
"""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__ :       Optional[int]				= logging.getLogger(__name__)




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



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




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase=False    ):






  """simple docstring"""



  _lowercase:      int   			=  2
  if unlogit:
    _lowercase:      int   			=  torch.pow(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      int   			=  p * torch.log(_UpperCamelCase    )
  _lowercase:      Union[str, Any]   			=  0
  return -plogp.sum(dim=-1    )




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  logger.info('''lv, h >\t''' + '''\t'''.join(f'''{x + 1}''' for x in range(len(_UpperCamelCase    )    )    )    )
  for row in range(len(_UpperCamelCase    )    ):
    if tensor.dtype != torch.long:
      logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:.5f}''' for x in tensor[row].cpu().data    )    )
    else:
      logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:d}''' for x in tensor[row].cpu().data    )    )




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase=True			,					_UpperCamelCase=True			,					_UpperCamelCase=None			,					_UpperCamelCase=False    ):






  """simple docstring"""



  _lowercase			,					_lowercase:      Optional[Any]   			=  model.config.num_hidden_layers, model.config.num_attention_heads
  _lowercase:      Union[str, Any]   			=  torch.zeros(_UpperCamelCase			,					_UpperCamelCase    ).to(args.device    )
  _lowercase:      Dict   			=  torch.zeros(_UpperCamelCase			,					_UpperCamelCase    ).to(args.device    )

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

  head_mask.requires_grad_(requires_grad=_UpperCamelCase    )
  # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  if actually_pruned:
    _lowercase:      List[Any]   			=  None

  _lowercase:      str   			=  0.0
  _lowercase:      Dict   			=  0.0
  for step, inputs in enumerate(tqdm(_UpperCamelCase			,					desc='''Iteration'''			,					disable=args.local_rank not in [-1, 0]    )    ):
    _lowercase:      List[str]   			=  tuple(t.to(args.device    ) for t in inputs    )
    ((_lowercase)			,					):      str   			=  inputs

    # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
    _lowercase:      int   			=  model(_UpperCamelCase			,					labels=_UpperCamelCase			,					head_mask=_UpperCamelCase    )
    #  (loss), lm_logits, presents, (all hidden_states), (attentions)
    _lowercase			,					_lowercase			,					_lowercase:      Any   			=  (
        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(_UpperCamelCase    ):
        _lowercase:      Dict   			=  entropy(attn.detach()			,					_UpperCamelCase    )
        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(_UpperCamelCase    ).float().detach().sum().data

  # Normalize
  attn_entropy /= tot_tokens
  head_importance /= tot_tokens
  # Layerwise importance normalization
  if not args.dont_normalize_importance_by_layer:
    _lowercase:      Tuple   			=  2
    _lowercase:      Optional[int]   			=  torch.pow(torch.pow(_UpperCamelCase			,					_UpperCamelCase    ).sum(-1    )			,					1 / exponent    )
    head_importance /= norm_by_layer.unsqueeze(-1    ) + 1e-20

  if not args.dont_normalize_global_importance:
    _lowercase:      str   			=  (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

  # Print matrices
  if compute_entropy:
    logger.info('''Attention entropies'''    )
    print_ad_tensor(_UpperCamelCase    )
  if compute_importance:
    logger.info('''Head importance scores'''    )
    print_ad_tensor(_UpperCamelCase    )
  logger.info('''Head ranked by importance scores'''    )
  _lowercase:      Optional[Any]   			=  torch.zeros(head_importance.numel()			,					dtype=torch.long			,					device=args.device    )
  _lowercase:      List[Any]   			=  torch.arange(
      head_importance.numel()			,					device=args.device    )
  _lowercase:      Optional[int]   			=  head_ranks.view_as(_UpperCamelCase    )
  print_ad_tensor(_UpperCamelCase    )
  return attn_entropy, head_importance, total_loss




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase			,					_lowercase			,					_lowercase:      Union[str, Any]   			=  compute_heads_importance(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					compute_entropy=_UpperCamelCase    )
  _lowercase:      List[str]   			=  1 / loss  # instead of downsteam score use the LM loss
  logger.info('''Pruning: original score: %f, threshold: %f'''			,					_UpperCamelCase			,					original_score * args.masking_threshold    )

  _lowercase:      List[Any]   			=  torch.ones_like(_UpperCamelCase    )
  _lowercase:      List[str]   			=  max(1			,					int(new_head_mask.numel() * args.masking_amount    )    )

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

    if len(_UpperCamelCase    ) <= num_to_mask:
      print('''BREAK BY num_to_mask'''    )
      break

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

    # Compute metric and head importance again
    _lowercase			,					_lowercase			,					_lowercase:      List[str]   			=  compute_heads_importance(
        _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					compute_entropy=_UpperCamelCase			,					head_mask=_UpperCamelCase    )
    _lowercase:      Any   			=  1 / loss
    logger.info(
        '''Masking: current score: %f, remaining heads %d (%.1f percents)'''			,					_UpperCamelCase			,					new_head_mask.sum()			,					new_head_mask.sum() / new_head_mask.numel() * 100			,					)

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

  return head_mask




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  datetime.now()
  _lowercase			,					_lowercase			,					_lowercase:      Any   			=  compute_heads_importance(
      _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					compute_entropy=_UpperCamelCase			,					compute_importance=_UpperCamelCase			,					head_mask=_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  1 / loss
  _lowercase:      List[str]   			=  datetime.now() - before_time

