"""simple docstring"""




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


_a				:		Dict	   =       logging.getLogger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      str		 =		"summarization"
     _UpperCamelCase							:      Union[str, Any]		 =		["loss"]
     _UpperCamelCase							:      Tuple		 =		ROUGE_KEYS
     _UpperCamelCase							:      Any		 =		"rouge2"






     def __init__(		self    ,					a__    ,					**a__  ):
       if hparams.sortish_sampler and hparams.gpus > 1:
         _lowerCAmelCase						:	str        =					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__(a__    ,					num_labels=a__    ,					mode=self.mode    ,					**a__  )
       use_task_specific_params(self.model    ,					"""summarization"""  )
       save_git_info(self.hparams.output_dir  )
       _lowerCAmelCase						:	Optional[Any]        =					Path(self.output_dir  ) / """metrics.json"""
       _lowerCAmelCase						:	Any        =					Path(self.output_dir  ) / """hparams.pkl"""
       pickle_save(self.hparams    ,					self.hparams_save_path  )
       _lowerCAmelCase						:	Any        =					0
       _lowerCAmelCase						:	List[str]        =					defaultdict(a__  )
       _lowerCAmelCase						:	str        =					self.config.model_type
       _lowerCAmelCase						:	Optional[Any]        =					self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

       _lowerCAmelCase						:	Optional[Any]        =					{
           """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()  )

       _lowerCAmelCase						:	Union[str, Any]        =					get_git_info()["""repo_sha"""]
       _lowerCAmelCase						:	Any        =					hparams.num_workers
       _lowerCAmelCase						:	Any        =					None  # default to config
       if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer    ,					a__  ):
         _lowerCAmelCase						:	List[Any]        =					self.tokenizer.lang_code_to_id[hparams.tgt_lang]
         _lowerCAmelCase						:	Optional[int]        =					self.decoder_start_token_id
       _lowerCAmelCase						:	Dict        =					(
           SeqaSeqDataset if hasattr(self.tokenizer    ,					"""prepare_seq2seq_batch"""  ) else LegacySeqaSeqDataset
       )
       _lowerCAmelCase						:	Optional[Any]        =					False
       _lowerCAmelCase						:	Union[str, Any]        =					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:
         _lowerCAmelCase						:	Tuple        =					self.hparams.eval_max_gen_length
       else:
         _lowerCAmelCase						:	Any        =					self.model.config.max_length
       _lowerCAmelCase						:	List[Any]        =					self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	str        =					{
           k: self.tokenizer.batch_decode(v.tolist()  ) if """mask""" not in k else v.shape for k, v in batch.items()
       }
       save_json(a__    ,					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"""  )

       _lowerCAmelCase						:	Optional[int]        =					True
       return readable_batch






     def    __A  (		self    ,					a__    ,					**a__  ):
       return self.model(a__    ,					**a__  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	List[str]        =					self.tokenizer.batch_decode(
           a__    ,					skip_special_tokens=a__    ,					clean_up_tokenization_spaces=a__  )
       return lmap(str.strip    ,					a__  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					self.tokenizer.pad_token_id
       _lowerCAmelCase      ,     _lowerCAmelCase						:	List[Any]        =					batch["""input_ids"""], batch["""attention_mask"""]
       _lowerCAmelCase						:	List[Any]        =					batch["""labels"""]
       if isinstance(self.model    ,					a__  ):
         _lowerCAmelCase						:	Any        =					self.model._shift_right(a__  )
       else:
         _lowerCAmelCase						:	Tuple        =					shift_tokens_right(a__    ,					a__  )
       if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
         _lowerCAmelCase						:	Tuple        =					decoder_input_ids
         self.save_readable_batch(a__  )

