






import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


_SCREAMING_SNAKE_CASE   =  datasets.logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   =  "\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n    author = \"Moosavi, Nafise Sadat  and\n      Strube, Michael\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1060\",\n    doi = \"10.18653/v1/P16-1060\",\n    pages = \"632--642\",\n}\n\n"

_SCREAMING_SNAKE_CASE   =  "\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"

_SCREAMING_SNAKE_CASE   =  "\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting 'keep_singletons=False', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    'mentions': mentions\n    'muc': MUC metric [Vilain et al, 1995]\n    'bcub': B-cubed [Bagga and Baldwin, 1998]\n    'ceafe': CEAFe [Luo et al., 2005]\n    'lea': LEA [Moosavi and Strube, 2016]\n    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric('coval')\n    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',\n    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',\n    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',\n    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',\n    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',\n    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"





def        _snake_case   (_snake_case				:       str      ,    _snake_case				:       Tuple      ,    _snake_case				:       Any=False      ,    _snake_case				:       List[Any]=False      ,    _snake_case				:       Union[str, Any]=True      ,    _snake_case				:       Optional[Any]=False      ,    _snake_case				:       List[Any]="dummy_doc")					->	int:
   _lowercase              ={doc: key_lines}
   _lowercase              ={doc: sys_lines}

   _lowercase              ={}

   _lowercase              =0
   _lowercase              =0
   _lowercase              =0
   _lowercase              =0
   _lowercase              =0
   _lowercase              =0

   _lowercase       ,       _lowercase              =reader.get_doc_mentions(_snake_case      ,    key_doc_lines[doc]      ,    _snake_case)
   key_singletons_num += singletons_num

   if NP_only or min_span:
      _lowercase              =reader.set_annotated_parse_trees(_snake_case      ,    key_doc_lines[doc]      ,    _snake_case      ,    _snake_case)

   _lowercase       ,       _lowercase              =reader.get_doc_mentions(_snake_case      ,    sys_doc_lines[doc]      ,    _snake_case)
   sys_singletons_num += singletons_num

   if NP_only or min_span:
      _lowercase              =reader.set_annotated_parse_trees(_snake_case      ,    key_doc_lines[doc]      ,    _snake_case      ,    _snake_case)

   if remove_nested:
      _lowercase       ,       _lowercase              =reader.remove_nested_coref_mentions(_snake_case      ,    _snake_case)
      key_nested_coref_num += nested_mentions
      key_removed_nested_clusters += removed_clusters

      _lowercase       ,       _lowercase              =reader.remove_nested_coref_mentions(_snake_case      ,    _snake_case)
      sys_nested_coref_num += nested_mentions
      sys_removed_nested_clusters += removed_clusters

   _lowercase              =reader.get_mention_assignments(_snake_case      ,    _snake_case)
   _lowercase              =reader.get_mention_assignments(_snake_case      ,    _snake_case)

   _lowercase              =(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

   if remove_nested:
      logger.info(
          'Number of removed nested coreferring mentions in the key '
          f'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}''')
      logger.info(
          'Number of resulting singleton clusters in the key '
          f'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}''')

   if not keep_singletons:
      logger.info(
          f'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
          'files, respectively')

   return doc_coref_infos





def        _snake_case   (_snake_case				:       Union[str, Any]      ,    _snake_case				:       Dict      ,    _snake_case				:       str      ,    _snake_case				:       List[str]      ,    _snake_case				:       str      ,    _snake_case				:       List[Any]      ,    _snake_case				:       Tuple)					->	Any:
   _lowercase              =get_coref_infos(_snake_case      ,    _snake_case      ,    _snake_case      ,    _snake_case      ,    _snake_case      ,    _snake_case)

