

"""simple docstring"""





import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__UpperCamelCase  :      int							      =		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	if "token" in model_name_or_path:
		return "rag_token"
	if "sequence" in model_name_or_path:
		return "rag_sequence"
	if "bart" in model_name_or_path:
		return "bart"
	return None

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	return max(metric_fn(A_						,     A_ ) for gt in ground_truths )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	[line.strip() for line in open(A_						,     '''r''' ).readlines()]
	lowerCAmelCase__	:						str  				=	[]

	if args.gold_data_mode == "qa":
		lowerCAmelCase__	:						Optional[int]  				=	pd.read_csv(A_						,     sep='''\t'''						,     header=A_ )
		for answer_list in data[1]:
			lowerCAmelCase__	:						Optional[Any]  				=	ast.literal_eval(A_ )
			answers.append(A_ )
	else:
		lowerCAmelCase__	:						Union[str, Any]  				=	[line.strip() for line in open(A_						,     '''r''' ).readlines()]
		lowerCAmelCase__	:						int  				=	[[reference] for reference in references]

	lowerCAmelCase__	:						Any  				=	0
	for prediction, ground_truths in zip(A_						,     A_ ):
		total += 1
		em += metric_max_over_ground_truths(A_						,     A_						,     A_ )
		fa += metric_max_over_ground_truths(A_						,     A_						,     A_ )

	lowerCAmelCase__	:						Any  				=	100.0 * em / total
	lowerCAmelCase__	:						List[Any]  				=	100.0 * fa / total

	logger.info(f'F1: {fa:.2f}' )
	logger.info(f'EM: {em:.2f}' )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	args.k
	lowerCAmelCase__	:						Union[str, Any]  				=	[line.strip() for line in open(A_						,     '''r''' ).readlines()]
	lowerCAmelCase__	:						Union[str, Any]  				=	[line.strip() for line in open(A_						,     '''r''' ).readlines()]

	lowerCAmelCase__	:						Optional[int]  				=	0
	for hypo, reference in zip(A_						,     A_ ):
		lowerCAmelCase__	:						List[str]  				=	set(hypo.split('''\t''' )[:k] )
		lowerCAmelCase__	:						int  				=	set(reference.split('''\t''' ) )
		total += 1
		em += len(hypo_provenance & ref_provenance ) / k

	lowerCAmelCase__	:						str  				=	100.0 * em / total
	logger.info(f'Precision@{k}: {em: .2f}' )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	def strip_title(A_ ):
		if title.startswith('''"''' ):
			lowerCAmelCase__	:						List[str]  				=	title[1:]
		if title.endswith('''"''' ):
			lowerCAmelCase__	:						Optional[Any]  				=	title[:-1]
		return title

	lowerCAmelCase__	:						str  				=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    A_						,     return_tensors='''pt'''						,     padding=A_						,     truncation=A_						,     )['''input_ids'''].to(args.device )

	lowerCAmelCase__	:						Optional[int]  				=	rag_model.rag.question_encoder(A_ )
	lowerCAmelCase__	:						str  				=	question_enc_outputs[0]

	lowerCAmelCase__	:						Any  				=	rag_model.retriever(
	    A_						,     question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy()						,     prefix=rag_model.rag.generator.config.prefix						,     n_docs=rag_model.config.n_docs						,     return_tensors='''pt'''						,     )
	lowerCAmelCase__	:						Optional[Any]  				=	rag_model.retriever.index.get_doc_dicts(result.doc_ids )
	lowerCAmelCase__	:						Tuple  				=	[]
	for docs in all_docs:
		lowerCAmelCase__	:						int  				=	[strip_title(A_ ) for title in docs['''title''']]
		provenance_strings.append('''\t'''.join(A_ ) )
	return provenance_strings

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	with torch.no_grad():
		lowerCAmelCase__	:						Tuple  				=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    A_						,     return_tensors='''pt'''						,     padding=A_						,     truncation=A_ )

		lowerCAmelCase__	:						Dict  				=	inputs_dict.input_ids.to(args.device )
		lowerCAmelCase__	:						Tuple  				=	inputs_dict.attention_mask.to(args.device )
		lowerCAmelCase__	:						List[str]  				=	rag_model.generate(  # rag_model overwrites generate
		    A_						,     attention_mask=A_						,     num_beams=args.num_beams						,     min_length=args.min_length						,     max_length=args.max_length						,     early_stopping=A_						,     num_return_sequences=1						,     bad_words_ids=[[0, 0]]						,     )
		lowerCAmelCase__	:						Dict  				=	rag_model.retriever.generator_tokenizer.batch_decode(A_						,     skip_special_tokens=A_ )

