




'''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, 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


A_ 			=		logging.getLogger(__name__)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__					:      Tuple  			=						p * torch.log(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						0
      return -plogp.sum(dim=-1		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Dict:
      logger.info('lv, h >\t' + '\t'.join(f"{x + 1}" for x in range(len(__SCREAMING_SNAKE_CASE		)		)		)		)
      for row in range(len(__SCREAMING_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 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=True							,			__SCREAMING_SNAKE_CASE=True							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=False		)						->      Optional[Any]:
      snake_case__					:      List[str]  			=						model.config.num_hidden_layers, model.config.num_attention_heads
      snake_case__					:      Dict  			=						torch.zeros(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		).to(args.device		)
      snake_case__					:      Optional[Any]  			=						torch.zeros(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		).to(args.device		)

      if head_mask is None:
            snake_case__					:      Optional[Any]  			=						torch.ones(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		).to(args.device		)

      head_mask.requires_grad_(requires_grad=__SCREAMING_SNAKE_CASE		)
      # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
      if actually_pruned:
            snake_case__					:      Optional[int]  			=						None

      snake_case__					:      Any  			=						None
      snake_case__					:      Union[str, Any]  			=						None
      snake_case__					:      Optional[int]  			=						0.0

      for step, inputs in enumerate(tqdm(__SCREAMING_SNAKE_CASE							,			desc='Iteration'							,			disable=args.local_rank not in [-1, 0]		)		):
            for k, v in inputs.items():
                  snake_case__					:      Optional[Any]  			=						v.to(args.device		)

            # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
            snake_case__					:      Optional[Any]  			=						model(**__SCREAMING_SNAKE_CASE							,			head_mask=__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Tuple  			=						(
                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(__SCREAMING_SNAKE_CASE		):
                        snake_case__					:      List[Any]  			=						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:
                  snake_case__					:      int  			=						logits.detach().cpu().numpy()
                  snake_case__					:      Dict  			=						inputs['labels'].detach().cpu().numpy()
            else:
                  snake_case__					:      str  			=						np.append(__SCREAMING_SNAKE_CASE							,			logits.detach().cpu().numpy()							,			axis=0		)
                  snake_case__					:      Optional[Any]  			=						np.append(__SCREAMING_SNAKE_CASE							,			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:
            snake_case__					:      List[str]  			=						2
            snake_case__					:      Union[str, Any]  			=						torch.pow(torch.pow(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		).sum(-1		)							,			1 / exponent		)
            head_importance /= norm_by_layer.unsqueeze(-1		) + 1E-20

      if not args.dont_normalize_global_importance:
            snake_case__					:      List[str]  			=						(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(__SCREAMING_SNAKE_CASE		)
      logger.info('Head importance scores'		)
      print_ad_tensor(__SCREAMING_SNAKE_CASE		)
      logger.info('Head ranked by importance scores'		)
      snake_case__					:      Union[str, Any]  			=						torch.zeros(head_importance.numel()							,			dtype=torch.long							,			device=args.device		)
      snake_case__					:      List[str]  			=						torch.arange(
          head_importance.numel()							,			device=args.device		)
      snake_case__					:      List[Any]  			=						head_ranks.view_as(__SCREAMING_SNAKE_CASE		)
      print_ad_tensor(__SCREAMING_SNAKE_CASE		)

      return attn_entropy, head_importance, preds, labels






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Tuple:
      snake_case__					:      Any  			=						compute_heads_importance(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			compute_entropy=__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						np.argmax(__SCREAMING_SNAKE_CASE							,			axis=1		) if args.output_mode == 'classification' else np.squeeze(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      List[str]  			=						glue_compute_metrics(args.task_name							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)[args.metric_name]
      logger.info('Pruning: original score: %f, threshold: %f'							,			__SCREAMING_SNAKE_CASE							,			original_score * args.masking_threshold		)

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

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

            if len(__SCREAMING_SNAKE_CASE		) <= num_to_mask:
                  break

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

            # Compute metric and head importance again
            snake_case__					:      Any  			=						compute_heads_importance(
                __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			compute_entropy=__SCREAMING_SNAKE_CASE							,			head_mask=__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Dict  			=						np.argmax(__SCREAMING_SNAKE_CASE							,			axis=1		) if args.output_mode == 'classification' else np.squeeze(__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Tuple  			=						glue_compute_metrics(args.task_name							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)[args.metric_name]
            logger.info(
                'Masking: current score: %f, remaining heads %d (%.1f percents)'							,			__SCREAMING_SNAKE_CASE							,			new_head_mask.sum()							,			new_head_mask.sum() / new_head_mask.numel() * 100							,			)