   _lowercase              ={}
   _lowercase              =0
   _lowercase              =0

   for name, metric in metrics:
      _lowercase       ,       _lowercase       ,       _lowercase              =evaluator.evaluate_documents(_snake_case      ,    _snake_case      ,    beta=1)
      if name in ["muc", "bcub", "ceafe"]:
         conll += fa
         conll_subparts_num += 1
      output_scores.update({f'''{name}/recall''': recall, f'''{name}/precision''': precision, f'''{name}/f1''': fa})

      logger.info(
          name.ljust(10)      ,    f'''Recall: {recall * 100:.2f}'''      ,    f''' Precision: {precision * 100:.2f}'''      ,    f''' F1: {fa * 100:.2f}'''      ,    )

   if conll_subparts_num == 3:
      _lowercase              =(conll / 3) * 100
      logger.info(f'''CoNLL score: {conll:.2f}''')
      output_scores.update({'conll_score': conll})

   return output_scores





def        _snake_case   (_snake_case				:       Optional[int])					->	Tuple:
   _lowercase              =False
   for line in key_lines:
      if not line.startswith('#'):
         if len(line.split()) > 6:
            _lowercase              =line.split()[5]
            if not parse_col == "-":
               _lowercase              =True
               break
            else:
               break
   return has_gold_parse






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,						_KWARGS_DESCRIPTION )
class 					SCREAMING_SNAKE_CASE_  (       datasets.Metric ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          return datasets.MetricInfo(
              description=_DESCRIPTION,      citation=_CITATION,      inputs_description=_KWARGS_DESCRIPTION,      features=datasets.Features(
                  {
                      'predictions': datasets.Sequence(datasets.Value('string')),
                      'references': datasets.Sequence(datasets.Value('string')),
                  }),      codebase_urls=['https://github.com/ns-moosavi/coval'],      reference_urls=[
                  'https://github.com/ns-moosavi/coval',
                  'https://www.aclweb.org/anthology/P16-1060',
                  'http://www.conll.cemantix.org/2012/data.html',
              ],      )





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Optional[int],      snake_case	:List[Any],      snake_case	:Optional[int]=True,      snake_case	:Optional[int]=False,      snake_case	:Optional[Any]=False,      snake_case	:List[str]=False):



          """simple docstring"""

          _lowercase              =[
              ('mentions', evaluator.mentions),
              ('muc', evaluator.muc),
              ('bcub', evaluator.b_cubed),
              ('ceafe', evaluator.ceafe),
              ('lea', evaluator.lea),
          ]

          if min_span:
             _lowercase              =util.check_gold_parse_annotation(snake_case)
             if not has_gold_parse:
                raise NotImplementedError('References should have gold parse annotation to use \'min_span\'.')
                # util.parse_key_file(key_file)
                # key_file = key_file + ".parsed"

          _lowercase              =evaluate(
              key_lines=snake_case,      sys_lines=snake_case,      metrics=snake_case,      NP_only=snake_case,      remove_nested=snake_case,      keep_singletons=snake_case,      min_span=snake_case,      )

          return score










import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_SCREAMING_SNAKE_CASE   =  logging.getLogger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		int					='''summarization'''
       __lowerCAmelCase    :		str					=['''loss''']
       __lowerCAmelCase    :		Dict					=ROUGE_KEYS
       __lowerCAmelCase    :		str					='''rouge2'''





       def __init__(				self	:Dict,      snake_case	:List[Any],      **snake_case	:Tuple):



          """simple docstring"""

          if hparams.sortish_sampler and hparams.gpus > 1:
             _lowercase              =False
          elif hparams.max_tokens_per_batch is not None:
             if hparams.gpus > 1:
                raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training')
             if hparams.sortish_sampler:
                raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously')

          super().__init__(snake_case,      num_labels=snake_case,      mode=self.mode,      **snake_case)
          use_task_specific_params(self.model,      'summarization')
          save_git_info(self.hparams.output_dir)
          _lowercase              =Path(self.output_dir) / 'metrics.json'
          _lowercase              =Path(self.output_dir) / 'hparams.pkl'
          pickle_save(self.hparams,      self.hparams_save_path)
          _lowercase              =0
          _lowercase              =defaultdict(snake_case)
          _lowercase              =self.config.model_type
          _lowercase              =self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

          _lowercase              ={
              "data_dir": self.hparams.data_dir,
              "max_source_length": self.hparams.max_source_length,
              "prefix": self.model.config.prefix or "",
          }
          _lowercase              ={
              'train': self.hparams.n_train,
              'val': self.hparams.n_val,
              'test': self.hparams.n_test,
          }
          _lowercase              ={k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

          _lowercase              ={
              'train': self.hparams.max_target_length,
              'val': self.hparams.val_max_target_length,
              'test': self.hparams.test_max_target_length,
          }
          assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
          assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
          if self.hparams.freeze_embeds:
             freeze_embeds(self.model)
          if self.hparams.freeze_encoder:
             freeze_params(self.model.get_encoder())
             assert_all_frozen(self.model.get_encoder())