       _lowerCAmelCase						:	List[Any]        =					self(a__    ,					attention_mask=a__    ,					decoder_input_ids=a__    ,					use_cache=a__  )
       _lowerCAmelCase						:	Any        =					outputs["""logits"""]
       if self.hparams.label_smoothing == 0:
         # Same behavior as modeling_bart.py, besides ignoring pad_token_id
         _lowerCAmelCase						:	Union[str, Any]        =					nn.CrossEntropyLoss(ignore_index=a__  )

         assert lm_logits.shape[-1] == self.vocab_size
         _lowerCAmelCase						:	Any        =					ce_loss_fct(lm_logits.view(-1    ,					lm_logits.shape[-1]  )    ,					tgt_ids.view(-1  )  )
       else:
         _lowerCAmelCase						:	List[str]        =					nn.functional.log_softmax(a__    ,					dim=-1  )
         _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					label_smoothed_nll_loss(
             a__    ,					a__    ,					self.hparams.label_smoothing    ,					ignore_index=a__  )
       return (loss,)






     @property
     def    __A  (		self  ):
       return self.tokenizer.pad_token_id






     def    __A  (		self    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Union[str, Any]        =					self._step(a__  )

       _lowerCAmelCase						:	Tuple        =					dict(zip(self.loss_names    ,					a__  )  )
       # tokens per batch
       _lowerCAmelCase						:	Dict        =					batch["""input_ids"""].ne(self.pad  ).sum() + batch["""labels"""].ne(self.pad  ).sum()
       _lowerCAmelCase						:	Any        =					batch["""input_ids"""].shape[0]
       _lowerCAmelCase						:	Dict        =					batch["""input_ids"""].eq(self.pad  ).sum()
       _lowerCAmelCase						:	Dict        =					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    __A  (		self    ,					a__    ,					a__  ):
       return self._generative_step(a__  )






     def    __A  (		self    ,					a__    ,					a__="val"  ):
       self.step_count += 1
       _lowerCAmelCase						:	Dict        =					{k: torch.stack([x[k] for x in outputs]  ).mean() for k in self.loss_names}
       _lowerCAmelCase						:	str        =					losses["""loss"""]
       _lowerCAmelCase						:	List[Any]        =					{
           k: np.array([x[k] for x in outputs]  ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
       }
       _lowerCAmelCase						:	str        =					(
           generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
       )
       _lowerCAmelCase						:	torch.FloatTensor        =					torch.tensor(a__  ).type_as(a__  )
       generative_metrics.update({k: v.item() for k, v in losses.items()}  )
       losses.update(a__  )
       _lowerCAmelCase						:	Any        =					{F"{prefix}_avg_{k}": x for k, x in losses.items()}
       _lowerCAmelCase						:	Optional[int]        =					self.step_count
       self.metrics[prefix].append(a__  )  # callback writes this to self.metrics_save_path
       _lowerCAmelCase						:	str        =					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    __A  (		self    ,					a__    ,					a__  ):
       return calculate_rouge(a__    ,					a__  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					time.time()

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






     def    __A  (		self    ,					a__    ,					a__  ):
       return self._generative_step(a__  )






     def    __A  (		self    ,					a__  ):
       return self.validation_epoch_end(a__    ,					prefix="""test"""  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Dict        =					self.n_obs[type_path]
       _lowerCAmelCase						:	Union[str, Any]        =					self.target_lens[type_path]
       _lowerCAmelCase						:	Tuple        =					self.dataset_class(
           self.tokenizer    ,					type_path=a__    ,					n_obs=a__    ,					max_target_length=a__    ,					**self.dataset_kwargs    ,					)
       return dataset






     def    __A  (		self    ,					a__    ,					a__    ,					a__ = False  ):
       _lowerCAmelCase						:	List[Any]        =					self.get_dataset(a__  )

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

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					self.get_dataloader("""train"""    ,					batch_size=self.hparams.train_batch_size    ,					shuffle=a__  )
       return dataloader






     def    __A  (		self  ):
       return self.get_dataloader("""val"""    ,					batch_size=self.hparams.eval_batch_size  )






     def    __A  (		self  ):
       return self.get_dataloader("""test"""    ,					batch_size=self.hparams.eval_batch_size  )






