





'''simple docstring'''

import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


UpperCAmelCase					:		Tuple		=							datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
   import pyspark





@dataclass
class lowerCamelCase						(datasets.BuilderConfig					):
  _lowercase  :	Optional[datasets.Features]       					=				None




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      ):





      """simple docstring"""



      import pyspark

      def generate_fn():
            _snake_case		:	Tuple					     =     df.select('''*'''			,      pyspark.sql.functions.spark_partition_id().alias('''part_id'''	)	)
            for partition_id in partition_order:
                  _snake_case		:	List[Any]					     =     df_with_partition_id.select('''*'''	).where(f'''part_id = {partition_id}'''	).drop('''part_id'''	)
                  _snake_case		:	Optional[Any]					     =     partition_df.collect()
                  _snake_case		:	List[Any]					     =     0
                  for row in rows:
                        yield f'''{partition_id}_{row_id}''', row.asDict()
                        row_id += 1

      return generate_fn





class lowerCamelCase						(_BaseExamplesIterable					):







  def __init__(		self     ,     lowercase__     ,     lowercase__=None     ,     )		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	int					     =     df
        _snake_case		:	Any					     =     partition_order or range(self.df.rdd.getNumPartitions()		)
        _snake_case		:	Optional[Any]					     =     _generate_iterable_examples(self.df     ,     self.partition_order		)







  def __iter__(		self		)		->						Optional[int]:






        """simple docstring"""





        yield from self.generate_examples_fn()







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						"SparkExamplesIterable":






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     list(range(self.df.rdd.getNumPartitions()		)		)
        generator.shuffle(lowercase__		)
        return SparkExamplesIterable(self.df     ,     partition_order=lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						"SparkExamplesIterable":






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.split_shard_indices_by_worker(lowercase__     ,     lowercase__		)
        return SparkExamplesIterable(self.df     ,     partition_order=lowercase__		)







  @property
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        return len(self.partition_order		)





class lowerCamelCase						(datasets.DatasetBuilder					):
  _lowercase  :	List[Any]       					=				SparkConfig







  def __init__(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						Optional[int]:






        """simple docstring"""





        import pyspark

        _snake_case		:	Union[str, Any]					     =     pyspark.sql.SparkSession.builder.getOrCreate()
        _snake_case		:	List[str]					     =     df
        _snake_case		:	List[Any]					     =     working_dir

        super().__init__(
            cache_dir=lowercase__     ,     config_name=str(self.df.semanticHash()		)     ,     **lowercase__     ,     )







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        def create_cache_and_write_probe(lowercase__		):
              # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
              # already exist.
              os.makedirs(self._cache_dir     ,     exist_ok=lowercase__		)
              _snake_case		:	List[Any]					     =     os.path.join(self._cache_dir     ,     '''fs_test''' + uuid.uuida().hex		)
              # Opening the file in append mode will create a new file unless it already exists, in which case it will not
              # change the file contents.
              open(lowercase__     ,     '''a'''		)
              return [probe_file]

        if self._spark.conf.get('''spark.master'''     ,     ''''''		).startswith('''local'''		):
              return

        # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
        # accessible to the driver.
        # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
        if self._cache_dir:
              _snake_case		:	int					     =     (
                  self._spark.sparkContext.parallelize(range(1		)     ,     1		).mapPartitions(lowercase__		).collect()
              )
              if os.path.isfile(probe[0]		):
                    return

        raise ValueError(
            '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'''		)







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        return datasets.DatasetInfo(features=self.config.features		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        import pyspark

        def get_arrow_batch_size(lowercase__		):
              for batch in it:
                    yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}		)