          _lowercase              =get_git_info()['repo_sha']
          _lowercase              =hparams.num_workers
          _lowercase              =None  # default to config
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer,      snake_case):
             _lowercase              =self.tokenizer.lang_code_to_id[hparams.tgt_lang]
             _lowercase              =self.decoder_start_token_id
          _lowercase              =(
              SeqaSeqDataset if hasattr(self.tokenizer,      'prepare_seq2seq_batch') else LegacySeqaSeqDataset
          )
          _lowercase              =False
          _lowercase              =self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
          if self.hparams.eval_max_gen_length is not None:
             _lowercase              =self.hparams.eval_max_gen_length
          else:
             _lowercase              =self.model.config.max_length
          _lowercase              =self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Dict[str, torch.Tensor]):



          """simple docstring"""

          _lowercase              ={
              k: self.tokenizer.batch_decode(v.tolist()) if 'mask' not in k else v.shape for k, v in batch.items()
          }
          save_json(snake_case,      Path(self.output_dir) / 'text_batch.json')
          save_json({k: v.tolist() for k, v in batch.items()},      Path(self.output_dir) / 'tok_batch.json')

          _lowercase              =True
          return readable_batch





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[str],      **snake_case	:List[Any]):



          """simple docstring"""

          return self.model(snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:List[int]):



          """simple docstring"""

          _lowercase              =self.tokenizer.batch_decode(
              snake_case,      skip_special_tokens=snake_case,      clean_up_tokenization_spaces=snake_case)
          return lmap(str.strip,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:dict):



          """simple docstring"""

          _lowercase              =self.tokenizer.pad_token_id
          _lowercase       ,       _lowercase              =batch['input_ids'], batch['attention_mask']
          _lowercase              =batch['labels']
          if isinstance(self.model,      snake_case):
             _lowercase              =self.model._shift_right(snake_case)
          else:
             _lowercase              =shift_tokens_right(snake_case,      snake_case)
          if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
             _lowercase              =decoder_input_ids
             self.save_readable_batch(snake_case)

          _lowercase              =self(snake_case,      attention_mask=snake_case,      decoder_input_ids=snake_case,      use_cache=snake_case)
          _lowercase              =outputs['logits']
          if self.hparams.label_smoothing == 0:
             # Same behavior as modeling_bart.py, besides ignoring pad_token_id
             _lowercase              =nn.CrossEntropyLoss(ignore_index=snake_case)

             assert lm_logits.shape[-1] == self.vocab_size
             _lowercase              =ce_loss_fct(lm_logits.view(-1,      lm_logits.shape[-1]),      tgt_ids.view(-1))
          else:
             _lowercase              =nn.functional.log_softmax(snake_case,      dim=-1)
             _lowercase       ,       _lowercase              =label_smoothed_nll_loss(
                 snake_case,      snake_case,      self.hparams.label_smoothing,      ignore_index=snake_case)
          return (loss,)





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.tokenizer.pad_token_id





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Dict,      snake_case	:str):



          """simple docstring"""

          _lowercase              =self._step(snake_case)

          _lowercase              =dict(zip(self.loss_names,      snake_case))
          # tokens per batch
          _lowercase              =batch['input_ids'].ne(self.pad).sum() + batch['labels'].ne(self.pad).sum()
          _lowercase              =batch['input_ids'].shape[0]
          _lowercase              =batch['input_ids'].eq(self.pad).sum()
          _lowercase              =batch['input_ids'].eq(self.pad).float().mean()
          # TODO(SS): make a wandb summary metric for this
          return {"loss": loss_tensors[0], "log": logs}





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Dict,      snake_case	:List[Any]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:List[str],      snake_case	:str="val"):



          """simple docstring"""

          self.step_count += 1
          _lowercase              ={k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
          _lowercase              =losses['loss']
          _lowercase              ={
              k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['gen_time', 'gen_len']
          }
          _lowercase              =(
              generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
          )
          _lowercase              =torch.tensor(snake_case).type_as(snake_case)
          generative_metrics.update({k: v.item() for k, v in losses.items()})
          losses.update(snake_case)
          _lowercase              ={f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
          _lowercase              =self.step_count
          self.metrics[prefix].append(snake_case)  # callback writes this to self.metrics_save_path
          _lowercase              =flatten_list([x['preds'] for x in outputs])
          return {
              "log": all_metrics,
              "preds": preds,
              f'''{prefix}_loss''': loss,
              f'''{prefix}_{self.val_metric}''': metric_tensor,
          }





