





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase_      							=     subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
UpperCAmelCase_      							=     subprocess.check_output(F"git diff --name-only {fork_point_sha}".split()).decode("""utf-8""").split()

UpperCAmelCase_      							=     """|""".join(sys.argv[1:])
UpperCAmelCase_      							=     re.compile(RF"^({joined_dirs}).*?\.py$")

UpperCAmelCase_      							=     [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")










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_      							=     logging.getLogger(__name__)





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       List[str]						)			-> Tuple:



					'''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__			:       List[str]       ,    UpperCamelCase__			:       Dict=False						)			-> str:



					'''simple docstring'''




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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Dict						)			-> Optional[Any]:



					'''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__			:       Optional[int]       ,    UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Optional[Any]=True       ,    UpperCamelCase__			:       Any=True       ,    UpperCamelCase__			:       str=None       ,    UpperCamelCase__			:       Optional[int]=False						)			-> Any:



					'''simple docstring'''




					_snake_case							,     _snake_case									=				model.config.num_hidden_layers, model.config.num_attention_heads
					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__						).to(args.device						)
					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__						).to(args.device						)

					if head_mask is None:
										_snake_case									=				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									=				None

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

										# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
										_snake_case									=				model(UpperCamelCase__       ,    labels=UpperCamelCase__       ,    head_mask=UpperCamelCase__						)
										#  (loss), lm_logits, presents, (all hidden_states), (attentions)
										_snake_case							,     _snake_case							,     _snake_case									=				(
										    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									=				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									=				2
										_snake_case									=				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									=				(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									=				torch.zeros(head_importance.numel()       ,    dtype=torch.long       ,    device=args.device						)
					_snake_case									=				torch.arange(
					    head_importance.numel()       ,    device=args.device						)
					_snake_case									=				head_ranks.view_as(UpperCamelCase__						)
					print_ad_tensor(UpperCamelCase__						)
					return attn_entropy, head_importance, total_loss





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



					'''simple docstring'''




					_snake_case							,     _snake_case							,     _snake_case									=				compute_heads_importance(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    compute_entropy=UpperCamelCase__						)
					_snake_case									=				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									=				torch.ones_like(UpperCamelCase__						)
					_snake_case									=				max(1       ,    int(new_head_mask.numel() * args.masking_amount						)						)

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

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

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

										# Compute metric and head importance again
										_snake_case							,     _snake_case							,     _snake_case									=				compute_heads_importance(
										    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    compute_entropy=UpperCamelCase__       ,    head_mask=UpperCamelCase__						)
										_snake_case									=				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__			:       Any       ,    UpperCamelCase__			:       int       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       int						)			-> List[Any]:



					'''simple docstring'''




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

					_snake_case									=				sum(p.numel() for p in model.parameters()						)
					_snake_case									=				{
					    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									=				[
															    v,
															]

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

					_snake_case									=				datetime.now()
					_snake_case							,     _snake_case							,     _snake_case									=				compute_heads_importance(
					    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    compute_entropy=UpperCamelCase__       ,    compute_importance=UpperCamelCase__       ,    head_mask=UpperCamelCase__       ,    actually_pruned=UpperCamelCase__       ,    )

					_snake_case									=				1 / loss
					_snake_case									=				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__     (     )			-> str:



					'''simple docstring'''




					_snake_case									=				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									=				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									=				torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'						)
										_snake_case									=				0 if args.no_cuda else torch.cuda.device_count()
					else:
										torch.cuda.set_device(args.local_rank						)
										_snake_case									=				torch.device('cuda'       ,    args.local_rank						)
										_snake_case									=				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									=				GPTaLMHeadModel.from_pretrained(args.model_name_or_path						)

					# Distributed and parallel training
					model.to(args.device						)
					if args.local_rank != -1:
										_snake_case									=				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									=				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									=				np.concatenate(
					    [
					        np.loadtxt(args.data_dir       ,    dtype=np.intaa						),
					    ]						)
					_snake_case									=				(torch.from_numpy(UpperCamelCase__						),)
					_snake_case									=				TensorDataset(*UpperCamelCase__						)
					_snake_case									=				RandomSampler(UpperCamelCase__						)
					_snake_case									=				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									=				mask_heads(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)
										prune_heads(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)


if __name__ == "__main__":
					main()