     @staticmethod
     def    __A  (		a__    ,					a__  ):
       BaseTransformer.add_model_specific_args(a__    ,					a__  )
       add_generic_args(a__    ,					a__  )
       parser.add_argument(
           """--max_source_length"""    ,					default=1024    ,					type=a__    ,					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=a__    ,					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=a__    ,					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=a__    ,					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=a__  )
       parser.add_argument("""--overwrite_output_dir"""    ,					action="""store_true"""    ,					default=a__  )
       parser.add_argument("""--max_tokens_per_batch"""    ,					type=a__    ,					default=a__  )
       parser.add_argument("""--logger_name"""    ,					type=a__    ,					choices=["""default""", """wandb""", """wandb_shared"""]    ,					default="""default"""  )
       parser.add_argument("""--n_train"""    ,					type=a__    ,					default=-1    ,					required=a__    ,					help="""# examples. -1 means use all."""  )
       parser.add_argument("""--n_val"""    ,					type=a__    ,					default=500    ,					required=a__    ,					help="""# examples. -1 means use all."""  )
       parser.add_argument("""--n_test"""    ,					type=a__    ,					default=-1    ,					required=a__    ,					help="""# examples. -1 means use all."""  )
       parser.add_argument(
           """--task"""    ,					type=a__    ,					default="""summarization"""    ,					required=a__    ,					help="""# examples. -1 means use all."""  )
       parser.add_argument("""--label_smoothing"""    ,					type=a__    ,					default=0.0    ,					required=a__  )
       parser.add_argument("""--src_lang"""    ,					type=a__    ,					default=""""""    ,					required=a__  )
       parser.add_argument("""--tgt_lang"""    ,					type=a__    ,					default=""""""    ,					required=a__  )
       parser.add_argument("""--eval_beams"""    ,					type=a__    ,					default=a__    ,					required=a__  )
       parser.add_argument(
           """--val_metric"""    ,					type=a__    ,					default=a__    ,					required=a__    ,					choices=["""bleu""", """rouge2""", """loss""", None]  )
       parser.add_argument("""--eval_max_gen_length"""    ,					type=a__    ,					default=a__    ,					help="""never generate more than n tokens"""  )
       parser.add_argument("""--save_top_k"""    ,					type=a__    ,					default=1    ,					required=a__    ,					help="""How many checkpoints to save"""  )
       parser.add_argument(
           """--early_stopping_patience"""    ,					type=a__    ,					default=-1    ,					required=a__    ,					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 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[Any]		 =		"translation"
     _UpperCamelCase							:      Optional[int]		 =		["loss"]
     _UpperCamelCase							:      Optional[Any]		 =		["bleu"]
     _UpperCamelCase							:      List[str]		 =		"bleu"






     def __init__(		self    ,					a__    ,					**a__  ):
       super().__init__(a__    ,					**a__  )
       _lowerCAmelCase						:	Optional[int]        =					hparams.src_lang
       _lowerCAmelCase						:	str        =					hparams.tgt_lang






     def    __A  (		self    ,					a__    ,					a__  ):
       return calculate_bleu(a__    ,					a__  )







def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	Union[str, Any]=None     )	->				SummarizationModule:
  Path(args.output_dir     ).mkdir(exist_ok=_lowerCamelCase     )
  check_output_dir(_lowerCamelCase      ,expected_items=3     )

  if model is None:
    if "summarization" in args.task:
      _lowerCAmelCase						:	SummarizationModule        =					SummarizationModule(_lowerCamelCase     )
    else:
      _lowerCAmelCase						:	SummarizationModule        =					TranslationModule(_lowerCamelCase     )
  _lowerCAmelCase						:	List[Any]        =					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"""     )
  ):
    _lowerCAmelCase						:	Union[str, Any]        =					True  # don't pollute wandb logs unnecessarily
  elif args.logger_name == "wandb":
    from pytorch_lightning.loggers import WandbLogger