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:str,      snake_case	:List[str]):



          """simple docstring"""

          return calculate_rouge(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:str,      snake_case	:dict):



          """simple docstring"""

          _lowercase              =time.time()

          # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
          _lowercase              =self.model.generate(
              batch['input_ids'],      attention_mask=batch['attention_mask'],      use_cache=snake_case,      decoder_start_token_id=self.decoder_start_token_id,      num_beams=self.eval_beams,      max_length=self.eval_max_length,      )
          _lowercase              =(time.time() - ta) / batch['input_ids'].shape[0]
          _lowercase              =self.ids_to_clean_text(snake_case)
          _lowercase              =self.ids_to_clean_text(batch['labels'])
          _lowercase              =self._step(snake_case)
          _lowercase              =dict(zip(self.loss_names,      snake_case))
          _lowercase              =self.calc_generative_metrics(snake_case,      snake_case)
          _lowercase              =np.mean(lmap(snake_case,      snake_case))
          base_metrics.update(gen_time=snake_case,      gen_len=snake_case,      preds=snake_case,      target=snake_case,      **snake_case)
          return base_metrics





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:str,      snake_case	:Optional[int]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:List[str]):



          """simple docstring"""

          return self.validation_epoch_end(snake_case,      prefix='test')





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:str):



          """simple docstring"""

          _lowercase              =self.n_obs[type_path]
          _lowercase              =self.target_lens[type_path]
          _lowercase              =self.dataset_class(
              self.tokenizer,      type_path=snake_case,      n_obs=snake_case,      max_target_length=snake_case,      **self.dataset_kwargs,      )
          return dataset





       def 				UpperCamelCase__						(				self	:str,      snake_case	:str,      snake_case	:int,      snake_case	:bool = False):



          """simple docstring"""

          _lowercase              =self.get_dataset(snake_case)

          if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
             _lowercase              =dataset.make_sortish_sampler(snake_case,      distributed=self.hparams.gpus > 1)
             return DataLoader(
                 snake_case,      batch_size=snake_case,      collate_fn=dataset.collate_fn,      shuffle=snake_case,      num_workers=self.num_workers,      sampler=snake_case,      )

          elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
             _lowercase              =dataset.make_dynamic_sampler(
                 self.hparams.max_tokens_per_batch,      distributed=self.hparams.gpus > 1)
             return DataLoader(
                 snake_case,      batch_sampler=snake_case,      collate_fn=dataset.collate_fn,      num_workers=self.num_workers,      )
          else:
             return DataLoader(
                 snake_case,      batch_size=snake_case,      collate_fn=dataset.collate_fn,      shuffle=snake_case,      num_workers=self.num_workers,      sampler=snake_case,      )





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.get_dataloader('train',      batch_size=self.hparams.train_batch_size,      shuffle=snake_case)
          return dataloader





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.get_dataloader('val',      batch_size=self.hparams.eval_batch_size)





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          return self.get_dataloader('test',      batch_size=self.hparams.eval_batch_size)





       @staticmethod
       def 				UpperCamelCase__						(				snake_case	:Dict,      snake_case	:List[str]):



          """simple docstring"""

