code: 
  
  
  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, SequentialSampler, Subset
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      GlueDataset,
      default_data_collator,
      glue_compute_metrics,
      glue_output_modes,
      glue_processors,
      set_seed,
  )
  from transformers.trainer_utils import is_main_process
  
  
  __snake_case             =	logging.getLogger(__name__)
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   Dict:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = p * torch.log(_lowercase   )
       __UpperCamelCase   = 0
       return -plogp.sum(dim=-1   )
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   Union[str, Any]:
  
  
       """simple docstring"""
  
  
       logger.info('lv, h >\t' + '\t'.join(f'''{x + 1}''' for x in range(len(_lowercase   )   )   )   )
       for row in range(len(_lowercase   )   ):
            if tensor.dtype != torch.long:
                 logger.info(f'''layer {row + 1}:\t''' + '\t'.join(f'''{x:.5f}''' for x in tensor[row].cpu().data   )   )
            else:
                 logger.info(f'''layer {row + 1}:\t''' + '\t'.join(f'''{x:d}''' for x in tensor[row].cpu().data   )   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase=True				,     _lowercase=True				,     _lowercase=None				,     _lowercase=False   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = model.config.num_hidden_layers, model.config.num_attention_heads
       __UpperCamelCase   = torch.zeros(_lowercase				,     _lowercase   ).to(args.device   )
       __UpperCamelCase   = torch.zeros(_lowercase				,     _lowercase   ).to(args.device   )
  
       if head_mask is None:
            __UpperCamelCase   = torch.ones(_lowercase				,     _lowercase   ).to(args.device   )
  
       head_mask.requires_grad_(requires_grad=_lowercase   )
       # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
       if actually_pruned:
            __UpperCamelCase   = None
  
       __UpperCamelCase   = None
       __UpperCamelCase   = None
       __UpperCamelCase   = 0.0
  
       for step, inputs in enumerate(tqdm(_lowercase				,     desc='Iteration'				,     disable=args.local_rank not in [-1, 0]   )   ):
            for k, v in inputs.items():
                 __UpperCamelCase   = v.to(args.device   )
  
            # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
            __UpperCamelCase   = model(**_lowercase				,     head_mask=_lowercase   )
            __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = (
                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
  
            if compute_entropy:
                 for layer, attn in enumerate(_lowercase   ):
                      __UpperCamelCase   = entropy(attn.detach()   ) * inputs['attention_mask'].float().unsqueeze(1   )
                      attn_entropy[layer] += masked_entropy.sum(-1   ).sum(0   ).detach()
  
            if compute_importance:
                 head_importance += head_mask.grad.abs().detach()
  
            # Also store our logits/labels if we want to compute metrics afterwards
            if preds is None:
                 __UpperCamelCase   = logits.detach().cpu().numpy()
                 __UpperCamelCase   = inputs['labels'].detach().cpu().numpy()
            else:
                 __UpperCamelCase   = np.append(_lowercase				,     logits.detach().cpu().numpy()				,     axis=0   )
                 __UpperCamelCase   = np.append(_lowercase				,     inputs['labels'].detach().cpu().numpy()				,     axis=0   )
  
            tot_tokens += inputs["attention_mask"].float().detach().sum().data
  
       # Normalize
       attn_entropy /= tot_tokens
       head_importance /= tot_tokens
       # Layerwise importance normalization
       if not args.dont_normalize_importance_by_layer:
            __UpperCamelCase   = 2
            __UpperCamelCase   = torch.pow(torch.pow(_lowercase				,     _lowercase   ).sum(-1   )				,     1 / exponent   )
            head_importance /= norm_by_layer.unsqueeze(-1   ) + 1e-2_0
  
       if not args.dont_normalize_global_importance:
            __UpperCamelCase   = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
  