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

      return head_mask






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      snake_case__					:      List[str]  			=						datetime.now()
      snake_case__					:      str  			=						compute_heads_importance(
          __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			compute_entropy=__SCREAMING_SNAKE_CASE							,			compute_importance=__SCREAMING_SNAKE_CASE							,			head_mask=__SCREAMING_SNAKE_CASE		)
      snake_case__					:      List[Any]  			=						np.argmax(__SCREAMING_SNAKE_CASE							,			axis=1		) if args.output_mode == 'classification' else np.squeeze(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      int  			=						glue_compute_metrics(args.task_name							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)[args.metric_name]
      snake_case__					:      Optional[int]  			=						datetime.now() - before_time

      snake_case__					:      Union[str, Any]  			=						sum(p.numel() for p in model.parameters()		)
      snake_case__					:      List[str]  			=						{
          layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__SCREAMING_SNAKE_CASE		)		)
      }

      assert sum(len(__SCREAMING_SNAKE_CASE		) for h in heads_to_prune.values()		) == (1 - head_mask.long()).sum().item()
      model.prune_heads(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Union[str, Any]  			=						sum(p.numel() for p in model.parameters()		)

      snake_case__					:      List[str]  			=						datetime.now()
      snake_case__					:      Optional[Any]  			=						compute_heads_importance(
          __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			compute_entropy=__SCREAMING_SNAKE_CASE							,			compute_importance=__SCREAMING_SNAKE_CASE							,			head_mask=__SCREAMING_SNAKE_CASE							,			actually_pruned=__SCREAMING_SNAKE_CASE							,			)
      snake_case__					:      Any  			=						np.argmax(__SCREAMING_SNAKE_CASE							,			axis=1		) if args.output_mode == 'classification' else np.squeeze(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Dict  			=						glue_compute_metrics(args.task_name							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)[args.metric_name]
      snake_case__					:      Dict  			=						datetime.now() - before_time

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






def 		UpperCamelCase__			(		)						->      Any:
      snake_case__					:      Tuple  			=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--data_dir'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			required=__SCREAMING_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=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			required=__SCREAMING_SNAKE_CASE							,			help='Path to pretrained model or model identifier from huggingface.co/models'							,			)
      parser.add_argument(
          '--task_name'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			required=__SCREAMING_SNAKE_CASE							,			help='The name of the task to train selected in the list: ' + ', '.join(glue_processors.keys()		)							,			)
      parser.add_argument(
          '--output_dir'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			required=__SCREAMING_SNAKE_CASE							,			help='The output directory where the model predictions and checkpoints will be written.'							,			)

      # Other parameters
      parser.add_argument(
          '--config_name'							,			default=''							,			type=__SCREAMING_SNAKE_CASE							,			help='Pretrained config name or path if not the same as model_name_or_path'							,			)
      parser.add_argument(
          '--tokenizer_name'							,			default=''							,			type=__SCREAMING_SNAKE_CASE							,			help='Pretrained tokenizer name or path if not the same as model_name_or_path'							,			)
      parser.add_argument(
          '--cache_dir'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			help='Where do you want to store the pre-trained models downloaded from huggingface.co'							,			)
      parser.add_argument(
          '--data_subset'							,			type=__SCREAMING_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=__SCREAMING_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=__SCREAMING_SNAKE_CASE							,			help='Amount to heads to masking at each masking step.'		)
      parser.add_argument('--metric_name'							,			default='acc'							,			type=__SCREAMING_SNAKE_CASE							,			help='Metric to use for head masking.'		)

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

      parser.add_argument('--seed'							,			type=__SCREAMING_SNAKE_CASE							,			default=42		)
      parser.add_argument('--local_rank'							,			type=__SCREAMING_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=__SCREAMING_SNAKE_CASE							,			default=''							,			help='Can be used for distant debugging.'		)
      parser.add_argument('--server_port'							,			type=__SCREAMING_SNAKE_CASE							,			default=''							,			help='Can be used for distant debugging.'		)
      snake_case__					:      Any  			=						parser.parse_args()

      if args.server_ip and args.server_port:
            # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
            import ptvsd

            print('Waiting for debugger attach'		)
            ptvsd.enable_attach(address=(args.server_ip, args.server_port)							,			redirect_output=__SCREAMING_SNAKE_CASE		)
            ptvsd.wait_for_attach()