  _lowercase:      Optional[int]   			=  sum(p.numel() for p in model.parameters()    )
  _lowercase:      Optional[int]   			=  {
      layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_UpperCamelCase    )    )
  }

  for k, v in heads_to_prune.items():
    if isinstance(_UpperCamelCase			,					_UpperCamelCase    ):
      _lowercase:      Optional[int]   			=  [
          v,
      ]

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

  _lowercase:      Tuple   			=  datetime.now()
  _lowercase			,					_lowercase			,					_lowercase:      List[Any]   			=  compute_heads_importance(
      _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					compute_entropy=_UpperCamelCase			,					compute_importance=_UpperCamelCase			,					head_mask=_UpperCamelCase			,					actually_pruned=_UpperCamelCase			,					)

  _lowercase:      int   			=  1 / loss
  _lowercase:      Optional[int]   			=  datetime.now() - before_time

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




def 			_lowerCAmelCase		(		):






  """simple docstring"""



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

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

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

  parser.add_argument('''--seed'''			,					type=_UpperCamelCase			,					default=42    )
  parser.add_argument('''--local_rank'''			,					type=_UpperCamelCase			,					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=_UpperCamelCase			,					default=''''''			,					help='''Can be used for distant debugging.'''    )
  parser.add_argument('''--server_port'''			,					type=_UpperCamelCase			,					default=''''''			,					help='''Can be used for distant debugging.'''    )
  _lowercase:      Optional[int]   			=  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=_UpperCamelCase    )
    ptvsd.wait_for_attach()

  # Setup devices and distributed training
  if args.local_rank == -1 or args.no_cuda:
    _lowercase:      Any   			=  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''    )
    _lowercase:      str   			=  0 if args.no_cuda else torch.cuda.device_count()
  else:
    torch.cuda.set_device(args.local_rank    )
    _lowercase:      Any   			=  torch.device('''cuda'''			,					args.local_rank    )
    _lowercase:      Union[str, Any]   			=  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    )    )    )

  _lowercase:      Optional[int]   			=  GPTaLMHeadModel.from_pretrained(args.model_name_or_path    )

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

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

  # Prepare dataset
  _lowercase:      Tuple   			=  np.concatenate(
      [
          np.loadtxt(args.data_dir			,					dtype=np.intaa    ),
      ]    )
  _lowercase:      str   			=  (torch.from_numpy(_UpperCamelCase    ),)
  _lowercase:      Optional[int]   			=  TensorDataset(*_UpperCamelCase    )
  _lowercase:      int   			=  RandomSampler(_UpperCamelCase    )
  _lowercase:      Any   			=  DataLoader(_UpperCamelCase			,					sampler=_UpperCamelCase			,					batch_size=args.batch_size    )

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

  # 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:
    _lowercase:      List[str]   			=  mask_heads(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )
    prune_heads(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()


"""simple docstring"""




from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A__ :       Any				= [
    'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
    ' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
    ' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
    'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
    ' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
    ' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
    ' body.',
    'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
    ' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
    ' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
    ' punishment.',
]

A__ :       Tuple				= [
    'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
    ' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
    ' had informed his Lufthansa training school of an episode of severe depression, airline says .',
    'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
    ' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
    ' Israelis .',
    'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
    ' death . Organization claims that governments around the world are using the threat of terrorism to advance'
    ' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
    ' sentences up by 28% .',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Any   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					bootstrap_aggregation=_UpperCamelCase			,					rouge_keys=['''rouge2''', '''rougeL''']    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      List[Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					bootstrap_aggregation=_UpperCamelCase			,					rouge_keys=['''rouge2''']    )
  assert (
      pd.DataFrame(no_aggregation['''rouge2''']    ).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra['''rouge2''']    ).fmeasure.mean()
  )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  '''rougeLsum'''
  _lowercase:      Dict   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=[k]    )[k]
  _lowercase:      List[str]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=[k]    )[k]
  assert score > score_no_sep




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Tuple   			=  ['''rouge1''', '''rouge2''', '''rougeL''']
  _lowercase:      Dict   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=_UpperCamelCase    )
  assert score_sep == score_no_sep




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  [
      '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
  ]
  _lowercase:      Union[str, Any]   			=  [
      '''Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
      ''' the final seconds on board Flight 9525.''',
  ]
  assert calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase    ) == calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      int   			=  [
      '''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
  ]
  _lowercase:      Union[str, Any]   			=  [
      ''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
  ]

  _lowercase:      List[Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					rouge_keys=['''rougeLsum''']			,					newline_sep=_UpperCamelCase    )['''rougeLsum''']
  _lowercase:      Union[str, Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					rouge_keys=['''rougeLsum''']    )['''rougeLsum''']
  assert new_score > prev_score







def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  Path('''examples/seq2seq/test_data/wmt_en_ro'''    )
  _lowercase:      int   			=  calculate_rouge_path(data_dir.joinpath('''test.source'''    )			,					data_dir.joinpath('''test.target'''    )    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  calculate_rouge_path(
      data_dir.joinpath('''test.source'''    )			,					data_dir.joinpath('''test.target'''    )			,					bootstrap_aggregation=_UpperCamelCase    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
