



'''simple docstring'''





import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.generation import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )






@require_flax
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self					,  A					,  A			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    jnp.ones((batch_size, length)			) / length
      return scores



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    None
      _a           =    20

      _a           =    self._get_uniform_logits(batch_size=2					,  length=A			)

      # tweak scores to not be uniform anymore
      _a           =    scores.at[1, 5].set((1 / length) + 0.1			)  # peak, 1st batch
      _a           =    scores.at[1, 10].set((1 / length) - 0.4			)  # valley, 1st batch

      # compute softmax
      _a           =    jax.nn.softmax(A					,  axis=-1			)

      _a           =    FlaxTemperatureLogitsWarper(temperature=0.5			)
      _a           =    FlaxTemperatureLogitsWarper(temperature=1.3			)

      _a           =    jax.nn.softmax(temp_dist_warper_sharper(A					,  scores.copy()					,  cur_len=A			)					,  axis=-1			)
      _a           =    jax.nn.softmax(temp_dist_warper_smoother(A					,  scores.copy()					,  cur_len=A			)					,  axis=-1			)

      # uniform distribution stays uniform
      self.assertTrue(jnp.allclose(probs[0, :]					,  warped_prob_sharp[0, :]					,  atol=1E-3			)			)
      self.assertTrue(jnp.allclose(probs[0, :]					,  warped_prob_smooth[0, :]					,  atol=1E-3			)			)

      # sharp peaks get higher, valleys get lower
      self.assertLess(probs[1, :].max()					,  warped_prob_sharp[1, :].max()			)
      self.assertGreater(probs[1, :].min()					,  warped_prob_sharp[1, :].min()			)

      # smooth peaks get lower, valleys get higher
      self.assertGreater(probs[1, :].max()					,  warped_prob_smooth[1, :].max()			)
      self.assertLess(probs[1, :].min()					,  warped_prob_smooth[1, :].min()			)



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


      """simple docstring"""



      _a           =    None
      _a           =    10
      _a           =    2

      # create ramp distribution
      _a           =    np.broadcast_to(np.arange(A			)[None, :]					,  (batch_size, vocab_size)			).copy()
      _a           =    ramp_logits[1:, : vocab_size // 2] + vocab_size

      _a           =    FlaxTopKLogitsWarper(3			)

      _a           =    top_k_warp(A					,  A					,  cur_len=A			)

      # check that correct tokens are filtered
      self.assertListEqual(jnp.isinf(scores[0]			).tolist()					,  7 * [True] + 3 * [False]			)
      self.assertListEqual(jnp.isinf(scores[1]			).tolist()					,  2 * [True] + 3 * [False] + 5 * [True]			)

      # check special case
      _a           =    5
      _a           =    FlaxTopKLogitsWarper(top_k=1					,  filter_value=0.0					,  min_tokens_to_keep=3			)

      _a           =    np.broadcast_to(np.arange(A			)[None, :]					,  (batch_size, length)			).copy()
      _a           =    top_k_warp_safety_check(A					,  A					,  cur_len=A			)

      # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
      self.assertListEqual((scores == 0.0).sum(axis=-1			).tolist()					,  [2, 2]			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    None
      _a           =    10
      _a           =    2

      # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
      _a           =    np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]			)			)

      _a           =    FlaxTopPLogitsWarper(0.8			)
      _a           =    np.exp(top_p_warp(A					,  A					,  cur_len=A			)			)

      # dist should be filtered to keep min num values so that sum is >= top_p
      # exp (-inf) => 0
      _a           =    np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]			)
      self.assertTrue(np.allclose(A					,  A					,  atol=1E-3			)			)

      # check edge cases with negative and extreme logits
      _a           =    np.broadcast_to(np.arange(A			)[None, :]					,  (batch_size, vocab_size)			).copy() - (
          vocab_size // 2
      )

      # make ramp_logits more extreme
      _a           =    ramp_logits[1] * 100.0

      # make sure at least 2 tokens are kept
      _a           =    FlaxTopPLogitsWarper(0.9					,  min_tokens_to_keep=2					,  filter_value=0.0			)
      _a           =    top_p_warp(A					,  A					,  cur_len=A			)

      # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
      self.assertListEqual((filtered_dist != 0.0).sum(axis=-1			).tolist()					,  [3, 2]			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    20
      _a           =    4
      _a           =    0

      _a           =    FlaxMinLengthLogitsProcessor(min_length=10					,  eos_token_id=A			)

      # check that min length is applied at length 5
      _a           =    ids_tensor((batch_size, 20)					,  vocab_size=20			)
      _a           =    5
      _a           =    self._get_uniform_logits(A					,  A			)
      _a           =    min_dist_processor(A					,  A					,  cur_len=A			)
      self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()					,  4 * [-float('''inf'''			)]			)

      # check that min length is not applied anymore at length 15
      _a           =    self._get_uniform_logits(A					,  A			)
      _a           =    15
      _a           =    min_dist_processor(A					,  A					,  cur_len=A			)
      self.assertFalse(jnp.isinf(A			).any()			)



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


      """simple docstring"""



      _a           =    20
      _a           =    4
      _a           =    0

      _a           =    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A			)

      # check that all scores are -inf except the bos_token_id score
      _a           =    ids_tensor((batch_size, 1)					,  vocab_size=20			)
      _a           =    1
      _a           =    self._get_uniform_logits(A					,  A			)
      _a           =    logits_processor(A					,  A					,  cur_len=A			)
      self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]			).all()			)
      self.assertListEqual(scores[:, bos_token_id].tolist()					,  4 * [0]			)  # score for bos_token_id shold be zero