      # Setup devices and distributed training
      if args.local_rank == -1 or args.no_cuda:
            snake_case__					:      Optional[int]  			=						torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'		)
            snake_case__					:      str  			=						0 if args.no_cuda else torch.cuda.device_count()
      else:
            torch.cuda.set_device(args.local_rank		)
            snake_case__					:      Tuple  			=						torch.device('cuda'							,			args.local_rank		)
            snake_case__					:      Dict  			=						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
      snake_case__					:      List[str]  			=						args.task_name.lower()
      if args.task_name not in glue_processors:
            raise ValueError('Task not found: %s' % (args.task_name)		)
      snake_case__					:      Any  			=						glue_processors[args.task_name]()
      snake_case__					:      Optional[int]  			=						glue_output_modes[args.task_name]
      snake_case__					:      Dict  			=						processor.get_labels()
      snake_case__					:      str  			=						len(__SCREAMING_SNAKE_CASE		)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      snake_case__					:      Union[str, Any]  			=						AutoConfig.from_pretrained(
          args.config_name if args.config_name else args.model_name_or_path							,			num_labels=__SCREAMING_SNAKE_CASE							,			finetuning_task=args.task_name							,			output_attentions=__SCREAMING_SNAKE_CASE							,			cache_dir=args.cache_dir							,			)
      snake_case__					:      Any  			=						AutoTokenizer.from_pretrained(
          args.tokenizer_name if args.tokenizer_name else args.model_name_or_path							,			cache_dir=args.cache_dir							,			)
      snake_case__					:      Optional[int]  			=						AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path							,			from_tf=bool('.ckpt' in args.model_name_or_path		)							,			config=__SCREAMING_SNAKE_CASE							,			cache_dir=args.cache_dir							,			)

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

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

      # Prepare dataset for the GLUE task
      snake_case__					:      Tuple  			=						GlueDataset(__SCREAMING_SNAKE_CASE							,			tokenizer=__SCREAMING_SNAKE_CASE							,			mode='dev'		)
      if args.data_subset > 0:
            snake_case__					:      Tuple  			=						Subset(__SCREAMING_SNAKE_CASE							,			list(range(min(args.data_subset							,			len(__SCREAMING_SNAKE_CASE		)		)		)		)		)
      snake_case__					:      Any  			=						SequentialSampler(__SCREAMING_SNAKE_CASE		) if args.local_rank == -1 else DistributedSampler(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Union[str, Any]  			=						DataLoader(
          __SCREAMING_SNAKE_CASE							,			sampler=__SCREAMING_SNAKE_CASE							,			batch_size=args.batch_size							,			collate_fn=__SCREAMING_SNAKE_CASE		)

      # Compute head entropy and importance score
      compute_heads_importance(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_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:
            snake_case__					:      Any  			=						mask_heads(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
            prune_heads(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      main()








'''simple docstring'''






import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


A_ 			=		logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


A_ 			=		namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Dict:
      snake_case__					:      Dict  			=						BertTokenizer.from_pretrained('bert-base-uncased'							,			do_lower_case=__SCREAMING_SNAKE_CASE		)
      snake_case__					:      List[Any]  			=						BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'		)
      model.to(args.device		)
      model.eval()

      snake_case__					:      Optional[int]  			=						{
          'BOS': tokenizer.vocab['[unused0]'],
          'EOS': tokenizer.vocab['[unused1]'],
          'PAD': tokenizer.vocab['[PAD]'],
      }

      if args.compute_rouge:
            snake_case__					:      Any  			=						[]
            snake_case__					:      Any  			=						[]

            import nltk
            import rouge

            nltk.download('punkt'		)
            snake_case__					:      str  			=						rouge.Rouge(
                metrics=['rouge-n', 'rouge-l']							,			max_n=2							,			limit_length=__SCREAMING_SNAKE_CASE							,			length_limit=args.beam_size							,			length_limit_type='words'							,			apply_avg=__SCREAMING_SNAKE_CASE							,			apply_best=__SCREAMING_SNAKE_CASE							,			alpha=0.5							,			weight_factor=1.2							,			stemming=__SCREAMING_SNAKE_CASE							,			)