    _lowerCAmelCase						:	str        =					os.environ.get("""WANDB_PROJECT"""      ,_lowerCamelCase     )
    _lowerCAmelCase						:	Tuple        =					WandbLogger(name=model.output_dir.name      ,project=_lowerCamelCase     )

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

    _lowerCAmelCase						:	Dict        =					WandbLogger(name=model.output_dir.name      ,project=f"hf_{dataset}"     )

  if args.early_stopping_patience >= 0:
    _lowerCAmelCase						:	int        =					get_early_stopping_callback(model.val_metric      ,args.early_stopping_patience     )
  else:
    _lowerCAmelCase						:	Optional[Any]        =					False

  _lowerCAmelCase						:	Dict        =					args.val_metric == """loss"""
  _lowerCAmelCase						:	pl.Trainer        =					generic_train(
      _lowerCamelCase      ,_lowerCamelCase      ,logging_callback=SeqaSeqLoggingCallback()      ,checkpoint_callback=get_checkpoint_callback(
          args.output_dir      ,model.val_metric      ,args.save_top_k      ,_lowerCamelCase     )      ,early_stopping_callback=_lowerCamelCase      ,logger=_lowerCamelCase      ,)
  pickle_save(model.hparams      ,model.output_dir / """hparams.pkl"""     )
  if not args.do_predict:
    return model

  _lowerCAmelCase						:	List[Any]        =					""""""
  _lowerCAmelCase						:	List[str]        =					sorted(glob.glob(os.path.join(args.output_dir      ,"""*.ckpt"""     )      ,recursive=_lowerCamelCase     )     )
  if checkpoints:
    _lowerCAmelCase						:	str        =					checkpoints[-1]
    _lowerCAmelCase						:	Optional[int]        =					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__":
     _a				:		Dict	   =       argparse.ArgumentParser()
     _a				:		Optional[Any]	   =       pl.Trainer.add_argparse_args(parser)
     _a				:		Any	   =       SummarizationModule.add_model_specific_args(parser, os.getcwd())

     _a				:		Optional[int]	   =       parser.parse_args()

     main(args)





"""simple docstring"""




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_a				:		Optional[int]	   =       'bert-base-cased'
_a				:		Optional[Any]	   =       'google/pegasus-xsum'
_a				:		Union[str, Any]	   =       [' Sam ate lunch today.', 'Sams lunch ingredients.']
_a				:		int	   =       ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_a				:		Union[str, Any]	   =       'patrickvonplaten/t5-tiny-random'
_a				:		Tuple	   =       'sshleifer/bart-tiny-random'
_a				:		str	   =       'sshleifer/tiny-mbart'
_a				:		Optional[int]	   =       'sshleifer/tiny-marian-en-de'





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Path      ,_lowerCamelCase						:	list     )	->				str:
  _lowerCAmelCase						:	List[Any]        =					"""\n""".join(_lowerCamelCase     )
  Path(_lowerCamelCase     ).open("""w"""     ).writelines(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]     )	->				Union[str, Any]:
  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(_lowerCamelCase      ,f"{split}.source"     )      ,_lowerCamelCase     )
    _dump_articles(os.path.join(_lowerCamelCase      ,f"{split}.target"     )      ,_lowerCamelCase     )
  return tmp_dir






class 						__A							(			SCREAMING_SNAKE_CASE_		):