        _snake_case		:	Any					     =     self.df.count()
        _snake_case		:	List[Any]					     =     df_num_rows if df_num_rows <= 100 else 100
        # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
        _snake_case		:	List[str]					     =     (
            self.df.limit(lowercase__		)
            .repartition(1		)
            .mapInArrow(lowercase__     ,     '''batch_bytes: long'''		)
            .agg(pyspark.sql.functions.sum('''batch_bytes'''		).alias('''sample_bytes'''		)		)
            .collect()[0]
            .sample_bytes
            / sample_num_rows
        )
        _snake_case		:	Optional[int]					     =     approx_bytes_per_row * df_num_rows
        if approx_total_size > max_shard_size:
              # Make sure there is at least one row per partition.
              _snake_case		:	Union[str, Any]					     =     min(lowercase__     ,     int(approx_total_size / max_shard_size		)		)
              _snake_case		:	Optional[int]					     =     self.df.repartition(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						Iterable[Tuple[int, bool, Union[int, tuple]]]:






        """simple docstring"""





        import pyspark

        _snake_case		:	int					     =     ParquetWriter if file_format == '''parquet''' else ArrowWriter
        _snake_case		:	Optional[Any]					     =     os.path.join(self._working_dir     ,     os.path.basename(lowercase__		)		) if self._working_dir else fpath
        _snake_case		:	int					     =     file_format == '''parquet'''

        # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
        # pickling the SparkContext.
        _snake_case		:	Tuple					     =     self.config.features
        _snake_case		:	Dict					     =     self._writer_batch_size
        _snake_case		:	Dict					     =     self._fs.storage_options

        def write_arrow(lowercase__		):
              # Within the same SparkContext, no two task attempts will share the same attempt ID.
              _snake_case		:	int					     =     pyspark.TaskContext().taskAttemptId()
              _snake_case		:	List[str]					     =     next(lowercase__     ,     lowercase__		)
              if first_batch is None:
                    # Some partitions might not receive any data.
                    return pa.RecordBatch.from_arrays(
                        [[task_id], [0], [0]]     ,     names=['''task_id''', '''num_examples''', '''num_bytes''']     ,     )
              _snake_case		:	List[str]					     =     0
              _snake_case		:	str					     =     writer_class(
                  features=lowercase__     ,     path=working_fpath.replace('''SSSSS'''     ,     F'''{shard_id:05d}'''		).replace('''TTTTT'''     ,     F'''{task_id:05d}'''		)     ,     writer_batch_size=lowercase__     ,     storage_options=lowercase__     ,     embed_local_files=lowercase__     ,     )
              _snake_case		:	Optional[int]					     =     pa.Table.from_batches([first_batch]		)
              writer.write_table(lowercase__		)
              for batch in it:
                    if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                          _snake_case     ,							_snake_case		:	Any					     =     writer.finalize()
                          writer.close()
                          yield pa.RecordBatch.from_arrays(
                              [[task_id], [num_examples], [num_bytes]]     ,     names=['''task_id''', '''num_examples''', '''num_bytes''']     ,     )
                          shard_id += 1
                          _snake_case		:	Tuple					     =     writer_class(
                              features=writer._features     ,     path=working_fpath.replace('''SSSSS'''     ,     F'''{shard_id:05d}'''		).replace('''TTTTT'''     ,     F'''{task_id:05d}'''		)     ,     writer_batch_size=lowercase__     ,     storage_options=lowercase__     ,     embed_local_files=lowercase__     ,     )
                    _snake_case		:	List[Any]					     =     pa.Table.from_batches([batch]		)
                    writer.write_table(lowercase__		)

              if writer._num_bytes > 0:
                    _snake_case     ,							_snake_case		:	List[str]					     =     writer.finalize()
                    writer.close()
                    yield pa.RecordBatch.from_arrays(
                        [[task_id], [num_examples], [num_bytes]]     ,     names=['''task_id''', '''num_examples''', '''num_bytes''']     ,     )

              if working_fpath != fpath:
                    for file in os.listdir(os.path.dirname(lowercase__		)		):
                          _snake_case		:	Any					     =     os.path.join(os.path.dirname(lowercase__		)     ,     os.path.basename(lowercase__		)		)
                          shutil.move(lowercase__     ,     lowercase__		)