      # these (unused) arguments are defined to keep the compatibility
      # with the legacy code and will be deleted in a next iteration.
      snake_case__					:      List[Any]  			=						''
      snake_case__					:      int  			=						''

      snake_case__					:      str  			=						build_data_iterator(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Optional[Any]  			=						build_predictor(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

      logger.info('***** Running evaluation *****'		)
      logger.info('  Number examples = %d'							,			len(data_iterator.dataset		)		)
      logger.info('  Batch size = %d'							,			args.batch_size		)
      logger.info(''		)
      logger.info('***** Beam Search parameters *****'		)
      logger.info('  Beam size = %d'							,			args.beam_size		)
      logger.info('  Minimum length = %d'							,			args.min_length		)
      logger.info('  Maximum length = %d'							,			args.max_length		)
      logger.info('  Alpha (length penalty) = %.2f'							,			args.alpha		)
      logger.info('  Trigrams %s be blocked'							,			('will' if args.block_trigram else 'will NOT')		)

      for batch in tqdm(__SCREAMING_SNAKE_CASE		):
            snake_case__					:      Dict  			=						predictor.translate_batch(__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Optional[Any]  			=						predictor.from_batch(__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Union[str, Any]  			=						[format_summary(__SCREAMING_SNAKE_CASE		) for t in translations]
            save_summaries(__SCREAMING_SNAKE_CASE							,			args.summaries_output_dir							,			batch.document_names		)

            if args.compute_rouge:
                  reference_summaries += batch.tgt_str
                  generated_summaries += summaries

      if args.compute_rouge:
            snake_case__					:      int  			=						rouge_evaluator.get_scores(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
            snake_case__					:      str  			=						format_rouge_scores(__SCREAMING_SNAKE_CASE		)
            save_rouge_scores(__SCREAMING_SNAKE_CASE		)
            print(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
      for summary, document_name in zip(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		):
            # Prepare the summary file's name
            if "." in document_name:
                  snake_case__					:      str  			=						'.'.join(document_name.split('.'		)[:-1]		)
                  snake_case__					:      Optional[int]  			=						document_name.split('.'		)[-1]
                  snake_case__					:      Tuple  			=						bare_document_name + '_summary.' + extension
            else:
                  snake_case__					:      Union[str, Any]  			=						document_name + '_summary'

            snake_case__					:      Union[str, Any]  			=						os.path.join(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
            with open(__SCREAMING_SNAKE_CASE							,			'w'		) as output:
                  output.write(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__			,       snake_case__			,       snake_case__					:      Any  			=						translation
      snake_case__					:      Any  			=						(
          raw_summary.replace('[unused0]'							,			''		)
          .replace('[unused3]'							,			''		)
          .replace('[PAD]'							,			''		)
          .replace('[unused1]'							,			''		)
          .replace(r' +'							,			' '		)
          .replace(' [unused2] '							,			'. '		)
          .replace('[unused2]'							,			''		)
          .strip()
      )

      return summary






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
      return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
          scores['rouge-1']['f']							,			scores['rouge-1']['p']							,			scores['rouge-1']['r']							,			scores['rouge-2']['f']							,			scores['rouge-2']['p']							,			scores['rouge-2']['r']							,			scores['rouge-l']['f']							,			scores['rouge-l']['p']							,			scores['rouge-l']['r']							,			)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Optional[Any]:
      with open('rouge_scores.txt'							,			'w'		) as output:
            output.write(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      snake_case__					:      Optional[Any]  			=						load_and_cache_examples(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Union[str, Any]  			=						SequentialSampler(__SCREAMING_SNAKE_CASE		)

      def collate_fn(__SCREAMING_SNAKE_CASE		):
            return collate(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			block_size=512							,			device=args.device		)

      snake_case__					:      List[Any]  			=						DataLoader(
          __SCREAMING_SNAKE_CASE							,			sampler=__SCREAMING_SNAKE_CASE							,			batch_size=args.batch_size							,			collate_fn=__SCREAMING_SNAKE_CASE							,			)

      return iterator






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Dict:
      snake_case__					:      Any  			=						CNNDMDataset(args.documents_dir		)
      return dataset






