



'''simple docstring'''





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

lowercase_									=    logging.getLogger(__name__)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			Optional[str]		=			field(
     default='tab_fact'  ,  metadata={'help': 'The name of the dataset to use (via the datasets library).'}		)
 __lowerCamelCase       :			Optional[str]		=			field(
     default='tab_fact'  ,  metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}  ,  )
 __lowerCamelCase       :			int		=			field(
     default=1_024  ,  metadata={
         'help': (
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )
     }  ,  )
 __lowerCamelCase       :			bool		=			field(
     default=A  ,  metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}		)
 __lowerCamelCase       :			bool		=			field(
     default=A  ,  metadata={
         'help': (
             'Whether to pad all samples to `max_seq_length`. '
             'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
         )
     }  ,  )
 __lowerCamelCase       :			Optional[int]		=			field(
     default=A  ,  metadata={
         'help': (
             'For debugging purposes or quicker training, truncate the number of training examples to this '
             'value if set.'
         )
     }  ,  )
 __lowerCamelCase       :			Optional[int]		=			field(
     default=A  ,  metadata={
         'help': (
             'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
             'value if set.'
         )
     }  ,  )
 __lowerCamelCase       :			Optional[int]		=			field(
     default=A  ,  metadata={
         'help': (
             'For debugging purposes or quicker training, truncate the number of prediction examples to this '
             'value if set.'
         )
     }  ,  )
 __lowerCamelCase       :			Optional[str]		=			field(
     default=A  ,  metadata={'help': 'A csv or a json file containing the training data.'}		)
 __lowerCamelCase       :			Optional[str]		=			field(
     default=A  ,  metadata={'help': 'A csv or a json file containing the validation data.'}		)
 __lowerCamelCase       :			Optional[str]		=			field(default=A  ,  metadata={'help': 'A csv or a json file containing the test data.'}		)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      if self.dataset_name is not None:
           pass
      elif self.train_file is None or self.validation_file is None:
           raise ValueError('''Need either a GLUE task, a training/validation file or a dataset name.'''			)
      else:
           _a           =    self.train_file.split('''.'''			)[-1]
           assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
           _a           =    self.validation_file.split('''.'''			)[-1]
           assert (
               validation_extension == train_extension
           ), "`validation_file` should have the same extension (csv or json) as `train_file`."






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			str		=			field(
     default=A  ,  metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		)
 __lowerCamelCase       :			Optional[str]		=			field(
     default=A  ,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}		)
 __lowerCamelCase       :			Optional[str]		=			field(
     default=A  ,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}		)
 __lowerCamelCase       :			Optional[str]		=			field(
     default=A  ,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,  )
 __lowerCamelCase       :			bool		=			field(
     default=A  ,  metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}  ,  )
 __lowerCamelCase       :			str		=			field(
     default='main'  ,  metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}  ,  )
 __lowerCamelCase       :			bool		=			field(
     default=A  ,  metadata={
         'help': (
             'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
             'with private models).'
         )
     }  ,  )





def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
     if len(sys.argv) == 2 and sys.argv[1].endswith('''.json'''):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _a    ,       _a    ,       _a           =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
     else:
          _a    ,       _a    ,       _a           =    parser.parse_args_into_dataclasses()

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,      datefmt='''%m/%d/%Y %H:%M:%S'''    ,      handlers=[logging.StreamHandler(sys.stdout)]    ,      )

     _a           =    training_args.get_process_log_level()
     logger.setLevel(__A)
     datasets.utils.logging.set_verbosity(__A)
     transformers.utils.logging.set_verbosity(__A)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
         + F'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
     logger.info(F'''Training/evaluation parameters {training_args}''')

     # Detecting last checkpoint.
     _a           =    None
     if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
          _a           =    get_last_checkpoint(training_args.output_dir)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
               raise ValueError(
                   F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                   '''Use --overwrite_output_dir to overcome.''')
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                   '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''')

    # Set seed before initializing model.
     set_seed(training_args.seed)

     # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
     # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
     #
     # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
     # single column. You can easily tweak this behavior (see below)
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          _a           =    load_dataset(
              data_args.dataset_name    ,      data_args.dataset_config_name    ,      cache_dir=model_args.cache_dir)
     else:
          # Loading a dataset from your local files.
          # CSV/JSON training and evaluation files are needed.
          _a           =    {'''train''': data_args.train_file, '''validation''': data_args.validation_file}

          # Get the test dataset: you can provide your own CSV/JSON test file (see below)
          # when you use `do_predict` without specifying a GLUE benchmark task.
          if training_args.do_predict:
               if data_args.test_file is not None:
                    _a           =    data_args.train_file.split('''.''')[-1]
                    _a           =    data_args.test_file.split('''.''')[-1]
                    assert (
                        test_extension == train_extension
                    ), "`test_file` should have the same extension (csv or json) as `train_file`."
                    _a           =    data_args.test_file
               else:
                    raise ValueError('''Need either a GLUE task or a test file for `do_predict`.''')

