

"""simple docstring"""




from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
     from fairscale.optim import OSS


SCREAMING_SNAKE_CASE_     :			Any	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}


class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         super().__init__(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)

         if config is None:
            assert isinstance(self.model    ,						SCREAMING_SNAKE_CASE__		), (
                "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                f' {self.model.__class__}'
            )
            lowerCamelCase__						=			self.model.config
         else:
            lowerCamelCase__						=			config

         lowerCamelCase__						=			data_args
         lowerCamelCase__						=			self.config.tgt_vocab_size if isinstance(self.config    ,						SCREAMING_SNAKE_CASE__		) else self.config.vocab_size

         if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
            assert self.config.pad_token_id is not None, (
                "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                " calculation or doing label smoothing."
            )

         if self.config.pad_token_id is None and self.config.eos_token_id is not None:
            logger.warning(
                f'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
                " padding.."		)

         if self.args.label_smoothing == 0:
            lowerCamelCase__						=			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id		)
         else:
            # dynamically import label_smoothed_nll_loss
            from utils import label_smoothed_nll_loss

            lowerCamelCase__						=			label_smoothed_nll_loss





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         if self.optimizer is None:
            lowerCamelCase__						=			["bias", "LayerNorm.weight"]
            lowerCamelCase__						=			[
                {
                    "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay		)],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay		)],
                    "weight_decay": 0.0,
                },
            ]
            lowerCamelCase__						=			Adafactor if self.args.adafactor else AdamW
            if self.args.adafactor:
               lowerCamelCase__						=			Adafactor
               lowerCamelCase__						=			{"scale_parameter": False, "relative_step": False}
            else:
               lowerCamelCase__						=			AdamW
               lowerCamelCase__						=			{
                   "betas": (self.args.adam_betaa, self.args.adam_betaa),
                   "eps": self.args.adam_epsilon,
               }
            lowerCamelCase__						=			self.args.learning_rate
            if self.sharded_ddp:
               lowerCamelCase__						=			OSS(
                   params=SCREAMING_SNAKE_CASE__    ,						optim=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__    ,						)
            else:
               lowerCamelCase__						=			optimizer_cls(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)

         if self.lr_scheduler is None:
            lowerCamelCase__						=			self._get_lr_scheduler(SCREAMING_SNAKE_CASE__		)
         else:  # ignoring --lr_scheduler
            logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         lowerCamelCase__						=			arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
            lowerCamelCase__						=			schedule_func(self.optimizer		)
         elif self.args.lr_scheduler == "constant_w_warmup":
            lowerCamelCase__						=			schedule_func(self.optimizer    ,						num_warmup_steps=self.args.warmup_steps		)
         else:
            lowerCamelCase__						=			schedule_func(
                self.optimizer    ,						num_warmup_steps=self.args.warmup_steps    ,						num_training_steps=SCREAMING_SNAKE_CASE__		)
         return scheduler





      def 					_lowerCamelCase  (  self		)   ->					Optional[torch.utils.data.Sampler]:
         if isinstance(self.train_dataset    ,						torch.utils.data.IterableDataset		):
            return None
         elif is_torch_tpu_available():
            return get_tpu_sampler(self.train_dataset		)
         else:
            if self.args.sortish_sampler:
               self.train_dataset.make_sortish_sampler(
                   self.args.per_device_train_batch_size    ,						distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)    ,						)

            return (
                RandomSampler(self.train_dataset		)
                if self.args.local_rank == -1
                else DistributedSampler(self.train_dataset		)
            )





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Dict:
         if self.args.label_smoothing == 0:
            if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
               # force training to ignore pad token
               lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__    ,						use_cache=SCREAMING_SNAKE_CASE__		)[0]
               lowerCamelCase__						=			self.loss_fn(logits.view(-1    ,						logits.shape[-1]		)    ,						labels.view(-1		)		)
            else:
               # compute usual loss via models
               lowerCamelCase__					,       lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__    ,						use_cache=SCREAMING_SNAKE_CASE__		)[:2]
         else:
            # compute label smoothed loss
            lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__    ,						use_cache=SCREAMING_SNAKE_CASE__		)[0]
            lowerCamelCase__						=			torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE__    ,						dim=-1		)
            lowerCamelCase__					,       lowerCamelCase__						=			self.loss_fn(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						self.args.label_smoothing    ,						ignore_index=self.config.pad_token_id		)
         return loss, logits





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         lowerCamelCase__						=			inputs.pop("labels"		)
         lowerCamelCase__					,       lowerCamelCase__						=			self._compute_loss(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         return loss





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						)   ->					Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
         lowerCamelCase__						=			self._prepare_inputs(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			{
             "max_length": self.data_args.val_max_target_length
             if self.data_args is not None
             else self.config.max_length,
             "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
         }

         if self.args.predict_with_generate and not self.args.prediction_loss_only:
            lowerCamelCase__						=			self.model.generate(
                inputs["input_ids"]    ,						attention_mask=inputs["attention_mask"]    ,						**SCREAMING_SNAKE_CASE__    ,						)
            # in case the batch is shorter than max length, the output should be padded
            if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
               lowerCamelCase__						=			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__    ,						gen_kwargs["max_length"]		)

         lowerCamelCase__						=			inputs.pop("labels"		)
         with torch.no_grad():
            # compute loss on predict data
            lowerCamelCase__					,       lowerCamelCase__						=			self._compute_loss(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			loss.mean().detach()
         if self.args.prediction_loss_only:
            return (loss, None, None)

         lowerCamelCase__						=			generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
            lowerCamelCase__						=			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__    ,						gen_kwargs["max_length"]		)

         return (loss, logits, labels)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[str]:
         # If PAD token is not defined at least EOS token has to be defined
         lowerCamelCase__						=			self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

         if pad_token_id is None:
            raise ValueError(
                "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
                f' padded to `max_length`={max_length}'		)

         lowerCamelCase__						=			pad_token_id * torch.ones(
             (tensor.shape[0], max_length)    ,						dtype=tensor.dtype    ,						device=tensor.device		)
         lowerCamelCase__						=			tensor
         return padded_tensor







