


import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCAmelCase__   	=							getLogger(__name__)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case = 8     ,						__snake_case = 1024     ,						__snake_case="val"     ,						__snake_case=None     ,						__snake_case=False     ,						__snake_case="summarization"     ,						__snake_case=None     ,						__snake_case=1     ,						__snake_case = None     ,						__snake_case=""     ,						**__snake_case     ,						)     ->			Dict:







 """simple docstring"""


 _lowercase							      =str(__snake_case						)
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''     ,						rank=__snake_case						)

 _lowercase							      =Path(__snake_case						)
 _lowercase							      =save_dir.joinpath(F"rank_{local_rank}_output.json"						)
 torch.cuda.set_device(__snake_case						)
 _lowercase							      =AutoModelForSeqaSeqLM.from_pretrained(__snake_case						).cuda()
 if fpaa:
  _lowercase							      =model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(__snake_case     ,						__snake_case						)  # update config with task specific params
 _lowercase							      =generate_kwargs.pop('''num_beams'''     ,						model.config.num_beams						)  # AttributeError risk?
 if num_return_sequences > num_beams:
  _lowercase							      =num_return_sequences

 _lowercase							      =AutoTokenizer.from_pretrained(__snake_case						)
 logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"						)  # if this is wrong, check config.model_type.

 if max_source_length is None:
  _lowercase							      =tokenizer.model_max_length
 if prefix is None:
  _lowercase							      =prefix or getattr(model.config     ,						'''prefix'''     ,						''''''						) or ''''''
 _lowercase							      =SeqaSeqDataset(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						max_target_length=1024     ,						type_path=__snake_case     ,						n_obs=__snake_case     ,						prefix=__snake_case     ,						**__snake_case     ,						)
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 _lowercase							      =ds.make_sortish_sampler(__snake_case     ,						distributed=__snake_case     ,						add_extra_examples=__snake_case     ,						shuffle=__snake_case						)
 _lowercase							      =DataLoader(__snake_case     ,						sampler=__snake_case     ,						batch_size=__snake_case     ,						collate_fn=ds.collate_fn						)
 _lowercase							      =[]
 for batch in tqdm(__snake_case						):
  _lowercase							      =model.generate(
      input_ids=batch['''input_ids'''].to(model.device						)     ,						attention_mask=batch['''attention_mask'''].to(model.device						)     ,						num_return_sequences=__snake_case     ,						num_beams=__snake_case     ,						**__snake_case     ,						)
  _lowercase							      =tokenizer.batch_decode(__snake_case     ,						skip_special_tokens=__snake_case     ,						clean_up_tokenization_spaces=__snake_case						)
  _lowercase							      =batch['''ids''']
  if num_return_sequences > 1:
   _lowercase							      =chunks(__snake_case     ,						__snake_case						)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(__snake_case						):
   results.append({'''pred''': pred, '''id''': ids[i].item()}						)
 save_json(__snake_case     ,						__snake_case						)
 return results, sampler.num_replicas
def 				UpperCAmelCase_					(							)     ->			Dict:







 """simple docstring"""


 _lowercase							      =argparse.ArgumentParser(
     epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''						)
 parser.add_argument('''--data_dir'''     ,						type=__snake_case     ,						help='''like cnn_dm/test.source'''						)
 parser.add_argument(
     '''--model_name'''     ,						type=__snake_case     ,						help='''like facebook/bart-large-cnn,t5-base, etc.'''     ,						default='''sshleifer/distilbart-xsum-12-3'''     ,						)
 parser.add_argument('''--save_dir'''     ,						type=__snake_case     ,						help='''where to save'''     ,						default='''tmp_gen'''						)
 parser.add_argument('''--max_source_length'''     ,						type=__snake_case     ,						default=__snake_case						)
 parser.add_argument(
     '''--type_path'''     ,						type=__snake_case     ,						default='''test'''     ,						help='''which subset to evaluate typically train/val/test'''						)
 parser.add_argument('''--task'''     ,						type=__snake_case     ,						default='''summarization'''     ,						help='''used for task_specific_params + metrics'''						)
 parser.add_argument('''--bs'''     ,						type=__snake_case     ,						default=8     ,						required=__snake_case     ,						help='''batch size'''						)
 parser.add_argument(
     '''--local_rank'''     ,						type=__snake_case     ,						default=-1     ,						required=__snake_case     ,						help='''should be passed by distributed.launch'''						)