          for key in data_files.keys():
               logger.info(F'''load a local file for {key}: {data_files[key]}''')

          if data_args.train_file.endswith('''.csv'''):
               # Loading a dataset from local csv files
               _a           =    load_dataset('''csv'''    ,      data_files=__A    ,      cache_dir=model_args.cache_dir)
          else:
               # Loading a dataset from local json files
               _a           =    load_dataset('''json'''    ,      data_files=__A    ,      cache_dir=model_args.cache_dir)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
     _a           =    raw_datasets['''train'''].features['''label'''].names
     _a           =    len(__A)

     # Load pretrained model and tokenizer
     #
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     _a           =    AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,      num_labels=__A    ,      cache_dir=model_args.cache_dir    ,      revision=model_args.model_revision    ,      use_auth_token=True if model_args.use_auth_token else None    ,      )
     # load tapex tokenizer
     _a           =    TapexTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir    ,      use_fast=model_args.use_fast_tokenizer    ,      revision=model_args.model_revision    ,      use_auth_token=True if model_args.use_auth_token else None    ,      add_prefix_space=__A    ,      )
     _a           =    BartForSequenceClassification.from_pretrained(
         model_args.model_name_or_path    ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path)    ,      config=__A    ,      cache_dir=model_args.cache_dir    ,      revision=model_args.model_revision    ,      use_auth_token=True if model_args.use_auth_token else None    ,      )

     # Padding strategy
     if data_args.pad_to_max_length:
          _a           =    '''max_length'''
     else:
          # We will pad later, dynamically at batch creation, to the max sequence length in each batch
          _a           =    False

     # Some models have set the order of the labels to use, so let's make sure we do use it.
     _a           =    {'''Refused''': 0, '''Entailed''': 1}
     _a           =    {0: '''Refused''', 1: '''Entailed'''}

     if data_args.max_seq_length > tokenizer.model_max_length:
          logger.warning(
              F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
              F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''')
     _a           =    min(data_args.max_seq_length    ,      tokenizer.model_max_length)

     def preprocess_tabfact_function(__A):
          # Tokenize the texts
          def _convert_table_text_to_pandas(__A):
               _a           =    [_table_row.split('''#''') for _table_row in _table_text.strip('''\n''').split('''\n''')]
               _a           =    pd.DataFrame.from_records(_table_content[1:]    ,      columns=_table_content[0])
               return _table_pd

          _a           =    examples['''statement''']
          _a           =    list(map(_convert_table_text_to_pandas    ,      examples['''table_text''']))
          _a           =    tokenizer(__A    ,      __A    ,      padding=__A    ,      max_length=__A    ,      truncation=__A)

          _a           =    examples['''label''']
          return result

     with training_args.main_process_first(desc='''dataset map pre-processing'''):
          _a           =    raw_datasets.map(
              __A    ,      batched=__A    ,      load_from_cache_file=not data_args.overwrite_cache    ,      desc='''Running tokenizer on dataset'''    ,      )
     if training_args.do_train:
          if "train" not in raw_datasets:
               raise ValueError('''--do_train requires a train dataset''')
          _a           =    raw_datasets['''train''']
          if data_args.max_train_samples is not None:
               _a           =    train_dataset.select(range(data_args.max_train_samples))

     if training_args.do_eval:
          if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
               raise ValueError('''--do_eval requires a validation dataset''')
          _a           =    raw_datasets['''validation''']
          if data_args.max_eval_samples is not None:
               _a           =    eval_dataset.select(range(data_args.max_eval_samples))

     if training_args.do_predict or data_args.test_file is not None:
          if "test" not in raw_datasets and "test_matched" not in raw_datasets:
               raise ValueError('''--do_predict requires a test dataset''')
          _a           =    raw_datasets['''test''']
          if data_args.max_predict_samples is not None:
               _a           =    predict_dataset.select(range(data_args.max_predict_samples))

    # Log a few random samples from the training set:
     if training_args.do_train:
          for index in random.sample(range(len(__A))    ,      3):
               logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''')

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(__A):
          _a           =    p.predictions[0] if isinstance(p.predictions    ,      __A) else p.predictions
          _a           =    np.argmax(__A    ,      axis=1)
          return {"accuracy": (preds == p.label_ids).astype(np.floataa).mean().item()}

     # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
     if data_args.pad_to_max_length:
          _a           =    default_data_collator
     elif training_args.fpaa:
          _a           =    DataCollatorWithPadding(__A    ,      pad_to_multiple_of=8)
     else:
          _a           =    None