		if args.print_predictions:
			for q, a in zip(A_						,     A_ ):
				logger.info('''Q: {} - A: {}'''.format(A_						,     A_ ) )

		return answers

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	argparse.ArgumentParser()
	parser.add_argument(
	    '''--model_type'''						,     choices=['''rag_sequence''', '''rag_token''', '''bart''']						,     type=A_						,     help=(
	        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
	        ''' model_name_or_path'''
	    )						,     )
	parser.add_argument(
	    '''--index_name'''						,     default=A_						,     choices=['''exact''', '''compressed''', '''legacy''']						,     type=A_						,     help='''RAG model retriever type'''						,     )
	parser.add_argument(
	    '''--index_path'''						,     default=A_						,     type=A_						,     help='''Path to the retrieval index'''						,     )
	parser.add_argument('''--n_docs'''						,     default=5						,     type=A_						,     help='''Number of retrieved docs''' )
	parser.add_argument(
	    '''--model_name_or_path'''						,     default=A_						,     type=A_						,     required=A_						,     help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''						,     )
	parser.add_argument(
	    '''--eval_mode'''						,     choices=['''e2e''', '''retrieval''']						,     default='''e2e'''						,     type=A_						,     help=(
	        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
	        ''' precision@k.'''
	    )						,     )
	parser.add_argument('''--k'''						,     default=1						,     type=A_						,     help='''k for the precision@k calculation''' )
	parser.add_argument(
	    '''--evaluation_set'''						,     default=A_						,     type=A_						,     required=A_						,     help='''Path to a file containing evaluation samples'''						,     )
	parser.add_argument(
	    '''--gold_data_path'''						,     default=A_						,     type=A_						,     required=A_						,     help='''Path to a tab-separated file with gold samples'''						,     )
	parser.add_argument(
	    '''--gold_data_mode'''						,     default='''qa'''						,     type=A_						,     choices=['''qa''', '''ans''']						,     help=(
	        '''Format of the gold data file'''
	        '''qa - a single line in the following format: question [tab] answer_list'''
	        '''ans - a single line of the gold file contains the expected answer string'''
	    )						,     )
	parser.add_argument(
	    '''--predictions_path'''						,     type=A_						,     default='''predictions.txt'''						,     help='''Name of the predictions file, to be stored in the checkpoints directory'''						,     )
	parser.add_argument(
	    '''--eval_all_checkpoints'''						,     action='''store_true'''						,     help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''						,     )
	parser.add_argument(
	    '''--eval_batch_size'''						,     default=8						,     type=A_						,     help='''Batch size per GPU/CPU for evaluation.'''						,     )
	parser.add_argument(
	    '''--recalculate'''						,     help='''Recalculate predictions even if the prediction file exists'''						,     action='''store_true'''						,     )
	parser.add_argument(
	    '''--num_beams'''						,     default=4						,     type=A_						,     help='''Number of beams to be used when generating answers'''						,     )
	parser.add_argument('''--min_length'''						,     default=1						,     type=A_						,     help='''Min length of the generated answers''' )
	parser.add_argument('''--max_length'''						,     default=50						,     type=A_						,     help='''Max length of the generated answers''' )

	parser.add_argument(
	    '''--print_predictions'''						,     action='''store_true'''						,     help='''If True, prints predictions while evaluating.'''						,     )
	parser.add_argument(
	    '''--print_docs'''						,     action='''store_true'''						,     help='''If True, prints docs retried while generating.'''						,     )
	lowerCAmelCase__	:						List[str]  				=	parser.parse_args()
	lowerCAmelCase__	:						Tuple  				=	torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
	return args