 parser.add_argument(
     '''--n_obs'''     ,						type=__snake_case     ,						default=__snake_case     ,						required=__snake_case     ,						help='''How many observations. Defaults to all.'''						)
 parser.add_argument(
     '''--num_return_sequences'''     ,						type=__snake_case     ,						default=1     ,						required=__snake_case     ,						help='''How many sequences to return'''						)
 parser.add_argument(
     '''--sync_timeout'''     ,						type=__snake_case     ,						default=600     ,						required=__snake_case     ,						help='''How long should master process wait for other processes to finish.'''     ,						)
 parser.add_argument('''--src_lang'''     ,						type=__snake_case     ,						default=__snake_case     ,						required=__snake_case						)
 parser.add_argument('''--tgt_lang'''     ,						type=__snake_case     ,						default=__snake_case     ,						required=__snake_case						)
 parser.add_argument(
     '''--prefix'''     ,						type=__snake_case     ,						required=__snake_case     ,						default=__snake_case     ,						help='''will be added to the begininng of src examples'''						)
 parser.add_argument('''--fp16'''     ,						action='''store_true'''						)
 parser.add_argument('''--debug'''     ,						action='''store_true'''						)
 _lowercase							      =time.time()
 _lowercase      ,    _lowercase							      =parser.parse_known_args()
 _lowercase							      =parse_numeric_n_bool_cl_kwargs(__snake_case						)
 if generate_kwargs and args.local_rank <= 0:
  print(F"parsed the following generate kwargs: {generate_kwargs}"						)
 _lowercase							      =Path(args.save_dir + '''_tmp'''						)
 Path(__snake_case						).mkdir(exist_ok=__snake_case						)  # this handles locking.
 _lowercase							      =list(json_save_dir.glob('''rank_*.json'''						)						)
 if intermediate_files:
  raise ValueError(F"Found files at {json_save_dir} please move or remove them."						)
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 _lowercase							      ={}
 if args.src_lang is not None:
  _lowercase							      =args.src_lang
 if args.tgt_lang is not None:
  _lowercase							      =args.tgt_lang

 Path(args.save_dir						).mkdir(exist_ok=__snake_case						)
 _lowercase      ,    _lowercase							      =eval_data_dir(
     args.data_dir     ,						__snake_case     ,						args.model_name     ,						type_path=args.type_path     ,						bs=args.bs     ,						fpaa=args.fpaa     ,						task=args.task     ,						local_rank=args.local_rank     ,						n_obs=args.n_obs     ,						max_source_length=args.max_source_length     ,						num_return_sequences=args.num_return_sequences     ,						prefix=args.prefix     ,						dataset_kwargs=__snake_case     ,						**__snake_case     ,						)

 if args.local_rank <= 0:
  _lowercase							      =Path(args.save_dir						)
  save_dir.mkdir(exist_ok=__snake_case						)
  _lowercase							      =gather_results_from_each_node(__snake_case     ,						__snake_case     ,						args.sync_timeout						)
  _lowercase							      =combine_partial_results(__snake_case						)
  if args.num_return_sequences > 1:
   _lowercase							      =save_dir.joinpath('''pseudolabel_results.json'''						)
   print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"						)
   save_json(__snake_case     ,						__snake_case						)
   return
  _lowercase							      =Path(args.data_dir						).joinpath(args.type_path + '''.target'''						)
  with open(__snake_case						) as f:
   _lowercase							      =[x.rstrip() for x in f.readlines()][: len(__snake_case						)]