     # Initialize our Trainer
     _a           =    Trainer(
         model=__A    ,      args=__A    ,      train_dataset=train_dataset if training_args.do_train else None    ,      eval_dataset=eval_dataset if training_args.do_eval else None    ,      compute_metrics=__A    ,      tokenizer=__A    ,      data_collator=__A    ,      )

     # Training
     if training_args.do_train:
          _a           =    None
          if training_args.resume_from_checkpoint is not None:
               _a           =    training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _a           =    last_checkpoint
          _a           =    trainer.train(resume_from_checkpoint=__A)
          _a           =    train_result.metrics
          _a           =    (
              data_args.max_train_samples if data_args.max_train_samples is not None else len(__A)
          )
          _a           =    min(__A    ,      len(__A))

          trainer.save_model()  # Saves the tokenizer too for easy upload

          trainer.log_metrics('''train'''    ,      __A)
          trainer.save_metrics('''train'''    ,      __A)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          logger.info('''*** Evaluate ***''')

          _a           =    trainer.evaluate(eval_dataset=__A)
          _a           =    data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A)
          _a           =    min(__A    ,      len(__A))

          trainer.log_metrics('''eval'''    ,      __A)
          trainer.save_metrics('''eval'''    ,      __A)

     if training_args.do_predict:
          logger.info('''*** Predict ***''')

          # Removing the `label` columns because it contains -1 and Trainer won't like that.
          _a           =    predict_dataset.remove_columns('''label''')
          _a           =    trainer.predict(__A    ,      metric_key_prefix='''predict''').predictions
          _a           =    np.argmax(__A    ,      axis=1)

          _a           =    os.path.join(training_args.output_dir    ,      '''predict_results_tabfact.txt''')
          if trainer.is_world_process_zero():
               with open(__A    ,      '''w''') as writer:
                    logger.info('''***** Predict Results *****''')
                    writer.write('''index\tprediction\n''')
                    for index, item in enumerate(__A):
                         _a           =    label_list[item]
                         writer.write(F'''{index}\t{item}\n''')

     _a           =    {'''finetuned_from''': model_args.model_name_or_path, '''tasks''': '''text-classification'''}

     if training_args.push_to_hub:
          trainer.push_to_hub(**__A)
     else:
          trainer.create_model_card(**__A)




def lowerCAmelCase    (__A):



     """simple docstring"""



     main()


if __name__ == "__main__":
  main()








'''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


lowercase_									=    logging.getLogger(__name__)




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     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    (__A    ,      __A=False):



     """simple docstring"""



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




def lowerCAmelCase    (__A):



     """simple docstring"""



     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    (__A    ,      __A    ,      __A    ,      __A=True    ,      __A=True    ,      __A=None    ,      __A=False):



     """simple docstring"""



     _a    ,       _a           =    model.config.num_hidden_layers, model.config.num_attention_heads
     _a           =    torch.zeros(__A    ,      __A).to(args.device)
     _a           =    torch.zeros(__A    ,      __A).to(args.device)

     if head_mask is None:
          _a           =    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:
          _a           =    None

     _a           =    0.0
     _a           =    0.0
     for step, inputs in enumerate(tqdm(__A    ,      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(__A    ,      labels=__A    ,      head_mask=__A)
          #  (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(__A):
                    _a           =    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:
          _a           =    2
          _a           =    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:
          _a           =    (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''')
     _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(__A)
     print_ad_tensor(__A)
     return attn_entropy, head_importance, total_loss




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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

     _a           =    torch.ones_like(__A)
     _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(__A) <= 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(__A)
          _a           =    new_head_mask.clone().detach()
          print_ad_tensor(__A)

          # Compute metric and head importance again
          _a    ,       _a    ,       _a           =    compute_heads_importance(
              __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      head_mask=__A)
          _a           =    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    (__A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    datetime.now()
     _a    ,       _a    ,       _a           =    compute_heads_importance(
         __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      compute_importance=__A    ,      head_mask=__A)
     _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(__A))
     }

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

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

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

     _a           =    1 / loss
     _a           =    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    ():



     """simple docstring"""



     _a           =    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.''')
     _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=__A)
          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(
              __A    ,      device_ids=[args.local_rank]    ,      output_device=args.local_rank    ,      find_unused_parameters=__A)
     elif args.n_gpu > 1:
          _a           =    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
     _a           =    np.concatenate(
         [
             np.loadtxt(args.data_dir    ,      dtype=np.intaa),
         ])
     _a           =    (torch.from_numpy(__A),)
     _a           =    TensorDataset(*__A)
     _a           =    RandomSampler(__A)
     _a           =    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:
          _a           =    mask_heads(__A    ,      __A    ,      __A)
          prune_heads(__A    ,      __A    ,      __A    ,      __A)


if __name__ == "__main__":
  main()