       # Print/save matrices
       np.save(os.path.join(args.output_dir				,     'attn_entropy.npy'   )				,     attn_entropy.detach().cpu().numpy()   )
       np.save(os.path.join(args.output_dir				,     'head_importance.npy'   )				,     head_importance.detach().cpu().numpy()   )
  
       logger.info('Attention entropies'   )
       print_ad_tensor(_lowercase   )
       logger.info('Head importance scores'   )
       print_ad_tensor(_lowercase   )
       logger.info('Head ranked by importance scores'   )
       __UpperCamelCase   = torch.zeros(head_importance.numel()				,     dtype=torch.long				,     device=args.device   )
       __UpperCamelCase   = torch.arange(
           head_importance.numel()				,     device=args.device   )
       __UpperCamelCase   = head_ranks.view_as(_lowercase   )
       print_ad_tensor(_lowercase   )
  
       return attn_entropy, head_importance, preds, labels
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   Optional[Any]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = compute_heads_importance(_lowercase				,     _lowercase				,     _lowercase				,     compute_entropy=_lowercase   )
       __UpperCamelCase   = np.argmax(_lowercase				,     axis=1   ) if args.output_mode == 'classification' else np.squeeze(_lowercase   )
       __UpperCamelCase   = glue_compute_metrics(args.task_name				,     _lowercase				,     _lowercase   )[args.metric_name]
       logger.info('Pruning: original score: %f, threshold: %f'				,     _lowercase				,     original_score * args.masking_threshold   )
  
       __UpperCamelCase   = torch.ones_like(_lowercase   )
       __UpperCamelCase   = max(1				,     int(new_head_mask.numel() * args.masking_amount   )   )
  
       __UpperCamelCase   = original_score
       while current_score >= original_score * args.masking_threshold:
            __UpperCamelCase   = new_head_mask.clone()  # save current head mask
            # heads from least important to most - keep only not-masked heads
            __UpperCamelCase   = float('Inf'   )
            __UpperCamelCase   = head_importance.view(-1   ).sort()[1]
  
            if len(_lowercase   ) <= num_to_mask:
                 break
  
            # mask heads
            __UpperCamelCase   = current_heads_to_mask[:num_to_mask]
            logger.info('Heads to mask: %s'				,     str(current_heads_to_mask.tolist()   )   )
            __UpperCamelCase   = new_head_mask.view(-1   )
            __UpperCamelCase   = 0.0
            __UpperCamelCase   = new_head_mask.view_as(_lowercase   )
            __UpperCamelCase   = new_head_mask.clone().detach()
            print_ad_tensor(_lowercase   )
  
            # Compute metric and head importance again
            __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = compute_heads_importance(
                _lowercase				,     _lowercase				,     _lowercase				,     compute_entropy=_lowercase				,     head_mask=_lowercase   )
            __UpperCamelCase   = np.argmax(_lowercase				,     axis=1   ) if args.output_mode == 'classification' else np.squeeze(_lowercase   )
            __UpperCamelCase   = glue_compute_metrics(args.task_name				,     _lowercase				,     _lowercase   )[args.metric_name]
            logger.info(
                'Masking: current score: %f, remaining heads %d (%.1f percents)'				,     _lowercase				,     new_head_mask.sum()				,     new_head_mask.sum() / new_head_mask.numel() * 1_00				,     )
  
       logger.info('Final head mask'   )
       print_ad_tensor(_lowercase   )
       np.save(os.path.join(args.output_dir				,     'head_mask.npy'   )				,     head_mask.detach().cpu().numpy()   )
  
       return head_mask
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase   )				->   Optional[int]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = datetime.now()
       __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = compute_heads_importance(
           _lowercase				,     _lowercase				,     _lowercase				,     compute_entropy=_lowercase				,     compute_importance=_lowercase				,     head_mask=_lowercase   )
       __UpperCamelCase   = np.argmax(_lowercase				,     axis=1   ) if args.output_mode == 'classification' else np.squeeze(_lowercase   )
       __UpperCamelCase   = glue_compute_metrics(args.task_name				,     _lowercase				,     _lowercase   )[args.metric_name]
       __UpperCamelCase   = datetime.now() - before_time
  