"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			logging.getLogger(__name__)


@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,       )
      __a								=	field(default=__a				,       metadata={'help': 'Whether tp freeze the encoder.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Whether  to freeze the embeddings.'}					)






@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
      __a								=	field(
          default='summarization'				,       metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}				,       )
      __a								=	field(
          default=1024				,       metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(
          default=128				,       metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(
          default=142				,       metadata={
              'help': (
                  'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded. '
                  'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                  'during ``evaluate`` and ``predict``.'
              )
          }				,       )
      __a								=	field(
          default=142				,       metadata={
              'help': (
                  'The maximum total sequence length for test target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(default=-1				,       metadata={'help': '# training examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# validation examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# test examples. -1 means use all.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Source language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Target language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': '# num_beams to use for evaluation.'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}				,       )






def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Union[str, Any]:




   """simple docstring"""
   logger.info(f'***** {split} metrics *****'   )
   for key in sorted(metrics.keys()   ):
      logger.info(f'  {key} = {metrics[key]}'   )
   save_json(A__	,    os.path.join(A__	,    f'{split}_results.json'   )   )



def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowerCamelCase__						=			HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

   if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_args_into_dataclasses()

   check_output_dir(A__   )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,    datefmt="%m/%d/%Y %H:%M:%S"	,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,    )
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"	,    training_args.local_rank	,    training_args.device	,    training_args.n_gpu	,    bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )	,    training_args.fpaa	,    )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank   ):
      transformers.utils.logging.set_verbosity_info()
   logger.info("Training/evaluation parameters %s"	,    A__   )

   # Set seed
   set_seed(training_args.seed   )

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

   lowerCamelCase__						=			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path	,    cache_dir=model_args.cache_dir	,    )

   lowerCamelCase__						=			("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(A__	,    A__	,    A__   ):
         assert hasattr(A__	,    A__   ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
         setattr(A__	,    A__	,    getattr(A__	,    A__   )   )

   lowerCamelCase__						=			AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,    cache_dir=model_args.cache_dir	,    )
   lowerCamelCase__						=			AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path	,    from_tf=".ckpt" in model_args.model_name_or_path	,    config=A__	,    cache_dir=model_args.cache_dir	,    )

   # use task specific params
   use_task_specific_params(A__	,    data_args.task   )

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      lowerCamelCase__						=			model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(A__	,    (MBartTokenizer, MBartTokenizerFast)   ):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(A__	,    A__   ):
         lowerCamelCase__						=			tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowerCamelCase__						=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

   if model_args.freeze_embeds:
      freeze_embeds(A__   )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()   )
      assert_all_frozen(model.get_encoder()   )

   lowerCamelCase__						=			SeqaSeqDataset

   # Get datasets
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="train"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_train	,    max_target_length=data_args.max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_train
       else None
   )
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="val"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_val	,    max_target_length=data_args.val_max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="test"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_test	,    max_target_length=data_args.test_max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   lowerCamelCase__						=			(
       build_compute_metrics_fn(data_args.task	,    A__   ) if training_args.predict_with_generate else None
   )
   lowerCamelCase__						=			SeqaSeqTrainer(
       model=A__	,    args=A__	,    data_args=A__	,    train_dataset=A__	,    eval_dataset=A__	,    data_collator=SeqaSeqDataCollator(
           A__	,    A__	,    model.config.decoder_start_token_id	,    training_args.tpu_num_cores   )	,    compute_metrics=A__	,    tokenizer=A__	,    )

   lowerCamelCase__						=			{}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"   )

      lowerCamelCase__						=			trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
      lowerCamelCase__						=			train_result.metrics
      lowerCamelCase__						=			data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir	,    "trainer_state.json"   )   )

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"   )

      lowerCamelCase__						=			trainer.evaluate(metric_key_prefix="val"   )
      lowerCamelCase__						=			data_args.n_val
      lowerCamelCase__						=			round(metrics["val_loss"]	,    4   )

      if trainer.is_world_process_zero():
         handle_metrics("val"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

   if training_args.do_predict:
      logger.info("*** Predict ***"   )

      lowerCamelCase__						=			trainer.predict(test_dataset=A__	,    metric_key_prefix="test"   )
      lowerCamelCase__						=			test_output.metrics
      lowerCamelCase__						=			data_args.n_test

      if trainer.is_world_process_zero():
         lowerCamelCase__						=			round(metrics["test_loss"]	,    4   )
         handle_metrics("test"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         if training_args.predict_with_generate:
            lowerCamelCase__						=			tokenizer.batch_decode(
                test_output.predictions	,    skip_special_tokens=A__	,    clean_up_tokenization_spaces=A__   )
            lowerCamelCase__						=			lmap(str.strip	,    A__   )
            write_txt_file(A__	,    os.path.join(training_args.output_dir	,    "test_generations.txt"   )   )

   if trainer.is_world_process_zero():
      save_json(A__	,    os.path.join(training_args.output_dir	,    "all_results.json"   )   )

   return all_metrics



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
     main()