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__					:      Optional[int]  			=						[x for x in data if not len(x[1]		) == 0]  # remove empty_files
      snake_case__					:      List[str]  			=						[name for name, _, _ in data]
      snake_case__					:      List[str]  			=						[' '.join(__SCREAMING_SNAKE_CASE		) for _, _, summary_list in data]

      snake_case__					:      int  			=						[encode_for_summarization(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		) for _, story, summary in data]
      snake_case__					:      str  			=						torch.tensor(
          [truncate_or_pad(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			tokenizer.pad_token_id		) for story, _ in encoded_text]		)
      snake_case__					:      Any  			=						compute_token_type_ids(__SCREAMING_SNAKE_CASE							,			tokenizer.cls_token_id		)
      snake_case__					:      List[str]  			=						build_mask(__SCREAMING_SNAKE_CASE							,			tokenizer.pad_token_id		)

      snake_case__					:      int  			=						Batch(
          document_names=__SCREAMING_SNAKE_CASE							,			batch_size=len(__SCREAMING_SNAKE_CASE		)							,			src=encoded_stories.to(__SCREAMING_SNAKE_CASE		)							,			segs=encoder_token_type_ids.to(__SCREAMING_SNAKE_CASE		)							,			mask_src=encoder_mask.to(__SCREAMING_SNAKE_CASE		)							,			tgt_str=__SCREAMING_SNAKE_CASE							,			)

      return batch






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
      snake_case__					:      Optional[Any]  			=						summary_tokens.to('cpu'		).numpy()
      snake_case__					:      Dict  			=						tokenizer.decode(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Optional[int]  			=						summary.split('.'		)
      snake_case__					:      Dict  			=						[s + '.' for s in sentences]
      return sentences






def 		UpperCamelCase__			(		)						->      Any:
      snake_case__					:      Optional[int]  			=						argparse.ArgumentParser()
      parser.add_argument(
          '--documents_dir'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			required=__SCREAMING_SNAKE_CASE							,			help='The folder where the documents to summarize are located.'							,			)
      parser.add_argument(
          '--summaries_output_dir'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			required=__SCREAMING_SNAKE_CASE							,			help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'							,			)
      parser.add_argument(
          '--compute_rouge'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			required=__SCREAMING_SNAKE_CASE							,			help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'							,			)
      # EVALUATION options
      parser.add_argument(
          '--no_cuda'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			help='Whether to force the execution on CPU.'							,			)
      parser.add_argument(
          '--batch_size'							,			default=4							,			type=__SCREAMING_SNAKE_CASE							,			help='Batch size per GPU/CPU for training.'							,			)
      # BEAM SEARCH arguments
      parser.add_argument(
          '--min_length'							,			default=50							,			type=__SCREAMING_SNAKE_CASE							,			help='Minimum number of tokens for the summaries.'							,			)
      parser.add_argument(
          '--max_length'							,			default=200							,			type=__SCREAMING_SNAKE_CASE							,			help='Maixmum number of tokens for the summaries.'							,			)
      parser.add_argument(
          '--beam_size'							,			default=5							,			type=__SCREAMING_SNAKE_CASE							,			help='The number of beams to start with for each example.'							,			)
      parser.add_argument(
          '--alpha'							,			default=0.95							,			type=__SCREAMING_SNAKE_CASE							,			help='The value of alpha for the length penalty in the beam search.'							,			)
      parser.add_argument(
          '--block_trigram'							,			default=__SCREAMING_SNAKE_CASE							,			type=__SCREAMING_SNAKE_CASE							,			help='Whether to block the existence of repeating trigrams in the text generated by beam search.'							,			)
      snake_case__					:      str  			=						parser.parse_args()

      # Select device (distibuted not available)
      snake_case__					:      str  			=						torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'		)

      # Check the existence of directories
      if not args.summaries_output_dir:
            snake_case__					:      Union[str, Any]  			=						args.documents_dir

      if not documents_dir_is_valid(args.documents_dir		):
            raise FileNotFoundError(
                'We could not find the directory you specified for the documents to summarize, or it was empty. Please'
                ' specify a valid path.'		)
      os.makedirs(args.summaries_output_dir							,			exist_ok=__SCREAMING_SNAKE_CASE		)

      evaluate(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Dict:
      if not os.path.exists(__SCREAMING_SNAKE_CASE		):
            return False

      snake_case__					:      int  			=						os.listdir(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) == 0:
            return False

      return True


if __name__ == "__main__":
      main()