       __UpperCamelCase   = sum(p.numel() for p in model.parameters()   )
       __UpperCamelCase   = {
           layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_lowercase   )   )
       }
  
       assert sum(len(_lowercase   ) for h in heads_to_prune.values()   ) == (1 - head_mask.long()).sum().item()
       model.prune_heads(_lowercase   )
       __UpperCamelCase   = sum(p.numel() for p in model.parameters()   )
  
       __UpperCamelCase   = datetime.now()
       __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = compute_heads_importance(
           _lowercase				,     _lowercase				,     _lowercase				,     compute_entropy=_lowercase				,     compute_importance=_lowercase				,     head_mask=_lowercase				,     actually_pruned=_lowercase				,     )
       __UpperCamelCase   = np.argmax(_lowercase				,     axis=1   ) if args.output_mode == 'classification' else np.squeeze(_lowercase   )
       __UpperCamelCase   = glue_compute_metrics(args.task_name				,     _lowercase				,     _lowercase   )[args.metric_name]
       __UpperCamelCase   = datetime.now() - before_time
  
       logger.info(
           'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'				,     _lowercase				,     _lowercase				,     pruned_num_params / original_num_params * 1_00				,     )
       logger.info('Pruning: score with masking: %f score with pruning: %f'				,     _lowercase				,     _lowercase   )
       logger.info('Pruning: speed ratio (new timing / original timing): %f percents'				,     original_time / new_time * 1_00   )
  
  
  
  
  
  
  def 		_A		(		)				->   str:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--data_dir'				,     default=_lowercase				,     type=_lowercase				,     required=_lowercase				,     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=_lowercase				,     type=_lowercase				,     required=_lowercase				,     help='Path to pretrained model or model identifier from huggingface.co/models'				,     )
       parser.add_argument(
           '--task_name'				,     default=_lowercase				,     type=_lowercase				,     required=_lowercase				,     help='The name of the task to train selected in the list: ' + ', '.join(glue_processors.keys()   )				,     )
       parser.add_argument(
           '--output_dir'				,     default=_lowercase				,     type=_lowercase				,     required=_lowercase				,     help='The output directory where the model predictions and checkpoints will be written.'				,     )
  
       # Other parameters
       parser.add_argument(
           '--config_name'				,     default=''				,     type=_lowercase				,     help='Pretrained config name or path if not the same as model_name_or_path'				,     )
       parser.add_argument(
           '--tokenizer_name'				,     default=''				,     type=_lowercase				,     help='Pretrained tokenizer name or path if not the same as model_name_or_path'				,     )
       parser.add_argument(
           '--cache_dir'				,     default=_lowercase				,     type=_lowercase				,     help='Where do you want to store the pre-trained models downloaded from huggingface.co'				,     )
       parser.add_argument(
           '--data_subset'				,     type=_lowercase				,     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=_lowercase				,     help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'				,     )
       parser.add_argument(
           '--masking_amount'				,     default=0.1				,     type=_lowercase				,     help='Amount to heads to masking at each masking step.'   )
       parser.add_argument('--metric_name'				,     default='acc'				,     type=_lowercase				,     help='Metric to use for head masking.'   )
  
       parser.add_argument(
           '--max_seq_length'				,     default=1_28				,     type=_lowercase				,     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=_lowercase				,     help='Batch size.'   )
  
       parser.add_argument('--seed'				,     type=_lowercase				,     default=42   )
       parser.add_argument('--local_rank'				,     type=_lowercase				,     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=_lowercase				,     default=''				,     help='Can be used for distant debugging.'   )
       parser.add_argument('--server_port'				,     type=_lowercase				,     default=''				,     help='Can be used for distant debugging.'   )
       __UpperCamelCase   = 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=_lowercase   )
            ptvsd.wait_for_attach()
  