  # Calculate metrics, save metrics,  and save _generations.txt
  _lowercase							      ='''translation''' in args.task
  _lowercase							      =calculate_bleu if calc_bleu else calculate_rouge
  _lowercase							      ='''bleu''' if calc_bleu else '''rouge'''
  _lowercase							      =score_fn(__snake_case     ,						__snake_case						)
  _lowercase							      =len(__snake_case						)
  _lowercase							      =time.time() - start_time
  _lowercase							      =round(runtime / metrics['''n_obs''']     ,						4						)
  _lowercase							      =num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  _lowercase							      =save_dir.joinpath(F"{args.type_path}_{metric_name}.json"						)
  save_json(__snake_case     ,						__snake_case     ,						indent=__snake_case						)
  print(__snake_case						)
  write_txt_file(__snake_case     ,						save_dir.joinpath(F"{args.type_path}_generations.txt"						)						)
  if args.debug:
   write_txt_file(__snake_case     ,						save_dir.joinpath(F"{args.type_path}.target"						)						)
  else:
   shutil.rmtree(__snake_case						)
def 				UpperCAmelCase_					(							__snake_case						)     ->			List:







 """simple docstring"""


 _lowercase							      =[]
 for partial_result in partial_results:
  records.extend(__snake_case						)
 _lowercase							      =sorted(__snake_case     ,						key=lambda __snake_case						: x["id"]						)
 _lowercase							      =[x['''pred'''] for x in records]
 return preds
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Dict[str, List]]:







 """simple docstring"""


 _lowercase							      =time.time()
 logger.info('''waiting for all nodes to finish'''						)
 _lowercase							      =None
 while (time.time() - start_wait) < timeout:
  _lowercase							      =list(save_dir.glob('''rank_*.json'''						)						)
  if len(__snake_case						) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   _lowercase							      =lmap(__snake_case     ,						__snake_case						)
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('''Rank 0 gave up on waiting for other processes'''						)
 # Unreachable


if __name__ == "__main__":
  # Usage for MT:
  run_generate()





import comet  # From: unbabel-comet
import torch

import datasets


UpperCAmelCase__   	=							datasets.logging.get_logger(__name__)

UpperCAmelCase__   	=							'''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

UpperCAmelCase__   	=							'''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

UpperCAmelCase__   	=							'''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,							_KWARGS_DESCRIPTION)
class       lowerCamelCase__   (       datasets.Metric):





     def      __A					(self       )     ->   Optional[int]:
      return datasets.MetricInfo(
          description=_DESCRIPTION		,     citation=_CITATION		,     homepage='''https://unbabel.github.io/COMET/html/index.html'''		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
              {
                  '''sources''': datasets.Value('''string'''		,     id='''sequence'''       ),
                  '''predictions''': datasets.Value('''string'''		,     id='''sequence'''       ),
                  '''references''': datasets.Value('''string'''		,     id='''sequence'''       ),
              }       )		,     codebase_urls=['''https://github.com/Unbabel/COMET''']		,     reference_urls=[
              '''https://github.com/Unbabel/COMET''',
              '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
              '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
          ]		,     )





     def      __A					(self		,     UpperCAmelCase       )     ->   Dict:
      if self.config_name == "default":
       _lowercase							      =comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
      else:
       _lowercase							      =comet.load_from_checkpoint(comet.download_model(self.config_name       )       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=False       )     ->   int:
      if gpus is None:
       _lowercase							      =1 if torch.cuda.is_available() else 0
      _lowercase							      ={'''src''': sources, '''mt''': predictions, '''ref''': references}
      _lowercase							      =[dict(zip(UpperCAmelCase		,     UpperCAmelCase       )       ) for t in zip(*data.values()       )]
      _lowercase      ,    _lowercase							      =self.scorer.predict(UpperCAmelCase		,     gpus=UpperCAmelCase		,     progress_bar=UpperCAmelCase       )
      return {"mean_score": mean_score, "scores": scores}