     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,					)
     @slow
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	str        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Any        =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       _lowerCAmelCase						:	Union[str, Any]        =					max(len(tokenizer.encode(a__  )  ) for a in ARTICLES  )
       _lowerCAmelCase						:	Optional[int]        =					max(len(tokenizer.encode(a__  )  ) for a in SUMMARIES  )
       _lowerCAmelCase						:	str        =					4
       _lowerCAmelCase						:	Optional[int]        =					8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[int]        =					"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
       _lowerCAmelCase						:	Optional[int]        =					SeqaSeqDataset(
           a__    ,					data_dir=a__    ,					type_path="""train"""    ,					max_source_length=a__    ,					max_target_length=a__    ,					src_lang=a__    ,					tgt_lang=a__    ,					)
       _lowerCAmelCase						:	int        =					DataLoader(a__    ,					batch_size=2    ,					collate_fn=train_dataset.collate_fn  )
       for batch in dataloader:
         assert isinstance(a__    ,					a__  )
         assert batch["attention_mask"].shape == batch["input_ids"].shape
         # show that articles were trimmed.
         assert batch["input_ids"].shape[1] == max_src_len
         # show that targets are the same len
         assert batch["labels"].shape[1] == max_tgt_len
         if tok_name != MBART_TINY:
           continue
         # check language codes in correct place
         _lowerCAmelCase						:	Any        =					shift_tokens_right(batch["""labels"""]    ,					tokenizer.pad_token_id  )
         assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
         assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
         assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
         assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

         break  # No need to test every batch






     @parameterized.expand([BART_TINY, BERT_BASE_CASED]  )
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Union[str, Any]        =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       _lowerCAmelCase						:	Optional[int]        =					max(len(tokenizer.encode(a__  )  ) for a in ARTICLES  )
       _lowerCAmelCase						:	Any        =					max(len(tokenizer.encode(a__  )  ) for a in SUMMARIES  )
       _lowerCAmelCase						:	int        =					4
       _lowerCAmelCase						:	List[str]        =					LegacySeqaSeqDataset(
           a__    ,					data_dir=a__    ,					type_path="""train"""    ,					max_source_length=20    ,					max_target_length=a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					DataLoader(a__    ,					batch_size=2    ,					collate_fn=train_dataset.collate_fn  )
       for batch in dataloader:
         assert batch["attention_mask"].shape == batch["input_ids"].shape
         # show that articles were trimmed.
         assert batch["input_ids"].shape[1] == max_len_source
         assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
         # show that targets were truncated
         assert batch["labels"].shape[1] == trunc_target  # Truncated
         assert max_len_target > trunc_target  # Truncated
         break  # No need to test every batch






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""  )

       _lowerCAmelCase						:	List[Any]        =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       _lowerCAmelCase						:	List[Any]        =					tmp_dir.joinpath("""train.source"""  ).open().readlines()
       _lowerCAmelCase						:	str        =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       pack_data_dir(a__    ,					a__    ,					128    ,					a__  )
       _lowerCAmelCase						:	List[Any]        =					{x.name for x in tmp_dir.iterdir()}
       _lowerCAmelCase						:	Tuple        =					{x.name for x in save_dir.iterdir()}
       _lowerCAmelCase						:	Union[str, Any]        =					save_dir.joinpath("""train.source"""  ).open().readlines()
       # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
       # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
       assert len(a__  ) < len(a__  )
       assert len(a__  ) == 1
       assert len(packed_examples[0]  ) == sum(len(a__  ) for x in orig_examples  )
       assert orig_paths == new_paths






     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,					reason="""This test requires fairseq"""  )
     def    __A  (		self  ):
       if not FAIRSEQ_AVAILABLE:
         return
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Tuple        =					self._get_dataset(max_len=64  )
       _lowerCAmelCase						:	Optional[int]        =					64
       _lowerCAmelCase						:	str        =					ds.make_dynamic_sampler(a__    ,					required_batch_size_multiple=a__  )
       _lowerCAmelCase						:	int        =					[len(a__  ) for x in batch_sampler]
       assert len(set(a__  )  ) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(a__  ) == len(a__  )  # no dropped or added examples
       _lowerCAmelCase						:	List[str]        =					DataLoader(a__    ,					batch_sampler=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2  )
       _lowerCAmelCase						:	List[Any]        =					[]
       _lowerCAmelCase						:	Optional[int]        =					[]
       for batch in data_loader:
         _lowerCAmelCase						:	int        =					batch["""input_ids"""].shape
         _lowerCAmelCase						:	Union[str, Any]        =					src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         _lowerCAmelCase						:	List[str]        =					np.product(batch["""input_ids"""].shape  )
         num_src_per_batch.append(a__  )
         if num_src_tokens > (max_tokens * 1.1):
           failures.append(a__  )
       assert num_src_per_batch[0] == max(a__  )
       if failures:
         raise AssertionError(F"too many tokens in {len(a__  )} batches"  )