        _snake_case		:	Dict					     =     (
            self.df.mapInArrow(lowercase__     ,     '''task_id: long, num_examples: long, num_bytes: long'''		)
            .groupBy('''task_id'''		)
            .agg(
                pyspark.sql.functions.sum('''num_examples'''		).alias('''total_num_examples'''		)     ,     pyspark.sql.functions.sum('''num_bytes'''		).alias('''total_num_bytes'''		)     ,     pyspark.sql.functions.count('''num_bytes'''		).alias('''num_shards'''		)     ,     pyspark.sql.functions.collect_list('''num_examples'''		).alias('''shard_lengths'''		)     ,     )
            .collect()
        )
        for row in stats:
              yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = "arrow"     ,     lowercase__ = None     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						Optional[int]:






        """simple docstring"""





        self._validate_cache_dir()

        _snake_case		:	int					     =     convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
        self._repartition_df_if_needed(lowercase__		)
        _snake_case		:	Any					     =     not is_remote_filesystem(self._fs		)
        _snake_case		:	str					     =     os.path.join if is_local else posixpath.join

        _snake_case		:	int					     =     '''-TTTTT-SSSSS-of-NNNNN'''
        _snake_case		:	Dict					     =     F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
        _snake_case		:	Optional[int]					     =     path_join(self._output_dir     ,     lowercase__		)

        _snake_case		:	int					     =     0
        _snake_case		:	Tuple					     =     0
        _snake_case		:	List[str]					     =     0
        _snake_case		:	Optional[Any]					     =     []
        _snake_case		:	Optional[int]					     =     []

        for task_id, content in self._prepare_split_single(lowercase__     ,     lowercase__     ,     lowercase__		):
              (
                  (
                  _snake_case
              )     ,							(
                  _snake_case
              )     ,							(
                  _snake_case
              )     ,							(
                  _snake_case
              )     ,							
              )		:	Union[str, Any]					     =     content
              if num_bytes > 0:
                    total_num_examples += num_examples
                    total_num_bytes += num_bytes
                    total_shards += num_shards
                    task_id_and_num_shards.append((task_id, num_shards)		)
                    all_shard_lengths.extend(lowercase__		)

        _snake_case		:	Dict					     =     total_num_examples
        _snake_case		:	List[Any]					     =     total_num_bytes

        # should rename everything at the end
        logger.debug(F'''Renaming {total_shards} shards.'''		)
        if total_shards > 1:
              _snake_case		:	Dict					     =     all_shard_lengths

              # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
              # pickling error due to pickling the SparkContext.
              _snake_case		:	List[Any]					     =     self._fs

              # use the -SSSSS-of-NNNNN pattern
              def _rename_shard(
                  lowercase__     ,     lowercase__     ,     lowercase__     ,     ):
                    rename(
                        lowercase__     ,     fpath.replace('''SSSSS'''     ,     F'''{shard_id:05d}'''		).replace('''TTTTT'''     ,     F'''{task_id:05d}'''		)     ,     fpath.replace('''TTTTT-SSSSS'''     ,     F'''{global_shard_id:05d}'''		).replace('''NNNNN'''     ,     F'''{total_shards:05d}'''		)     ,     )

              _snake_case		:	int					     =     []
              _snake_case		:	Dict					     =     0
              for i in range(len(lowercase__		)		):
                    _snake_case     ,							_snake_case		:	Union[str, Any]					     =     task_id_and_num_shards[i]
                    for shard_id in range(lowercase__		):
                          args.append([task_id, shard_id, global_shard_id]		)
                          global_shard_id += 1
              self._spark.sparkContext.parallelize(lowercase__     ,     len(lowercase__		)		).map(lambda lowercase__		: _rename_shard(*lowercase__		)		).collect()
        else:
              # don't use any pattern
              _snake_case		:	Any					     =     0
              _snake_case		:	int					     =     task_id_and_num_shards[0][0]
              self._rename(
                  fpath.replace('''SSSSS'''     ,     F'''{shard_id:05d}'''		).replace('''TTTTT'''     ,     F'''{task_id:05d}'''		)     ,     fpath.replace(lowercase__     ,     ''''''		)     ,     )







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     )		->						SparkExamplesIterable:






        """simple docstring"""





        return SparkExamplesIterable(self.df		)









