



'''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


snake_case_ :	List[str]         =  logging.getLogger(__name__)






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						# 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 A__    (     UpperCAmelCase_  ,  UpperCAmelCase_=False							):
						_UpperCamelCase					:		Union[str, Any] 				=   2
						if unlogit:
												_UpperCamelCase					:		Optional[Any] 				=   torch.pow(__A  ,  __A							)
						_UpperCamelCase					:		int 				=   p * torch.log(__A							)
						_UpperCamelCase					:		List[Any] 				=   0
						return -plogp.sum(dim=-1							)






def A__    (     UpperCAmelCase_							):
						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 A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=True  ,  UpperCAmelCase_=True  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=False							):
						_UpperCamelCase				,			_UpperCamelCase					:		List[str] 				=   model.config.num_hidden_layers, model.config.num_attention_heads
						_UpperCamelCase					:		Any 				=   torch.zeros(__A  ,  __A							).to(args.device							)
						_UpperCamelCase					:		Dict 				=   torch.zeros(__A  ,  __A							).to(args.device							)

						if head_mask is None:
												_UpperCamelCase					:		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:
												_UpperCamelCase					:		Dict 				=   None

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

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												_UpperCamelCase					:		Optional[Any] 				=   model(__A  ,  labels=__A  ,  head_mask=__A							)
												#  (loss), lm_logits, presents, (all hidden_states), (attentions)
												_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Tuple 				=   (
												    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							):
																								_UpperCamelCase					:		Union[str, Any] 				=   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:
												_UpperCamelCase					:		Any 				=   2
												_UpperCamelCase					:		Optional[int] 				=   torch.pow(torch.pow(__A  ,  __A							).sum(-1							)  ,  1 / exponent							)
												head_importance /= norm_by_layer.unsqueeze(-1							) + 1E-20

						if not args.dont_normalize_global_importance:
												_UpperCamelCase					:		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(__A							)
						if compute_importance:
												logger.info('Head importance scores'							)
												print_ad_tensor(__A							)
						logger.info('Head ranked by importance scores'							)
						_UpperCamelCase					:		int 				=   torch.zeros(head_importance.numel()  ,  dtype=torch.long  ,  device=args.device							)
						_UpperCamelCase					:		Optional[Any] 				=   torch.arange(
						    head_importance.numel()  ,  device=args.device							)
						_UpperCamelCase					:		Any 				=   head_ranks.view_as(__A							)
						print_ad_tensor(__A							)
						return attn_entropy, head_importance, total_loss






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Tuple 				=   compute_heads_importance(__A  ,  __A  ,  __A  ,  compute_entropy=__A							)
						_UpperCamelCase					:		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							)

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

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

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

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

												# Compute metric and head importance again
												_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Union[str, Any] 				=   compute_heads_importance(
												    __A  ,  __A  ,  __A  ,  compute_entropy=__A  ,  head_mask=__A							)
												_UpperCamelCase					:		Any 				=   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_0_0  ,  )

						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 A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Optional[int] 				=   datetime.now()
						_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Tuple 				=   compute_heads_importance(
						    __A  ,  __A  ,  __A  ,  compute_entropy=__A  ,  compute_importance=__A  ,  head_mask=__A							)
						_UpperCamelCase					:		str 				=   1 / loss
						_UpperCamelCase					:		str 				=   datetime.now() - before_time

						_UpperCamelCase					:		str 				=   sum(p.numel() for p in model.parameters()							)
						_UpperCamelCase					:		Dict 				=   {
						    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							):
																		_UpperCamelCase					:		int 				=   [
																		    v,
																		]

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

						_UpperCamelCase					:		List[str] 				=   datetime.now()
						_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		List[str] 				=   compute_heads_importance(
						    __A  ,  __A  ,  __A  ,  compute_entropy=__A  ,  compute_importance=__A  ,  head_mask=__A  ,  actually_pruned=__A  ,  )

						_UpperCamelCase					:		List[Any] 				=   1 / loss
						_UpperCamelCase					:		int 				=   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_0_0  ,  )
						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_0_0							)
						save_model(__A  ,  args.output_dir							)






def A__    (     ):
						_UpperCamelCase					:		Optional[int] 				=   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_2_8  ,  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=4_2							)
						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.'							)
						_UpperCamelCase					:		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:
												_UpperCamelCase					:		Dict 				=   torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
												_UpperCamelCase					:		Any 				=   0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank							)
												_UpperCamelCase					:		Dict 				=   torch.device('cuda'  ,  args.local_rank							)
												_UpperCamelCase					:		Optional[int] 				=   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							)							)							)

						_UpperCamelCase					:		Any 				=   GPTaLMHeadModel.from_pretrained(args.model_name_or_path							)

						# Distributed and parallel training
						model.to(args.device							)
						if args.local_rank != -1:
												_UpperCamelCase					:		Optional[int] 				=   nn.parallel.DistributedDataParallel(
												    __A  ,  device_ids=[args.local_rank]  ,  output_device=args.local_rank  ,  find_unused_parameters=__A							)
						elif args.n_gpu > 1:
												_UpperCamelCase					:		Optional[int] 				=   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
						_UpperCamelCase					:		int 				=   np.concatenate(
						    [
						        np.loadtxt(args.data_dir  ,  dtype=np.intaa							),
						    ]							)
						_UpperCamelCase					:		str 				=   (torch.from_numpy(__A							),)
						_UpperCamelCase					:		int 				=   TensorDataset(*__A							)
						_UpperCamelCase					:		List[str] 				=   RandomSampler(__A							)
						_UpperCamelCase					:		Optional[int] 				=   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:
												_UpperCamelCase					:		Optional[Any] 				=   mask_heads(__A  ,  __A  ,  __A							)
												prune_heads(__A  ,  __A  ,  __A  ,  __A							)


if __name__ == "__main__":
					main()




'''simple docstring'''







from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase    :							str      =					(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase    :							list[int]      =					[ord(letter) for letter in string.ascii_lowercase]
lowercase    :							set[int]      =					{ord(char) for char in VALID_CHARS}

lowercase    :							list[str]      =					["the", "be", "to", "of", "and", "in", "that", "have"]







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str | None:
	_snake_case   						=		""
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42

	for keychar, cipherchar in zip(cycle(__A	)			,				__A	):
		_snake_case   						=		cipherchar ^ keychar
		if decodedchar not in VALID_INTS:
			return None
		decoded += chr(__A	)

	return decoded







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	list[str]:
	_snake_case   						=		[]
	for key in product(__A			,				repeat=3	):
		_snake_case   						=		try_key(__A			,				__A	)
		if encoded is not None:
			possibles.append(__A	)
	return possibles







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	list[str]:
	return [possible for possible in possibles if common_word in possible.lower()]







def 				SCREAMING_SNAKE_CASE__	(   __A = "p059_cipher.txt"	)       ->	int:
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		Path(__A	).parent.joinpath(__A	).read_text(encoding='utf-8'	)

	_snake_case   						=		[int(__A	) for number in data.strip().split(','	)]

	_snake_case   						=		filter_valid_chars(__A	)
	for common_word in COMMON_WORDS:
		_snake_case   						=		filter_common_word(__A			,				__A	)
		if len(__A	) == 1:
			break

	_snake_case   						=		possibles[0]
	return sum(ord(__A	) for char in decoded_text	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

