\t'''.join(f'''{x + 1}''' for x in range(len(_UpperCamelCase			)			)			)			)
				for row in range(len(_UpperCamelCase			)			):
								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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=True			,    _UpperCamelCase=True			,    _UpperCamelCase=None			,    _UpperCamelCase=False			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_      ,							snake_case_						:   int           =  model.config.num_hidden_layers, model.config.num_attention_heads
				snake_case_						:   int           =  torch.zeros(_UpperCamelCase			,    _UpperCamelCase			).to(args.device			)
				snake_case_						:   Optional[int]           =  torch.zeros(_UpperCamelCase			,    _UpperCamelCase			).to(args.device			)

				if head_mask is None:
								snake_case_						:   Tuple           =  torch.ones(_UpperCamelCase			,    _UpperCamelCase			).to(args.device			)

				head_mask.requires_grad_(requires_grad=_UpperCamelCase			)
				# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
				if actually_pruned:
								snake_case_						:   Dict           =  None

				snake_case_						:   Tuple           =  0.0
				snake_case_						:   Dict           =  0.0
				for step, inputs in enumerate(tqdm(_UpperCamelCase			,    desc='''Iteration'''			,    disable=args.local_rank not in [-1, 0]			)			):
								snake_case_						:   Any           =  tuple(t.to(args.device			) for t in inputs			)
								((snake_case_)      ,							)						:   Union[str, Any]           =  inputs

								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
								snake_case_						:   List[str]           =  model(_UpperCamelCase			,    labels=_UpperCamelCase			,    head_mask=_UpperCamelCase			)
								#  (loss), lm_logits, presents, (all hidden_states), (attentions)
								snake_case_      ,							snake_case_      ,							snake_case_						:   int           =  (
								    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
								total_loss += loss.detach().cpu().numpy()
								if compute_entropy:
												for layer, attn in enumerate(_UpperCamelCase			):
																snake_case_						:   Dict           =  entropy(attn.detach()			,    _UpperCamelCase			)
																attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).sum(0			).detach()

								if compute_importance:
												head_importance += head_mask.grad.abs().detach()
								tot_tokens += torch.ones_like(_UpperCamelCase			).float().detach().sum().data

				# Normalize
				attn_entropy /= tot_tokens
				head_importance /= tot_tokens
				# Layerwise importance normalization
				if not args.dont_normalize_importance_by_layer:
								snake_case_						:   Union[str, Any]           =  2
								snake_case_						:   Any           =  torch.pow(torch.pow(_UpperCamelCase			,    _UpperCamelCase			).sum(-1			)			,    1 / exponent			)
								head_importance /= norm_by_layer.unsqueeze(-1			) + 1E-20

				if not args.dont_normalize_global_importance:
								snake_case_						:   Union[str, Any]           =  (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

				# Print matrices
				if compute_entropy:
								logger.info('''Attention entropies'''			)
								print_ad_tensor(_UpperCamelCase			)
				if compute_importance:
								logger.info('''Head importance scores'''			)
								print_ad_tensor(_UpperCamelCase			)
				logger.info('''Head ranked by importance scores'''			)
				snake_case_						:   Optional[int]           =  torch.zeros(head_importance.numel()			,    dtype=torch.long			,    device=args.device			)
				snake_case_						:   Union[str, Any]           =  torch.arange(
				    head_importance.numel()			,    device=args.device			)
				snake_case_						:   Dict           =  head_ranks.view_as(_UpperCamelCase			)
				print_ad_tensor(_UpperCamelCase			)
				return attn_entropy, head_importance, total_loss






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[int]           =  compute_heads_importance(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    compute_entropy=_UpperCamelCase			)
				snake_case_						:   Any           =  1 / loss  # instead of downsteam score use the LM loss
				logger.info('''Pruning: original score: %f, threshold: %f'''			,    _UpperCamelCase			,    original_score * args.masking_threshold			)

				snake_case_						:   Any           =  torch.ones_like(_UpperCamelCase			)
				snake_case_						:   Union[str, Any]           =  max(1			,    int(new_head_mask.numel() * args.masking_amount			)			)

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

								if len(_UpperCamelCase			) <= num_to_mask:
												print('''BREAK BY num_to_mask'''			)
												break

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

								# Compute metric and head importance again
								snake_case_      ,							snake_case_      ,							snake_case_						:   str           =  compute_heads_importance(
								    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    compute_entropy=_UpperCamelCase			,    head_mask=_UpperCamelCase			)
								snake_case_						:   Tuple           =  1 / loss
								logger.info(
								    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''			,    _UpperCamelCase			,    new_head_mask.sum()			,    new_head_mask.sum() / new_head_mask.numel() * 100			,    )