'''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


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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      if os.path.exists(lowerCAmelCase_	):
            if os.path.exists(os.path.join(lowerCAmelCase_			,      '''config.json'''	)	) and os.path.isfile(
                os.path.join(lowerCAmelCase_			,      '''config.json'''	)	):
                  os.remove(os.path.join(lowerCAmelCase_			,      '''config.json'''	)	)
            if os.path.exists(os.path.join(lowerCAmelCase_			,      '''pytorch_model.bin'''	)	) and os.path.isfile(
                os.path.join(lowerCAmelCase_			,      '''pytorch_model.bin'''	)	):
                  os.remove(os.path.join(lowerCAmelCase_			,      '''pytorch_model.bin'''	)	)
      else:
            os.makedirs(lowerCAmelCase_	)
      model.save_pretrained(lowerCAmelCase_	)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=False	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     2
      if unlogit:
            _snake_case		:	Any					     =     torch.pow(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Union[str, Any]					     =     p * torch.log(lowerCAmelCase_	)
      _snake_case		:	Optional[Any]					     =     0
      return -plogp.sum(dim=-1	)




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      logger.info('''lv, h >\t''' + '''\t'''.join(f'''{x + 1}''' for x in range(len(lowerCAmelCase_	)	)	)	)
      for row in range(len(lowerCAmelCase_	)	):
            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    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=True			,      lowerCAmelCase_=True			,      lowerCAmelCase_=None			,      lowerCAmelCase_=False	):





      """simple docstring"""



      _snake_case     ,							_snake_case		:	Optional[int]					     =     model.config.num_hidden_layers, model.config.num_attention_heads
      _snake_case		:	Tuple					     =     torch.zeros(lowerCAmelCase_			,      lowerCAmelCase_	).to(args.device	)
      _snake_case		:	Union[str, Any]					     =     torch.zeros(lowerCAmelCase_			,      lowerCAmelCase_	).to(args.device	)

      if head_mask is None:
            _snake_case		:	int					     =     torch.ones(lowerCAmelCase_			,      lowerCAmelCase_	).to(args.device	)

      head_mask.requires_grad_(requires_grad=lowerCAmelCase_	)
      # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
      if actually_pruned:
            _snake_case		:	Dict					     =     None

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

            # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
            _snake_case		:	Any					     =     model(lowerCAmelCase_			,      labels=lowerCAmelCase_			,      head_mask=lowerCAmelCase_	)
            #  (loss), lm_logits, presents, (all hidden_states), (attentions)
            _snake_case     ,							_snake_case     ,							_snake_case		:	List[Any]					     =     (
                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(lowerCAmelCase_	):
                        _snake_case		:	Union[str, Any]					     =     entropy(attn.detach()			,      lowerCAmelCase_	)
                        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(lowerCAmelCase_	).float().detach().sum().data

      # Normalize
      attn_entropy /= tot_tokens
      head_importance /= tot_tokens
      # Layerwise importance normalization
      if not args.dont_normalize_importance_by_layer:
            _snake_case		:	Any					     =     2
            _snake_case		:	List[str]					     =     torch.pow(torch.pow(lowerCAmelCase_			,      lowerCAmelCase_	).sum(-1	)			,      1 / exponent	)
            head_importance /= norm_by_layer.unsqueeze(-1	) + 1E-20

      if not args.dont_normalize_global_importance:
            _snake_case		:	Optional[int]					     =     (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

      # Print matrices
      if compute_entropy:
            logger.info('''Attention entropies'''	)
            print_ad_tensor(lowerCAmelCase_	)
      if compute_importance:
            logger.info('''Head importance scores'''	)
            print_ad_tensor(lowerCAmelCase_	)
      logger.info('''Head ranked by importance scores'''	)
      _snake_case		:	str					     =     torch.zeros(head_importance.numel()			,      dtype=torch.long			,      device=args.device	)
      _snake_case		:	List[Any]					     =     torch.arange(
          head_importance.numel()			,      device=args.device	)
      _snake_case		:	List[Any]					     =     head_ranks.view_as(lowerCAmelCase_	)
      print_ad_tensor(lowerCAmelCase_	)
      return attn_entropy, head_importance, total_loss




