




"""simple docstring"""

import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a         =      logging.get_logger('''transformers.models.encodec''')

a         =      {
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
a         =      {
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
a         =      {
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
a         =      {
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
a         =      {
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
a         =      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a         =      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a         =      []
a         =      []






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






      '''simple docstring'''


      for attribute in key.split('.'							):
            _A        =       getattr(_snake_case    ,  _snake_case							)

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

      if hf_shape != value.shape:
            raise ValueError(
                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
      elif weight_type == "running_mean":
            _A        =       value
      elif weight_type == "running_var":
            _A        =       value
      elif weight_type == "num_batches_tracked":
            _A        =       value
      elif weight_type == "weight_ih_l0":
            _A        =       value
      elif weight_type == "weight_hh_l0":
            _A        =       value
      elif weight_type == "bias_ih_l0":
            _A        =       value
      elif weight_type == "bias_hh_l0":
            _A        =       value
      elif weight_type == "weight_ih_l1":
            _A        =       value
      elif weight_type == "weight_hh_l1":
            _A        =       value
      elif weight_type == "bias_ih_l1":
            _A        =       value
      elif weight_type == "bias_hh_l1":
            _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							: List[Any]    ,  _snake_case							: Union[str, Any]							)   -> Tuple:






      '''simple docstring'''


      for key in ignore_keys:
            if key.endswith('.*'							):
                  if name.startswith(key[:-1]							):
                        return True
            elif ".*." in key:
                  _A       ,   _A        =       key.split('.*.'							)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False






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






      '''simple docstring'''


      _A        =       []

      if model_name == "encodec_24khz" or "encodec_32khz":
            _A        =       MAPPING_24K
      elif model_name == "encodec_48khz":
            _A        =       MAPPING_48K
      else:
            raise ValueError(F'''Unsupported model: {model_name}'''							)

      for name, value in orig_dict.items():
            if should_ignore(_snake_case    ,  _snake_case							):
                  logger.info(F'''{name} was ignored'''							)
                  continue

            _A        =       False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        _A       ,   _A        =       key.split('.*.'							)
                        if prefix in name and suffix in name:
                              _A        =       suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith('embed'							) and name.endswith('embed_avg'							):
                              continue

                        _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 "weight_ih_l0" in name:
                              _A        =       'weight_ih_l0'
                        elif "weight_hh_l0" in name:
                              _A        =       'weight_hh_l0'
                        elif "bias_ih_l0" in name:
                              _A        =       'bias_ih_l0'
                        elif "bias_hh_l0" in name:
                              _A        =       'bias_hh_l0'
                        elif "weight_ih_l1" in name:
                              _A        =       'weight_ih_l1'
                        elif "weight_hh_l1" in name:
                              _A        =       'weight_hh_l1'
                        elif "bias_ih_l1" in name:
                              _A        =       'bias_ih_l1'
                        elif "bias_hh_l1" in name:
                              _A        =       'bias_hh_l1'
                        elif "bias" in name:
                              _A        =       'bias'
                        elif "weight" in name:
                              _A        =       'weight'
                        elif "running_mean" in name:
                              _A        =       'running_mean'
                        elif "running_var" in name:
                              _A        =       'running_var'
                        elif "num_batches_tracked" in name:
                              _A        =       'num_batches_tracked'
                        else:
                              _A        =       None
                        set_recursively(_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}'''							)






@torch.no_grad()
def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: List[str]    ,  _snake_case							: Optional[int]    ,  _snake_case							: str=None    ,  _snake_case							: int=None    ,  )   -> int:






      '''simple docstring'''


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

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            _A        =       [8, 5, 4, 4]
            _A        =       [2.2]
            _A        =       64
            _A        =       3_20_00
            _A        =       20_48
            _A        =       False
            _A        =       False
            _A        =       False
      elif model_name == "encodec_48khz":
            _A        =       [8, 5, 4, 2]
            _A        =       [3.0, 6.0, 12.0, 24.0]
            _A        =       4_80_00
            _A        =       2
            _A        =       False
            _A        =       'time_group_norm'
            _A        =       True
            _A        =       1.0
            _A        =       0.01
      else:
            raise ValueError(F'''Unknown model name: {model_name}'''							)

      _A        =       EncodecModel(_snake_case							)

      _A        =       EncodecFeatureExtractor(
          feature_size=config.audio_channels    ,  sampling_rate=config.sampling_rate    ,  chunk_length_s=config.chunk_length_s    ,  overlap=config.overlap    ,  )
      feature_extractor.save_pretrained(_snake_case							)

      _A        =       torch.load(_snake_case							)
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            _A        =       original_checkpoint['best_state']
      recursively_load_weights(_snake_case    ,  _snake_case    ,  _snake_case							)
      model.save_pretrained(_snake_case							)

      if repo_id:
            print('Pushing to the hub...'							)
            feature_extractor.push_to_hub(_snake_case							)
            model.push_to_hub(_snake_case							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       parser.add_argument(
           '''--model''',
           default='''encodec_24khz''',
           type=str,
           help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
       )
       parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       parser.add_argument(
           '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
       )

       a         =      parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )







"""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()