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Dict        =					self._get_dataset(max_len=512  )
       _lowerCAmelCase						:	int        =					2
       _lowerCAmelCase						:	List[str]        =					ds.make_sortish_sampler(a__    ,					shuffle=a__  )

       _lowerCAmelCase						:	Dict        =					DataLoader(a__    ,					batch_size=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2  )
       _lowerCAmelCase						:	int        =					DataLoader(a__    ,					batch_size=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2    ,					sampler=a__  )

       _lowerCAmelCase						:	int        =					tokenizer.pad_token_id

       def count_pad_tokens(a__    ,					a__="input_ids"  ):
         return [batch[k].eq(a__  ).sum().item() for batch in data_loader]

       assert sum(count_pad_tokens(a__    ,					k="""labels"""  )  ) < sum(count_pad_tokens(a__    ,					k="""labels"""  )  )
       assert sum(count_pad_tokens(a__  )  ) < sum(count_pad_tokens(a__  )  )
       assert len(a__  ) == len(a__  )






     def    __A  (		self    ,					a__=1000    ,					a__=128  ):
       if os.getenv("""USE_REAL_DATA"""    ,					a__  ):
         _lowerCAmelCase						:	List[str]        =					"""examples/seq2seq/wmt_en_ro"""
         _lowerCAmelCase						:	str        =					max_len * 2 * 64
         if not Path(a__  ).joinpath("""train.len"""  ).exists():
           save_len_file(a__    ,					a__  )
       else:
         _lowerCAmelCase						:	List[str]        =					"""examples/seq2seq/test_data/wmt_en_ro"""
         _lowerCAmelCase						:	Dict        =					max_len * 4
         save_len_file(a__    ,					a__  )

       _lowerCAmelCase						:	Optional[Any]        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Dict        =					SeqaSeqDataset(
           a__    ,					data_dir=a__    ,					type_path="""train"""    ,					max_source_length=a__    ,					max_target_length=a__    ,					n_obs=a__    ,					)
       return ds, max_tokens, tokenizer






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					self._get_dataset()
       _lowerCAmelCase						:	Any        =					set(DistributedSortishSampler(a__    ,					256    ,					num_replicas=2    ,					rank=0    ,					add_extra_examples=a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					set(DistributedSortishSampler(a__    ,					256    ,					num_replicas=2    ,					rank=1    ,					add_extra_examples=a__  )  )
       assert idsa.intersection(a__  ) == set()






     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,					)
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	int        =					AutoTokenizer.from_pretrained(a__    ,					use_fast=a__  )
       if tok_name == MBART_TINY:
         _lowerCAmelCase						:	Dict        =					SeqaSeqDataset(
             a__    ,					data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )    ,					type_path="""train"""    ,					max_source_length=4    ,					max_target_length=8    ,					src_lang="""EN"""    ,					tgt_lang="""FR"""    ,					)
         _lowerCAmelCase						:	Optional[Any]        =					train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
         _lowerCAmelCase						:	List[Any]        =					SeqaSeqDataset(
             a__    ,					data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )    ,					type_path="""train"""    ,					max_source_length=4    ,					max_target_length=8    ,					)
         _lowerCAmelCase						:	Tuple        =					train_dataset.dataset_kwargs
         assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
         assert len(a__  ) == 1 if tok_name == BART_TINY else len(a__  ) == 0