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case     ,							_snake_case     ,							_snake_case		:	str					     =     compute_heads_importance(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      compute_entropy=lowerCAmelCase_	)
      _snake_case		:	Optional[Any]					     =     1 / loss  # instead of downsteam score use the LM loss
      logger.info('''Pruning: original score: %f, threshold: %f'''			,      lowerCAmelCase_			,      original_score * args.masking_threshold	)

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

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

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

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

            # Compute metric and head importance again
            _snake_case     ,							_snake_case     ,							_snake_case		:	Any					     =     compute_heads_importance(
                lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      compute_entropy=lowerCAmelCase_			,      head_mask=lowerCAmelCase_	)
            _snake_case		:	int					     =     1 / loss
            logger.info(
                '''Masking: current score: %f, remaining heads %d (%.1f percents)'''			,      lowerCAmelCase_			,      new_head_mask.sum()			,      new_head_mask.sum() / new_head_mask.numel() * 100			,      )

      logger.info('''Final head mask'''	)
      print_ad_tensor(lowerCAmelCase_	)
      np.save(os.path.join(args.output_dir			,      '''head_mask.npy'''	)			,      head_mask.detach().cpu().numpy()	)

      return head_mask




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     datetime.now()
      _snake_case     ,							_snake_case     ,							_snake_case		:	Union[str, Any]					     =     compute_heads_importance(
          lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      compute_entropy=lowerCAmelCase_			,      compute_importance=lowerCAmelCase_			,      head_mask=lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     1 / loss
      _snake_case		:	Dict					     =     datetime.now() - before_time

      _snake_case		:	List[Any]					     =     sum(p.numel() for p in model.parameters()	)
      _snake_case		:	int					     =     {
          layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCAmelCase_	)	)
      }

      for k, v in heads_to_prune.items():
            if isinstance(lowerCAmelCase_			,      lowerCAmelCase_	):
                  _snake_case		:	Union[str, Any]					     =     [
                      v,
                  ]

      assert sum(len(lowerCAmelCase_	) for h in heads_to_prune.values()	) == (1 - head_mask.long()).sum().item()
      model.prune_heads(lowerCAmelCase_	)
      _snake_case		:	List[str]					     =     sum(p.numel() for p in model.parameters()	)

      _snake_case		:	int					     =     datetime.now()
      _snake_case     ,							_snake_case     ,							_snake_case		:	Optional[Any]					     =     compute_heads_importance(
          lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      compute_entropy=lowerCAmelCase_			,      compute_importance=lowerCAmelCase_			,      head_mask=lowerCAmelCase_			,      actually_pruned=lowerCAmelCase_			,      )

      _snake_case		:	Optional[int]					     =     1 / loss
      _snake_case		:	Dict					     =     datetime.now() - before_time

      logger.info(
          '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''			,      lowerCAmelCase_			,      lowerCAmelCase_			,      pruned_num_params / original_num_params * 100			,      )
      logger.info('''Pruning: score with masking: %f score with pruning: %f'''			,      lowerCAmelCase_			,      lowerCAmelCase_	)
      logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''			,      original_time / new_time * 100	)
      save_model(lowerCAmelCase_			,      args.output_dir	)




def    _a    (							):





      """simple docstring"""



      _snake_case		:	List[str]					     =     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--data_dir'''			,      default=lowerCAmelCase_			,      type=lowerCAmelCase_			,      required=lowerCAmelCase_			,      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=lowerCAmelCase_			,      type=lowerCAmelCase_			,      required=lowerCAmelCase_			,      help='''Path to pretrained model or model identifier from huggingface.co/models'''			,      )
      parser.add_argument(
          '''--output_dir'''			,      default=lowerCAmelCase_			,      type=lowerCAmelCase_			,      required=lowerCAmelCase_			,      help='''The output directory where the model predictions and checkpoints will be written.'''			,      )