          BaseTransformer.add_model_specific_args(snake_case,      snake_case)
          add_generic_args(snake_case,      snake_case)
          parser.add_argument(
              '--max_source_length',      default=1024,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--max_target_length',      default=56,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--val_max_target_length',      default=142,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--test_max_target_length',      default=142,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument('--freeze_encoder',      action='store_true')
          parser.add_argument('--freeze_embeds',      action='store_true')
          parser.add_argument('--sortish_sampler',      action='store_true',      default=snake_case)
          parser.add_argument('--overwrite_output_dir',      action='store_true',      default=snake_case)
          parser.add_argument('--max_tokens_per_batch',      type=snake_case,      default=snake_case)
          parser.add_argument('--logger_name',      type=snake_case,      choices=['default', 'wandb', 'wandb_shared'],      default='default')
          parser.add_argument('--n_train',      type=snake_case,      default=-1,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--n_val',      type=snake_case,      default=500,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--n_test',      type=snake_case,      default=-1,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument(
              '--task',      type=snake_case,      default='summarization',      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--label_smoothing',      type=snake_case,      default=0.0,      required=snake_case)
          parser.add_argument('--src_lang',      type=snake_case,      default='',      required=snake_case)
          parser.add_argument('--tgt_lang',      type=snake_case,      default='',      required=snake_case)
          parser.add_argument('--eval_beams',      type=snake_case,      default=snake_case,      required=snake_case)
          parser.add_argument(
              '--val_metric',      type=snake_case,      default=snake_case,      required=snake_case,      choices=['bleu', 'rouge2', 'loss', None])
          parser.add_argument('--eval_max_gen_length',      type=snake_case,      default=snake_case,      help='never generate more than n tokens')
          parser.add_argument('--save_top_k',      type=snake_case,      default=1,      required=snake_case,      help='How many checkpoints to save')
          parser.add_argument(
              '--early_stopping_patience',      type=snake_case,      default=-1,      required=snake_case,      help=(
                  '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
                  ' val_check_interval will effect it.'
              ),      )
          return parser






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[str, Any]					='''translation'''
       __lowerCAmelCase    :		Any					=['''loss''']
       __lowerCAmelCase    :		Optional[int]					=['''bleu''']
       __lowerCAmelCase    :		Any					='''bleu'''





       def __init__(				self	:str,      snake_case	:Union[str, Any],      **snake_case	:Any):



          """simple docstring"""

          super().__init__(snake_case,      **snake_case)
          _lowercase              =hparams.src_lang
          _lowercase              =hparams.tgt_lang





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:Union[str, Any],      snake_case	:Any):



          """simple docstring"""

          return calculate_bleu(snake_case,      snake_case)





def        _snake_case   (_snake_case				:       Dict      ,    _snake_case				:       int=None)					->	SummarizationModule:
   Path(args.output_dir).mkdir(exist_ok=_snake_case)
   check_output_dir(_snake_case      ,    expected_items=3)

   if model is None:
      if "summarization" in args.task:
         _lowercase              =SummarizationModule(_snake_case)
      else:
         _lowercase              =TranslationModule(_snake_case)
   _lowercase              =Path(args.data_dir).name
   if (
       args.logger_name == "default"
       or args.fast_dev_run
       or str(args.output_dir).startswith('/tmp')
       or str(args.output_dir).startswith('/var')
   ):
      _lowercase              =True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      _lowercase              =os.environ.get('WANDB_PROJECT'      ,    _snake_case)
      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=_snake_case)

   elif args.logger_name == "wandb_shared":
      from pytorch_lightning.loggers import WandbLogger

      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=f'''hf_{dataset}''')

   if args.early_stopping_patience >= 0:
      _lowercase              =get_early_stopping_callback(model.val_metric      ,    args.early_stopping_patience)
   else:
      _lowercase              =False

   _lowercase              =args.val_metric == 'loss'
   _lowercase              =generic_train(
       _snake_case      ,    _snake_case      ,    logging_callback=SeqaSeqLoggingCallback()      ,    checkpoint_callback=get_checkpoint_callback(
           args.output_dir      ,    model.val_metric      ,    args.save_top_k      ,    _snake_case)      ,    early_stopping_callback=_snake_case      ,    logger=_snake_case      ,    )
   pickle_save(model.hparams      ,    model.output_dir / 'hparams.pkl')
   if not args.do_predict:
      return model

   _lowercase              =''
   _lowercase              =sorted(glob.glob(os.path.join(args.output_dir      ,    '*.ckpt')      ,    recursive=_snake_case))
   if checkpoints:
      _lowercase              =checkpoints[-1]
      _lowercase              =checkpoints[-1]
   trainer.logger.log_hyperparams(model.hparams)

   # test() without a model tests using the best checkpoint automatically
   trainer.test()
   return model


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  argparse.ArgumentParser()
    _SCREAMING_SNAKE_CASE   =  pl.Trainer.add_argparse_args(parser)
    _SCREAMING_SNAKE_CASE   =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

    _SCREAMING_SNAKE_CASE   =  parser.parse_args()

    main(args)