       # Setup devices and distributed training
       if args.local_rank == -1 or args.no_cuda:
            __UpperCamelCase   = torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'   )
            __UpperCamelCase   = 0 if args.no_cuda else torch.cuda.device_count()
       else:
            torch.cuda.set_device(args.local_rank   )
            __UpperCamelCase   = torch.device('cuda'				,     args.local_rank   )
            __UpperCamelCase   = 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   )   )   )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(args.local_rank   ):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
  
       # Set seeds
       set_seed(args.seed   )
  
       # Prepare GLUE task
       __UpperCamelCase   = args.task_name.lower()
       if args.task_name not in glue_processors:
            raise ValueError('Task not found: %s' % (args.task_name)   )
       __UpperCamelCase   = glue_processors[args.task_name]()
       __UpperCamelCase   = glue_output_modes[args.task_name]
       __UpperCamelCase   = processor.get_labels()
       __UpperCamelCase   = len(_lowercase   )
  
       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
  
       __UpperCamelCase   = AutoConfig.from_pretrained(
           args.config_name if args.config_name else args.model_name_or_path				,     num_labels=_lowercase				,     finetuning_task=args.task_name				,     output_attentions=_lowercase				,     cache_dir=args.cache_dir				,     )
       __UpperCamelCase   = AutoTokenizer.from_pretrained(
           args.tokenizer_name if args.tokenizer_name else args.model_name_or_path				,     cache_dir=args.cache_dir				,     )
       __UpperCamelCase   = AutoModelForSequenceClassification.from_pretrained(
           args.model_name_or_path				,     from_tf=bool('.ckpt' in args.model_name_or_path   )				,     config=_lowercase				,     cache_dir=args.cache_dir				,     )
  
       # Distributed and parallel training
       model.to(args.device   )
       if args.local_rank != -1:
            __UpperCamelCase   = nn.parallel.DistributedDataParallel(
                _lowercase				,     device_ids=[args.local_rank]				,     output_device=args.local_rank				,     find_unused_parameters=_lowercase   )
       elif args.n_gpu > 1:
            __UpperCamelCase   = nn.DataParallel(_lowercase   )
  
       # Print/save training arguments
       os.makedirs(args.output_dir				,     exist_ok=_lowercase   )
       torch.save(_lowercase				,     os.path.join(args.output_dir				,     'run_args.bin'   )   )
       logger.info('Training/evaluation parameters %s'				,     _lowercase   )
  
       # Prepare dataset for the GLUE task
       __UpperCamelCase   = GlueDataset(_lowercase				,     tokenizer=_lowercase				,     mode='dev'   )
       if args.data_subset > 0:
            __UpperCamelCase   = Subset(_lowercase				,     list(range(min(args.data_subset				,     len(_lowercase   )   )   )   )   )
       __UpperCamelCase   = SequentialSampler(_lowercase   ) if args.local_rank == -1 else DistributedSampler(_lowercase   )
       __UpperCamelCase   = DataLoader(
           _lowercase				,     sampler=_lowercase				,     batch_size=args.batch_size				,     collate_fn=_lowercase   )
  
       # Compute head entropy and importance score
       compute_heads_importance(_lowercase				,     _lowercase				,     _lowercase   )
  
       # 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:
            __UpperCamelCase   = mask_heads(_lowercase				,     _lowercase				,     _lowercase   )
            prune_heads(_lowercase				,     _lowercase				,     _lowercase				,     _lowercase   )
  
  
  if __name__ == "__main__":
      main()
  
  
  
style_context: 
  
  
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
       __UpperCamelCase   = GPTNeoConfig(
           hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
       print(f'''Building PyTorch model from configuration: {config}'''   )
       __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )
  
       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
       model.save_pretrained(_lowercase   )
  
  
  if __name__ == "__main__":
      __snake_case             =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __snake_case             =	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  