      # check that bos_token_id is not forced if current length is greater than 1
      _a           =    3
      _a           =    self._get_uniform_logits(A					,  A			)
      _a           =    logits_processor(A					,  A					,  cur_len=A			)
      self.assertFalse(jnp.isinf(A			).any()			)



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


      """simple docstring"""



      _a           =    20
      _a           =    4
      _a           =    0
      _a           =    5

      _a           =    FlaxForcedEOSTokenLogitsProcessor(max_length=A					,  eos_token_id=A			)

      # check that all scores are -inf except the eos_token_id when max_length is reached
      _a           =    ids_tensor((batch_size, 4)					,  vocab_size=20			)
      _a           =    4
      _a           =    self._get_uniform_logits(A					,  A			)
      _a           =    logits_processor(A					,  A					,  cur_len=A			)
      self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]			).all()			)
      self.assertListEqual(scores[:, eos_token_id].tolist()					,  4 * [0]			)  # score for eos_token_id should be zero

      # check that eos_token_id is not forced if max_length is not reached
      _a           =    3
      _a           =    self._get_uniform_logits(A					,  A			)
      _a           =    logits_processor(A					,  A					,  cur_len=A			)
      self.assertFalse(jnp.isinf(A			).any()			)



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    4
      _a           =    10
      _a           =    15
      _a           =    2
      _a           =    1
      _a           =    15

      # dummy input_ids and scores
      _a           =    ids_tensor((batch_size, sequence_length)					,  A			)
      _a           =    input_ids.copy()

      _a           =    self._get_uniform_logits(A					,  A			)
      _a           =    scores.copy()

      # instantiate all dist processors
      _a           =    FlaxTemperatureLogitsWarper(temperature=0.5			)
      _a           =    FlaxTopKLogitsWarper(3			)
      _a           =    FlaxTopPLogitsWarper(0.8			)

      # instantiate all logits processors
      _a           =    FlaxMinLengthLogitsProcessor(min_length=10					,  eos_token_id=A			)
      _a           =    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A			)
      _a           =    FlaxForcedEOSTokenLogitsProcessor(max_length=A					,  eos_token_id=A			)

      _a           =    10

      # no processor list
      _a           =    temp_dist_warp(A					,  A					,  cur_len=A			)
      _a           =    top_k_warp(A					,  A					,  cur_len=A			)
      _a           =    top_p_warp(A					,  A					,  cur_len=A			)
      _a           =    min_dist_proc(A					,  A					,  cur_len=A			)
      _a           =    bos_dist_proc(A					,  A					,  cur_len=A			)
      _a           =    eos_dist_proc(A					,  A					,  cur_len=A			)

      # with processor list
      _a           =    FlaxLogitsProcessorList(
          [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]			)
      _a           =    processor(A					,  A					,  cur_len=A			)

      # scores should be equal
      self.assertTrue(jnp.allclose(A					,  A					,  atol=1E-3			)			)

      # input_ids should never be changed
      self.assertListEqual(input_ids.tolist()					,  input_ids_comp.tolist()			)



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


      """simple docstring"""



      _a           =    4
      _a           =    10
      _a           =    15
      _a           =    2
      _a           =    1
      _a           =    15

      # dummy input_ids and scores
      _a           =    ids_tensor((batch_size, sequence_length)					,  A			)
      _a           =    input_ids.copy()

      _a           =    self._get_uniform_logits(A					,  A			)
      _a           =    scores.copy()

      # instantiate all dist processors
      _a           =    FlaxTemperatureLogitsWarper(temperature=0.5			)
      _a           =    FlaxTopKLogitsWarper(3			)
      _a           =    FlaxTopPLogitsWarper(0.8			)

      # instantiate all logits processors
      _a           =    FlaxMinLengthLogitsProcessor(min_length=10					,  eos_token_id=A			)
      _a           =    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A			)
      _a           =    FlaxForcedEOSTokenLogitsProcessor(max_length=A					,  eos_token_id=A			)

      _a           =    10

      # no processor list
      def run_no_processor_list(A					,  A					,  A			):
           _a           =    temp_dist_warp(A					,  A					,  cur_len=A			)
           _a           =    top_k_warp(A					,  A					,  cur_len=A			)
           _a           =    top_p_warp(A					,  A					,  cur_len=A			)
           _a           =    min_dist_proc(A					,  A					,  cur_len=A			)
           _a           =    bos_dist_proc(A					,  A					,  cur_len=A			)
           _a           =    eos_dist_proc(A					,  A					,  cur_len=A			)
           return scores

      # with processor list
      def run_processor_list(A					,  A					,  A			):
           _a           =    FlaxLogitsProcessorList(
               [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]			)
           _a           =    processor(A					,  A					,  cur_len=A			)
           return scores

      _a           =    jax.jit(A			)
      _a           =    jax.jit(A			)

      _a           =    jitted_run_no_processor_list(A					,  A					,  A			)
      _a           =    jitted_run_processor_list(A					,  A					,  A			)

      # scores should be equal
      self.assertTrue(jnp.allclose(A					,  A					,  atol=1E-3			)			)

      # input_ids should never be changed
      self.assertListEqual(input_ids.tolist()					,  input_ids_comp.tolist()			)








'''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()



