



"""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


_lowerCAmelCase    :Optional[int]     =					logging.getLogger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		Optional[Any]							):
    # save results
    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__		:		int     ,       UpperCamelCase__		:		Dict=False							):
    _UpperCAmelCase							:  int											=    2
    if unlogit:
        _UpperCAmelCase							:  List[str]											=    torch.pow(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  Union[str, Any]											=    p * torch.log(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    0
    return -plogp.sum(dim=-1							)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]							):
    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__		:		List[str]     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		Any=True     ,       UpperCamelCase__		:		int=True     ,       UpperCamelCase__		:		Dict=None     ,       UpperCamelCase__		:		Any=False							):
    _UpperCAmelCase							,				_UpperCAmelCase							:  Union[str, Any]											=    model.config.num_hidden_layers, model.config.num_attention_heads
    _UpperCAmelCase							:  str											=    torch.zeros(UpperCamelCase__     ,       UpperCamelCase__							).to(args.device							)
    _UpperCAmelCase							:  Union[str, Any]											=    torch.zeros(UpperCamelCase__     ,       UpperCamelCase__							).to(args.device							)

    if head_mask is None:
        _UpperCAmelCase							:  Optional[int]											=    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:
        _UpperCAmelCase							:  Optional[Any]											=    None

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

        # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
        _UpperCAmelCase							:  List[Any]											=    model(UpperCamelCase__     ,       labels=UpperCamelCase__     ,       head_mask=UpperCamelCase__							)
        #  (loss), lm_logits, presents, (all hidden_states), (attentions)
        _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  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(UpperCamelCase__							):
                _UpperCAmelCase							:  Optional[int]											=    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:
        _UpperCAmelCase							:  Tuple											=    2
        _UpperCAmelCase							:  List[str]											=    torch.pow(torch.pow(UpperCamelCase__     ,       UpperCamelCase__							).sum(-1							)     ,       1 / exponent							)
        head_importance /= norm_by_layer.unsqueeze(-1							) + 1E-2_0

    if not args.dont_normalize_global_importance:
        _UpperCAmelCase							:  str											=    (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'''							)
    _UpperCAmelCase							:  Any											=    torch.zeros(head_importance.numel()     ,       dtype=torch.long     ,       device=args.device							)
    _UpperCAmelCase							:  List[Any]											=    torch.arange(
        head_importance.numel()     ,       device=args.device							)
    _UpperCAmelCase							:  str											=    head_ranks.view_as(UpperCamelCase__							)
    print_ad_tensor(UpperCamelCase__							)
    return attn_entropy, head_importance, total_loss





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

    _UpperCAmelCase							:  Dict											=    torch.ones_like(UpperCamelCase__							)
    _UpperCAmelCase							:  Any											=    max(1     ,       int(new_head_mask.numel() * args.masking_amount							)							)

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

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

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

        # Compute metric and head importance again
        _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    compute_heads_importance(
            UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       compute_entropy=UpperCamelCase__     ,       head_mask=UpperCamelCase__							)
        _UpperCAmelCase							:  Optional[Any]											=    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__		:		Optional[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							:  Dict											=    datetime.now()
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    compute_heads_importance(
        UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       compute_entropy=UpperCamelCase__     ,       compute_importance=UpperCamelCase__     ,       head_mask=UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    1 / loss
    _UpperCAmelCase							:  Any											=    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(UpperCamelCase__							)							)
    }

    for k, v in heads_to_prune.items():
        if isinstance(UpperCamelCase__     ,       UpperCamelCase__							):
            _UpperCAmelCase							:  List[str]											=    [
                v,
            ]

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

    _UpperCAmelCase							:  Dict											=    datetime.now()
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    compute_heads_importance(
        UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       compute_entropy=UpperCamelCase__     ,       compute_importance=UpperCamelCase__     ,       head_mask=UpperCamelCase__     ,       actually_pruned=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    1 / loss
    _UpperCAmelCase							:  List[str]											=    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_						():
    _UpperCAmelCase							:  str											=    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.'''							)
    _UpperCAmelCase							:  Union[str, 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=UpperCamelCase__							)
        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							:  Optional[Any]											=    0 if args.no_cuda else torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank							)
        _UpperCAmelCase							:  Union[str, Any]											=    torch.device('''cuda'''     ,       args.local_rank							)
        _UpperCAmelCase							:  List[str]											=    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							:  Optional[Any]											=    GPTaLMHeadModel.from_pretrained(args.model_name_or_path							)

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


if __name__ == "__main__":
  main()







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase    :int     =					{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMSNModel',
      'ViTMSNForImageClassification',
      'ViTMSNPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


