


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


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





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  List[str]    )			-> List[Any]:





	"""simple docstring"""







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





def 	_lowerCAmelCase					( UpperCamelCase__:  Any				,       UpperCamelCase__:  Dict=False    )			-> Optional[int]:





	"""simple docstring"""







	A             =					2
	if unlogit:
		A             =					torch.pow(lowerCAmelCase__				,       lowerCAmelCase__    )
	A             =					p * torch.log(lowerCAmelCase__    )
	A             =					0
	return -plogp.sum(dim=-1    )





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> Tuple:





	"""simple docstring"""







	logger.info("""lv, h >\t""" + """\t""".join(f'{x + 1}' for x in range(len(lowerCAmelCase__    )    )    )    )
	for row in range(len(lowerCAmelCase__    )    ):
		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__:  Dict				,       UpperCamelCase__:  int				,       UpperCamelCase__:  Dict				,       UpperCamelCase__:  Any=True				,       UpperCamelCase__:  Optional[int]=True				,       UpperCamelCase__:  Optional[int]=None				,       UpperCamelCase__:  Optional[Any]=False    )			-> Tuple:





	"""simple docstring"""







	A       ,      A             =					model.config.num_hidden_layers, model.config.num_attention_heads
	A             =					torch.zeros(lowerCAmelCase__				,       lowerCAmelCase__    ).to(args.device    )
	A             =					torch.zeros(lowerCAmelCase__				,       lowerCAmelCase__    ).to(args.device    )

	if head_mask is None:
		A             =					torch.ones(lowerCAmelCase__				,       lowerCAmelCase__    ).to(args.device    )

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

	A             =					0.0
	A             =					0.0
	for step, inputs in enumerate(tqdm(lowerCAmelCase__				,       desc="""Iteration"""				,       disable=args.local_rank not in [-1, 0]    )    ):
		A             =					tuple(t.to(args.device    ) for t in inputs    )
		((A )       ,       )             =					inputs

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		A             =					model(lowerCAmelCase__				,       labels=lowerCAmelCase__				,       head_mask=lowerCAmelCase__    )
		#  (loss), lm_logits, presents, (all hidden_states), (attentions)
		A       ,      A       ,      A             =					(
		    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(lowerCAmelCase__    ):
				A             =					entropy(attn.detach()				,       lowerCAmelCase__    )
				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(lowerCAmelCase__    ).float().detach().sum().data

	# Normalize
	attn_entropy /= tot_tokens
	head_importance /= tot_tokens
	# Layerwise importance normalization
	if not args.dont_normalize_importance_by_layer:
		A             =					2
		A             =					torch.pow(torch.pow(lowerCAmelCase__				,       lowerCAmelCase__    ).sum(-1    )				,       1 / exponent    )
		head_importance /= norm_by_layer.unsqueeze(-1    ) + 1e-20

	if not args.dont_normalize_global_importance:
		A             =					(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

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





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Dict				,       UpperCamelCase__:  Any    )			-> List[str]:





	"""simple docstring"""







	A       ,      A       ,      A             =					compute_heads_importance(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       compute_entropy=lowerCAmelCase__    )
	A             =					1 / loss  # instead of downsteam score use the LM loss
	logger.info("""Pruning: original score: %f, threshold: %f"""				,       lowerCAmelCase__				,       original_score * args.masking_threshold    )

	A             =					torch.ones_like(lowerCAmelCase__    )
	A             =					max(1				,       int(new_head_mask.numel() * args.masking_amount    )    )

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

		if len(lowerCAmelCase__    ) <= num_to_mask:
			print("""BREAK BY num_to_mask"""    )
			break

		# mask heads
		A             =					current_heads_to_mask[:num_to_mask]
		logger.info("""Heads to mask: %s"""				,       str(current_heads_to_mask.tolist()    )    )
		A             =					new_head_mask.view(-1    )
		A             =					0.0
		A             =					new_head_mask.view_as(lowerCAmelCase__    )
		A             =					new_head_mask.clone().detach()
		print_ad_tensor(lowerCAmelCase__    )

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

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

	return head_mask





def 	_lowerCAmelCase					( UpperCamelCase__:  Any				,       UpperCamelCase__:  List[str]				,       UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  List[Any]    )			-> str:





	"""simple docstring"""







	A             =					datetime.now()
	A       ,      A       ,      A             =					compute_heads_importance(
	    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       compute_entropy=lowerCAmelCase__				,       compute_importance=lowerCAmelCase__				,       head_mask=lowerCAmelCase__    )
	A             =					1 / loss
	A             =					datetime.now() - before_time