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						int  				=	{}
	if args.model_type is None:
		lowerCAmelCase__	:						Optional[int]  				=	infer_model_type(args.model_name_or_path )
		assert args.model_type is not None
	if args.model_type.startswith('''rag''' ):
		lowerCAmelCase__	:						Dict  				=	RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
		lowerCAmelCase__	:						int  				=	args.n_docs
		if args.index_name is not None:
			lowerCAmelCase__	:						List[str]  				=	args.index_name
		if args.index_path is not None:
			lowerCAmelCase__	:						Optional[int]  				=	args.index_path
	else:
		lowerCAmelCase__	:						Dict  				=	BartForConditionalGeneration

	lowerCAmelCase__	:						Optional[int]  				=	(
	    [f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
	    if args.eval_all_checkpoints
	    else [args.model_name_or_path]
	)

	logger.info('''Evaluate the following checkpoints: %s'''						,     A_ )

	lowerCAmelCase__	:						Tuple  				=	get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
	lowerCAmelCase__	:						List[Any]  				=	evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

	for checkpoint in checkpoints:
		if os.path.exists(args.predictions_path ) and (not args.recalculate):
			logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path ) )
			score_fn(A_						,     args.predictions_path						,     args.gold_data_path )
			continue

		logger.info('''***** Running evaluation for {} *****'''.format(A_ ) )
		logger.info('''  Batch size = %d'''						,     args.eval_batch_size )
		logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path ) )

		if args.model_type.startswith('''rag''' ):
			lowerCAmelCase__	:						Dict  				=	RagRetriever.from_pretrained(A_						,     **A_ )
			lowerCAmelCase__	:						List[Any]  				=	model_class.from_pretrained(A_						,     retriever=A_						,     **A_ )
			model.retriever.init_retrieval()
		else:
			lowerCAmelCase__	:						Union[str, Any]  				=	model_class.from_pretrained(A_						,     **A_ )
		model.to(args.device )

		with open(args.evaluation_set						,     '''r''' ) as eval_file, open(args.predictions_path						,     '''w''' ) as preds_file:
			lowerCAmelCase__	:						Tuple  				=	[]
			for line in tqdm(A_ ):
				questions.append(line.strip() )
				if len(A_ ) == args.eval_batch_size:
					lowerCAmelCase__	:						int  				=	evaluate_batch_fn(A_						,     A_						,     A_ )
					preds_file.write('''\n'''.join(A_ ) + '''\n''' )
					preds_file.flush()
					lowerCAmelCase__	:						int  				=	[]
			if len(A_ ) > 0:
				lowerCAmelCase__	:						Optional[int]  				=	evaluate_batch_fn(A_						,     A_						,     A_ )
				preds_file.write('''\n'''.join(A_ ) )
				preds_file.flush()

			score_fn(A_						,     args.predictions_path						,     args.gold_data_path )


if __name__ == "__main__":
							__UpperCamelCase  :      Union[str, Any]							      =		get_args()
							main(args)







"""simple docstring"""





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


__UpperCamelCase  :      int							      =		logging.getLogger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# save results
	if os.path.exists(A_ ):
		if os.path.exists(os.path.join(A_						,     '''config.json''' ) ) and os.path.isfile(
		    os.path.join(A_						,     '''config.json''' ) ):
			os.remove(os.path.join(A_						,     '''config.json''' ) )
		if os.path.exists(os.path.join(A_						,     '''pytorch_model.bin''' ) ) and os.path.isfile(
		    os.path.join(A_						,     '''pytorch_model.bin''' ) ):
			os.remove(os.path.join(A_						,     '''pytorch_model.bin''' ) )
	else:
		os.makedirs(A_ )
	model.save_pretrained(A_ )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						Optional[Any]  				=	2
	if unlogit:
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.pow(A_						,     A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	p * torch.log(A_ )
	lowerCAmelCase__	:						List[Any]  				=	0
	return -plogp.sum(dim=-1 )

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	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   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_=True						,     A_=True						,     A_=None						,     A_=False ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	model.config.num_hidden_layers, model.config.num_attention_heads
	lowerCAmelCase__	:						Dict  				=	torch.zeros(A_						,     A_ ).to(args.device )
	lowerCAmelCase__	:						int  				=	torch.zeros(A_						,     A_ ).to(args.device )

	if head_mask is None:
		lowerCAmelCase__	:						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:
		lowerCAmelCase__	:						Union[str, Any]  				=	None

	lowerCAmelCase__	:						Optional[int]  				=	0.0
	lowerCAmelCase__	:						Optional[int]  				=	0.0
	for step, inputs in enumerate(tqdm(A_						,     desc='''Iteration'''						,     disable=args.local_rank not in [-1, 0] ) ):
		lowerCAmelCase__	:						Any  				=	tuple(t.to(args.device ) for t in inputs )
		((lowerCAmelCase__)				,)	:						List[Any]  				=	inputs

