




"""simple docstring"""

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a							     =      logging.get_logger(__name__)

a							     =      {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
a							     =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Tuple    ,  _snake_case							: Optional[int]    ,  _snake_case							: int    ,  _snake_case							: Union[str, Any]							)   -> List[str]:






      '''simple docstring'''


      for attribute in key.split('.'							):
            if is_finetuned:
                  if attribute in ["quantizer", "project_q", "project_hid"]:
                        # those layers are only relevant for pretraining and should be dropped
                        return

                  if attribute == "ctc_proj":
                        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                        _A        =       'lm_head'

            _A        =       getattr(_snake_case    ,  _snake_case							)

      if weight_type is not None:
            _A        =       getattr(_snake_case    ,  _snake_case							).shape
      else:
            _A        =       hf_pointer.shape

      assert hf_shape == value.shape, (
          F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
          F''' {value.shape} for {full_name}'''
      )

      if weight_type == "weight":
            _A        =       value
      elif weight_type == "weight_g":
            _A        =       value
      elif weight_type == "weight_v":
            _A        =       value
      elif weight_type == "bias":
            _A        =       value
      else:
            _A        =       value

      logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: int    ,  _snake_case							: Optional[Any]							)   -> Any:






      '''simple docstring'''


      _A        =       []
      _A        =       fairseq_model.state_dict()

      _A        =       hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            _A        =       False
            if "conv_layers" in name:
                  load_conv_layer(
                      _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  hf_model.config.feat_extract_norm == 'group'    ,  )
                  _A        =       True
            else:
                  for key, mapped_key in MAPPING.items():
                        _A        =       'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
                              _A        =       True
                              if "*" in mapped_key:
                                    _A        =       name.split(_snake_case							)[0].split('.'							)[-2]
                                    _A        =       mapped_key.replace('*'    ,  _snake_case							)
                              if "weight_g" in name:
                                    _A        =       'weight_g'
                              elif "weight_v" in name:
                                    _A        =       'weight_v'
                              elif "bias" in name:
                                    _A        =       'bias'
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _A        =       'weight'
                              else:
                                    _A        =       None
                              set_recursively(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        continue
            if not is_used:
                  unused_weights.append(_snake_case							)

      logger.warning(F'''Unused weights: {unused_weights}'''							)






def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: List[str]    ,  _snake_case							: Optional[int]    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]							)   -> List[Any]:






      '''simple docstring'''


      _A        =       full_name.split('conv_layers.'							)[-1]
      _A        =       name.split('.'							)
      _A        =       int(items[0]							)
      _A        =       int(items[1]							)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      F'''{full_name} has size {value.shape}, but'''
                      F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                  )
                  _A        =       value
                  logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      F'''{full_name} has size {value.shape}, but'''
                      F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                  )
                  _A        =       value
                  logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                      " found."
                  )
                  _A        =       value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      F'''{full_name} has size {value.shape}, but'''
                      F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                  )
                  _A        =       value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
      else:
            unused_weights.append(_snake_case							)






@torch.no_grad()
def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: int    ,  _snake_case							: Optional[Any]=None    ,  _snake_case							: Tuple=None    ,  _snake_case							: int=True							)   -> Dict:






      '''simple docstring'''


      if config_path is not None:
            _A        =       UniSpeechConfig.from_pretrained(_snake_case							)
      else:
            _A        =       UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  _A        =       Dictionary.load_from_json(_snake_case							)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _A        =       target_dict.pad_index
                  _A        =       target_dict.bos_index
                  _A        =       target_dict.eos_index
                  _A        =       len(target_dict.symbols							)
                  _A        =       os.path.join(_snake_case    ,  'vocab.json'							)
                  if not os.path.isdir(_snake_case							):
                        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_snake_case							)							)
                        return
                  os.makedirs(_snake_case    ,  exist_ok=_snake_case							)
                  _A        =       target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _A        =       42
                  _A        =       43
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as vocab_handle:
                        json.dump(_snake_case    ,  _snake_case							)
                  _A        =       WavaVecaPhonemeCTCTokenizer(
                      _snake_case    ,  unk_token=target_dict.unk_word    ,  pad_token=target_dict.pad_word    ,  bos_token=target_dict.bos_word    ,  eos_token=target_dict.eos_word    ,  word_delimiter_token='|'    ,  do_lower_case=_snake_case    ,  )
                  _A        =       True if config.feat_extract_norm == 'layer' else False
                  _A        =       WavaVecaFeatureExtractor(
                      feature_size=1    ,  sampling_rate=1_60_00    ,  padding_value=0    ,  do_normalize=_snake_case    ,  return_attention_mask=_snake_case    ,  )
                  _A        =       WavaVecaProcessor(feature_extractor=_snake_case    ,  tokenizer=_snake_case							)
                  processor.save_pretrained(_snake_case							)

            _A        =       UniSpeechForCTC(_snake_case							)
      else:
            _A        =       UniSpeechForPreTraining(_snake_case							)

      if is_finetuned:
            _A       ,   _A       ,   _A        =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]    ,  arg_overrides={'data': '/'.join(dict_path.split('/'							)[:-1]							), 'w2v_path': checkpoint_path}							)
      else:
            _A       ,   _A       ,   _A        =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

      _A        =       model[0].eval()

      recursively_load_weights(_snake_case    ,  _snake_case    ,  _snake_case							)

      hf_unispeech.save_pretrained(_snake_case							)


if __name__ == "__main__":
       a							     =      argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
       parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       parser.add_argument(
           '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
       )
       a							     =      parser.parse_args()
       convert_unispeech_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )







"""simple docstring"""

import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


a         =      logging.getLogger(__name__)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Tuple							)   -> Any:






      '''simple docstring'''


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






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: Optional[int]=False							)   -> Tuple:






      '''simple docstring'''


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






def 						_snake_case					( _snake_case							: Optional[Any]							)   -> int:






      '''simple docstring'''


      logger.info('lv, h >\t' + '\t'.join(F'''{x + 1}''' for x in range(len(_snake_case							)							)							)							)
      for row in range(len(_snake_case							)							):
            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 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: Dict    ,  _snake_case							: List[str]    ,  _snake_case							: Union[str, Any]=True    ,  _snake_case							: Any=True    ,  _snake_case							: List[str]=None    ,  _snake_case							: List[Any]=False							)   -> int:






      '''simple docstring'''


      _A       ,   _A        =       model.config.num_hidden_layers, model.config.num_attention_heads
      _A        =       torch.zeros(_snake_case    ,  _snake_case							).to(args.device							)
      _A        =       torch.zeros(_snake_case    ,  _snake_case							).to(args.device							)

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

      head_mask.requires_grad_(requires_grad=_snake_case							)
      # 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(_snake_case    ,  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(_snake_case    ,  labels=_snake_case    ,  head_mask=_snake_case							)
            #  (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(_snake_case							):
                        _A        =       entropy(attn.detach()    ,  _snake_case							)
                        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(_snake_case							).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(_snake_case    ,  _snake_case							).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(_snake_case							)
      if compute_importance:
            logger.info('Head importance scores'							)
            print_ad_tensor(_snake_case							)
      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(_snake_case							)
      print_ad_tensor(_snake_case							)
      return attn_entropy, head_importance, total_loss






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: Tuple    ,  _snake_case							: List[Any]							)   -> List[str]:






      '''simple docstring'''


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

      _A        =       torch.ones_like(_snake_case							)
      _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(_snake_case							) <= 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(_snake_case							)
            _A        =       new_head_mask.clone().detach()
            print_ad_tensor(_snake_case							)

            # Compute metric and head importance again
            _A       ,   _A       ,   _A        =       compute_heads_importance(
                _snake_case    ,  _snake_case    ,  _snake_case    ,  compute_entropy=_snake_case    ,  head_mask=_snake_case							)
            _A        =       1 / loss
            logger.info(
                'Masking: current score: %f, remaining heads %d (%.1f percents)'    ,  _snake_case    ,  new_head_mask.sum()    ,  new_head_mask.sum() / new_head_mask.numel() * 1_00    ,  )

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

      return head_mask






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: Any    ,  _snake_case							: Dict    ,  _snake_case							: Optional[Any]							)   -> Union[str, Any]:






      '''simple docstring'''


      _A        =       datetime.now()
      _A       ,   _A       ,   _A        =       compute_heads_importance(
          _snake_case    ,  _snake_case    ,  _snake_case    ,  compute_entropy=_snake_case    ,  compute_importance=_snake_case    ,  head_mask=_snake_case							)
      _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(_snake_case							)							)
      }

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

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

      _A        =       datetime.now()
      _A       ,   _A       ,   _A        =       compute_heads_importance(
          _snake_case    ,  _snake_case    ,  _snake_case    ,  compute_entropy=_snake_case    ,  compute_importance=_snake_case    ,  head_mask=_snake_case    ,  actually_pruned=_snake_case    ,  )

      _A        =       1 / loss
      _A        =       datetime.now() - before_time

      logger.info(
          'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'    ,  _snake_case    ,  _snake_case    ,  pruned_num_params / original_num_params * 1_00    ,  )
      logger.info('Pruning: score with masking: %f score with pruning: %f'    ,  _snake_case    ,  _snake_case							)
      logger.info('Pruning: speed ratio (original timing / new timing): %f percents'    ,  original_time / new_time * 1_00							)
      save_model(_snake_case    ,  args.output_dir							)






def 						_snake_case					( )   -> Dict:






      '''simple docstring'''


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

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

      parser.add_argument(
          '--max_seq_length'    ,  default=1_28    ,  type=_snake_case    ,  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=_snake_case    ,  help='Batch size.'							)

      parser.add_argument('--seed'    ,  type=_snake_case    ,  default=42							)
      parser.add_argument('--local_rank'    ,  type=_snake_case    ,  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=_snake_case    ,  default=''    ,  help='Can be used for distant debugging.'							)
      parser.add_argument('--server_port'    ,  type=_snake_case    ,  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=_snake_case							)
            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(
                _snake_case    ,  device_ids=[args.local_rank]    ,  output_device=args.local_rank    ,  find_unused_parameters=_snake_case							)
      elif args.n_gpu > 1:
            _A        =       nn.DataParallel(_snake_case							)

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

      # Prepare dataset
      _A        =       np.concatenate(
          [
              np.loadtxt(args.data_dir    ,  dtype=np.intaa							),
          ]							)
      _A        =       (torch.from_numpy(_snake_case							),)
      _A        =       TensorDataset(*_snake_case							)
      _A        =       RandomSampler(_snake_case							)
      _A        =       DataLoader(_snake_case    ,  sampler=_snake_case    ,  batch_size=args.batch_size							)

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

      # 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(_snake_case    ,  _snake_case    ,  _snake_case							)
            prune_heads(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)


if __name__ == "__main__":
       main()