      # Other parameters
      parser.add_argument(
          '''--config_name'''			,      default=''''''			,      type=lowerCAmelCase_			,      help='''Pretrained config name or path if not the same as model_name_or_path'''			,      )
      parser.add_argument(
          '''--tokenizer_name'''			,      default=''''''			,      type=lowerCAmelCase_			,      help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''			,      )
      parser.add_argument(
          '''--cache_dir'''			,      default=lowerCAmelCase_			,      type=lowerCAmelCase_			,      help='''Where do you want to store the pre-trained models downloaded from s3'''			,      )
      parser.add_argument(
          '''--data_subset'''			,      type=lowerCAmelCase_			,      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=lowerCAmelCase_			,      help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''			,      )
      parser.add_argument(
          '''--masking_amount'''			,      default=0.1			,      type=lowerCAmelCase_			,      help='''Amount to heads to masking at each masking step.'''	)
      parser.add_argument('''--metric_name'''			,      default='''acc'''			,      type=lowerCAmelCase_			,      help='''Metric to use for head masking.'''	)

      parser.add_argument(
          '''--max_seq_length'''			,      default=128			,      type=lowerCAmelCase_			,      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=lowerCAmelCase_			,      help='''Batch size.'''	)

      parser.add_argument('''--seed'''			,      type=lowerCAmelCase_			,      default=42	)
      parser.add_argument('''--local_rank'''			,      type=lowerCAmelCase_			,      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=lowerCAmelCase_			,      default=''''''			,      help='''Can be used for distant debugging.'''	)
      parser.add_argument('''--server_port'''			,      type=lowerCAmelCase_			,      default=''''''			,      help='''Can be used for distant debugging.'''	)
      _snake_case		:	Optional[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=lowerCAmelCase_	)
            ptvsd.wait_for_attach()

      # Setup devices and distributed training
      if args.local_rank == -1 or args.no_cuda:
            _snake_case		:	str					     =     torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''	)
            _snake_case		:	Optional[Any]					     =     0 if args.no_cuda else torch.cuda.device_count()
      else:
            torch.cuda.set_device(args.local_rank	)
            _snake_case		:	List[str]					     =     torch.device('''cuda'''			,      args.local_rank	)
            _snake_case		:	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	)	)	)

      _snake_case		:	Optional[Any]					     =     GPTaLMHeadModel.from_pretrained(args.model_name_or_path	)

      # Distributed and parallel training
      model.to(args.device	)
      if args.local_rank != -1:
            _snake_case		:	Optional[int]					     =     nn.parallel.DistributedDataParallel(
                lowerCAmelCase_			,      device_ids=[args.local_rank]			,      output_device=args.local_rank			,      find_unused_parameters=lowerCAmelCase_	)
      elif args.n_gpu > 1:
            _snake_case		:	List[Any]					     =     nn.DataParallel(lowerCAmelCase_	)

      # Print/save training arguments
      os.makedirs(args.output_dir			,      exist_ok=lowerCAmelCase_	)
      torch.save(lowerCAmelCase_			,      os.path.join(args.output_dir			,      '''run_args.bin'''	)	)
      logger.info('''Training/evaluation parameters %s'''			,      lowerCAmelCase_	)

      # Prepare dataset
      _snake_case		:	Dict					     =     np.concatenate(
          [
              np.loadtxt(args.data_dir			,      dtype=np.intaa	),
          ]	)
      _snake_case		:	int					     =     (torch.from_numpy(lowerCAmelCase_	),)
      _snake_case		:	Tuple					     =     TensorDataset(*lowerCAmelCase_	)
      _snake_case		:	List[str]					     =     RandomSampler(lowerCAmelCase_	)
      _snake_case		:	Dict					     =     DataLoader(lowerCAmelCase_			,      sampler=lowerCAmelCase_			,      batch_size=args.batch_size	)

      # Compute head entropy and importance score
      compute_heads_importance(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)

      # Try head masking (set heads to zero until the score goes under a threshole)
      # and head pruning (remove masked heads and see the effect on the network)
      if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
            _snake_case		:	Optional[int]					     =     mask_heads(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            prune_heads(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)


if __name__ == "__main__":
   main()


