

'''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


_a					:			List[Any]							=    logging.getLogger(__name__)


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  # 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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : List[Any]=False    ):
  UpperCAmelCase    				=						2
  if unlogit:
    UpperCAmelCase    				=						torch.pow(__A							, __A    )
  UpperCAmelCase    				=						p * torch.log(__A    )
  UpperCAmelCase    				=						0
  return -plogp.sum(dim=-1    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int    ):
  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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Union[str, Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Tuple=True							, SCREAMING_SNAKE_CASE   : Tuple=True							, SCREAMING_SNAKE_CASE   : Tuple=None							, SCREAMING_SNAKE_CASE   : Optional[int]=False    ):
  UpperCAmelCase    				=						model.config.num_hidden_layers, model.config.num_attention_heads
  UpperCAmelCase    				=						torch.zeros(__A							, __A    ).to(args.device    )
  UpperCAmelCase    				=						torch.zeros(__A							, __A    ).to(args.device    )

  if head_mask is None:
    UpperCAmelCase    				=						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    				=						None

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

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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Optional[Any]    ):
  UpperCAmelCase    				=						compute_heads_importance(__A							, __A							, __A							, compute_entropy=__A    )
  UpperCAmelCase    				=						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    				=						torch.ones_like(__A    )
  UpperCAmelCase    				=						max(1							, int(new_head_mask.numel() * args.masking_amount    )    )

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

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

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

    # Compute metric and head importance again
    UpperCAmelCase    				=						compute_heads_importance(
        __A							, __A							, __A							, compute_entropy=__A							, head_mask=__A    )
    UpperCAmelCase    				=						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() * 100							, )

  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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Union[str, Any]							, SCREAMING_SNAKE_CASE   : List[str]    ):
  UpperCAmelCase    				=						datetime.now()
  UpperCAmelCase    				=						compute_heads_importance(
      __A							, __A							, __A							, compute_entropy=__A							, compute_importance=__A							, head_mask=__A    )
  UpperCAmelCase    				=						1 / loss
  UpperCAmelCase    				=						datetime.now() - before_time

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

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

  UpperCAmelCase    				=						datetime.now()
  UpperCAmelCase    				=						compute_heads_importance(
      __A							, __A							, __A							, compute_entropy=__A							, compute_importance=__A							, head_mask=__A							, actually_pruned=__A							, )

  UpperCAmelCase    				=						1 / loss
  UpperCAmelCase    				=						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 * 100							, )
  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 * 100    )
  save_model(__A							, args.output_dir    )


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						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=128							, 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.'    )
  UpperCAmelCase    				=						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    				=						torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'    )
    UpperCAmelCase    				=						0 if args.no_cuda else torch.cuda.device_count()
  else:
    torch.cuda.set_device(args.local_rank    )
    UpperCAmelCase    				=						torch.device('cuda'							, args.local_rank    )
    UpperCAmelCase    				=						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    				=						GPTaLMHeadModel.from_pretrained(args.model_name_or_path    )

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


if __name__ == "__main__":
  main()










'''simple docstring'''







import functools





def 		_UpperCAmelCase (				__A				: list[int]    ,		__A				: list[int]   ):

				# Validation
				if not isinstance(__A    ,		__A   ) or not all(isinstance(__A    ,		__A   ) for day in days   ):
								raise ValueError('''The parameter days should be a list of integers'''   )

				if len(__A   ) != 3 or not all(isinstance(__A    ,		__A   ) for cost in costs   ):
								raise ValueError('''The parameter costs should be a list of three integers'''   )

				if len(__A   ) == 0:
								return 0

				if min(__A   ) <= 0:
								raise ValueError('''All days elements should be greater than 0'''   )

				if max(__A   ) >= 3_66:
								raise ValueError('''All days elements should be less than 366'''   )

				a_		:	List[Any]									=   set(__A   )

				@functools.cache
				def dynamic_programming(__A				: int   ) -> int:
								if index > 3_65:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1   )

								return min(
								    costs[0] + dynamic_programming(index + 1   )    ,		costs[1] + dynamic_programming(index + 7   )    ,		costs[2] + dynamic_programming(index + 30   )    ,		)

				return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