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

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

	assert sum(len(lowerCAmelCase__    ) for h in heads_to_prune.values()    ) == (1 - head_mask.long()).sum().item()
	model.prune_heads(lowerCAmelCase__    )
	A             =					sum(p.numel() for p in model.parameters()    )

	A             =					datetime.now()
	A       ,      A       ,      A             =					compute_heads_importance(
	    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       compute_entropy=lowerCAmelCase__				,       compute_importance=lowerCAmelCase__				,       head_mask=lowerCAmelCase__				,       actually_pruned=lowerCAmelCase__				,       )

	A             =					1 / loss
	A             =					datetime.now() - before_time

	logger.info(
	    """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""				,       lowerCAmelCase__				,       lowerCAmelCase__				,       pruned_num_params / original_num_params * 1_00				,       )
	logger.info("""Pruning: score with masking: %f score with pruning: %f"""				,       lowerCAmelCase__				,       lowerCAmelCase__    )
	logger.info("""Pruning: speed ratio (original timing / new timing): %f percents"""				,       original_time / new_time * 1_00    )
	save_model(lowerCAmelCase__				,       args.output_dir    )





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





	"""simple docstring"""







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

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

	parser.add_argument(
	    """--max_seq_length"""				,       default=1_28				,       type=lowerCAmelCase__				,       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=lowerCAmelCase__				,       help="""Batch size."""    )

	parser.add_argument("""--seed"""				,       type=lowerCAmelCase__				,       default=42    )
	parser.add_argument("""--local_rank"""				,       type=lowerCAmelCase__				,       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=lowerCAmelCase__				,       default=""""""				,       help="""Can be used for distant debugging."""    )
	parser.add_argument("""--server_port"""				,       type=lowerCAmelCase__				,       default=""""""				,       help="""Can be used for distant debugging."""    )
	A             =					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=lowerCAmelCase__    )
		ptvsd.wait_for_attach()

	# Setup devices and distributed training
	if args.local_rank == -1 or args.no_cuda:
		A             =					torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""    )
		A             =					0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank    )
		A             =					torch.device("""cuda"""				,       args.local_rank    )
		A             =					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    )    )    )

	A             =					GPTaLMHeadModel.from_pretrained(args.model_name_or_path    )

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

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

	# Prepare dataset
	A             =					np.concatenate(
	    [
	        np.loadtxt(args.data_dir				,       dtype=np.intaa    ),
	    ]    )
	A             =					(torch.from_numpy(lowerCAmelCase__    ),)
	A             =					TensorDataset(*lowerCAmelCase__    )
	A             =					RandomSampler(lowerCAmelCase__    )
	A             =					DataLoader(lowerCAmelCase__				,       sampler=lowerCAmelCase__				,       batch_size=args.batch_size    )

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

	# 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:
		A             =					mask_heads(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__    )
		prune_heads(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__    )


if __name__ == "__main__":
				main()





"""simple docstring"""


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_    							=					namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			return (abs(source - target						) / target) < 0.01






@pytest.mark.integration
def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  _TestCommandArgs(dataset=lowerCAmelCase__	,all_configs=lowerCAmelCase__	,save_infos=lowerCAmelCase__						)
			lowerCamelCase_					=  TestCommand(*lowerCAmelCase__						)
			test_command.run()
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,'''README.md'''						)
			assert os.path.exists(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict.from_directory(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict(
			    {
			        '''default''': DatasetInfo(
			            features=Features(
			                {
			                    '''tokens''': Sequence(Value('''string'''						)						),
			                    '''ner_tags''': Sequence(
			                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
			                    '''langs''': Sequence(Value('''string'''						)						),
			                    '''spans''': Sequence(Value('''string'''						)						),
			                }						)	,splits=[
			                {
			                    '''name''': '''train''',
			                    '''num_bytes''': 2_351_563,
			                    '''num_examples''': 10_000,
			                },
			                {
			                    '''name''': '''validation''',
			                    '''num_bytes''': 238_418,
			                    '''num_examples''': 1_000,
			                },
			            ]	,download_size=3_940_680	,dataset_size=2_589_981	,)
			    }						)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase_		, lowerCamelCase_					=  getattr(dataset_infos['''default''']	,lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']	,lowerCAmelCase__						)
						if key == "num_bytes":
									assert is_apercent_close(lowerCAmelCase__	,lowerCAmelCase__						)
						elif key == "splits":
									assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes	,expected[split].num_bytes						)
						else:
									result == expected