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		lowerCAmelCase__	:						Any  				=	model(A_						,     labels=A_						,     head_mask=A_ )
		#  (loss), lm_logits, presents, (all hidden_states), (attentions)
		lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						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
		total_loss += loss.detach().cpu().numpy()
		if compute_entropy:
			for layer, attn in enumerate(A_ ):
				lowerCAmelCase__	:						Dict  				=	entropy(attn.detach()						,     A_ )
				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(A_ ).float().detach().sum().data

	# Normalize
	attn_entropy /= tot_tokens
	head_importance /= tot_tokens
	# Layerwise importance normalization
	if not args.dont_normalize_importance_by_layer:
		lowerCAmelCase__	:						Any  				=	2
		lowerCAmelCase__	:						Dict  				=	torch.pow(torch.pow(A_						,     A_ ).sum(-1 )						,     1 / exponent )
		head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-2_0

	if not args.dont_normalize_global_importance:
		lowerCAmelCase__	:						List[Any]  				=	(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	compute_heads_importance(A_						,     A_						,     A_						,     compute_entropy=A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	1 / loss  # instead of downsteam score use the LM loss
	logger.info('''Pruning: original score: %f, threshold: %f'''						,     A_						,     original_score * args.masking_threshold )

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

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

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

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

		# Compute metric and head importance again
		lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	compute_heads_importance(
		    A_						,     A_						,     A_						,     compute_entropy=A_						,     head_mask=A_ )
		lowerCAmelCase__	:						Tuple  				=	1 / loss
		logger.info(
		    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''						,     A_						,     new_head_mask.sum()						,     new_head_mask.sum() / new_head_mask.numel() * 1_00						,     )

	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   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Optional[Any]  				=	datetime.now()
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	compute_heads_importance(
	    A_						,     A_						,     A_						,     compute_entropy=A_						,     compute_importance=A_						,     head_mask=A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	1 / loss
	lowerCAmelCase__	:						Tuple  				=	datetime.now() - before_time

	lowerCAmelCase__	:						int  				=	sum(p.numel() for p in model.parameters() )
	lowerCAmelCase__	:						List[Any]  				=	{
	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
	}

	for k, v in heads_to_prune.items():
		if isinstance(A_						,     A_ ):
			lowerCAmelCase__	:						int  				=	[
			    v,
			]

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

	lowerCAmelCase__	:						Any  				=	datetime.now()
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	compute_heads_importance(
	    A_						,     A_						,     A_						,     compute_entropy=A_						,     compute_importance=A_						,     head_mask=A_						,     actually_pruned=A_						,     )

	lowerCAmelCase__	:						int  				=	1 / loss
	lowerCAmelCase__	:						Dict  				=	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 * 1_00						,     )
	logger.info('''Pruning: score with masking: %f score with pruning: %f'''						,     A_						,     A_ )
	logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''						,     original_time / new_time * 1_00 )
	save_model(A_						,     args.output_dir )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	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(
	    '''--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 s3'''						,     )
	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=1_28						,     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.''' )
	lowerCAmelCase__	:						Optional[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=A_ )
		ptvsd.wait_for_attach()

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

	lowerCAmelCase__	:						List[str]  				=	GPTaLMHeadModel.from_pretrained(args.model_name_or_path )

	# Distributed and parallel training
	model.to(args.device )
	if args.local_rank != -1:
		lowerCAmelCase__	:						Dict  				=	nn.parallel.DistributedDataParallel(
		    A_						,     device_ids=[args.local_rank]						,     output_device=args.local_rank						,     find_unused_parameters=A_ )
	elif args.n_gpu > 1:
		lowerCAmelCase__	:						List[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
	lowerCAmelCase__	:						str  				=	np.concatenate(
	    [
	        np.loadtxt(args.data_dir						,     dtype=np.intaa ),
	    ] )
	lowerCAmelCase__	:						Union[str, Any]  				=	(torch.from_numpy(A_ ),)
	lowerCAmelCase__	:						Tuple  				=	TensorDataset(*A_ )
	lowerCAmelCase__	:						Optional[int]  				=	RandomSampler(A_ )
	lowerCAmelCase__	:						Dict  				=	DataLoader(A_						,     sampler=A_						,     batch_size=args.batch_size )

	# 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:
		lowerCAmelCase__	:						Tuple  				=	mask_heads(A_						,     A_						,     A_ )
		prune_heads(A_						,     A_						,     A_						,     A_ )


if __name__ == "__main__":
							main()