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

				return head_mask






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				snake_case_						:   str           =  datetime.now()
				snake_case_      ,							snake_case_      ,							snake_case_						:   List[Any]           =  compute_heads_importance(
				    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    compute_entropy=_UpperCamelCase			,    compute_importance=_UpperCamelCase			,    head_mask=_UpperCamelCase			)
				snake_case_						:   Union[str, Any]           =  1 / loss
				snake_case_						:   Union[str, Any]           =  datetime.now() - before_time

				snake_case_						:   int           =  sum(p.numel() for p in model.parameters()			)
				snake_case_						:   Tuple           =  {
				    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_UpperCamelCase			)			)
				}

				for k, v in heads_to_prune.items():
								if isinstance(_UpperCamelCase			,    _UpperCamelCase			):
												snake_case_						:   Any           =  [
												    v,
												]

				assert sum(len(_UpperCamelCase			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
				model.prune_heads(_UpperCamelCase			)
				snake_case_						:   Union[str, Any]           =  sum(p.numel() for p in model.parameters()			)

				snake_case_						:   Dict           =  datetime.now()
				snake_case_      ,							snake_case_      ,							snake_case_						:   Union[str, Any]           =  compute_heads_importance(
				    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    compute_entropy=_UpperCamelCase			,    compute_importance=_UpperCamelCase			,    head_mask=_UpperCamelCase			,    actually_pruned=_UpperCamelCase			,    )

				snake_case_						:   Union[str, Any]           =  1 / loss
				snake_case_						:   Optional[Any]           =  datetime.now() - before_time

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






def 					lowerCamelCase_						(    )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   Tuple           =  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--data_dir'''			,    default=_UpperCamelCase			,    type=_UpperCamelCase			,    required=_UpperCamelCase			,    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=_UpperCamelCase			,    type=_UpperCamelCase			,    required=_UpperCamelCase			,    help='''Path to pretrained model or model identifier from huggingface.co/models'''			,    )
				parser.add_argument(
				    '''--output_dir'''			,    default=_UpperCamelCase			,    type=_UpperCamelCase			,    required=_UpperCamelCase			,    help='''The output directory where the model predictions and checkpoints will be written.'''			,    )

				# Other parameters
				parser.add_argument(
				    '''--config_name'''			,    default=''''''			,    type=_UpperCamelCase			,    help='''Pretrained config name or path if not the same as model_name_or_path'''			,    )
				parser.add_argument(
				    '''--tokenizer_name'''			,    default=''''''			,    type=_UpperCamelCase			,    help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''			,    )
				parser.add_argument(
				    '''--cache_dir'''			,    default=_UpperCamelCase			,    type=_UpperCamelCase			,    help='''Where do you want to store the pre-trained models downloaded from s3'''			,    )
				parser.add_argument(
				    '''--data_subset'''			,    type=_UpperCamelCase			,    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=_UpperCamelCase			,    help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''			,    )
				parser.add_argument(
				    '''--masking_amount'''			,    default=0.1			,    type=_UpperCamelCase			,    help='''Amount to heads to masking at each masking step.'''			)
				parser.add_argument('''--metric_name'''			,    default='''acc'''			,    type=_UpperCamelCase			,    help='''Metric to use for head masking.'''			)

				parser.add_argument(
				    '''--max_seq_length'''			,    default=128			,    type=_UpperCamelCase			,    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=_UpperCamelCase			,    help='''Batch size.'''			)

				parser.add_argument('''--seed'''			,    type=_UpperCamelCase			,    default=42			)
				parser.add_argument('''--local_rank'''			,    type=_UpperCamelCase			,    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=_UpperCamelCase			,    default=''''''			,    help='''Can be used for distant debugging.'''			)
				parser.add_argument('''--server_port'''			,    type=_UpperCamelCase			,    default=''''''			,    help='''Can be used for distant debugging.'''			)
				snake_case_						:   Any           =  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=_UpperCamelCase			)
								ptvsd.wait_for_attach()

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

				snake_case_						:   int           =  GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

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

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

				# Prepare dataset
				snake_case_						:   str           =  np.concatenate(
				    [
				        np.loadtxt(args.data_dir			,    dtype=np.intaa			),
				    ]			)
				snake_case_						:   Any           =  (torch.from_numpy(_UpperCamelCase			),)
				snake_case_						:   Any           =  TensorDataset(*_UpperCamelCase			)
				snake_case_						:   List[str]           =  RandomSampler(_UpperCamelCase			)
				snake_case_						:   int           =  DataLoader(_UpperCamelCase			,    sampler=_UpperCamelCase			,    batch_size=args.batch_size			)

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

				# 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:
								snake_case_						:   List[str]           =  mask_heads(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								prune_heads(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)


if __name__ == "__main__":
	main()
