"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser


class        a				(		lowerCAmelCase_   ):


    @staticmethod
    @abstractmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      raise NotImplementedError()


    @abstractmethod
    def      lowerCAmelCase_  (					self	:  int					):
      raise NotImplementedError()

"""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,
)


UpperCAmelCase__  		=						logging.getLogger(__name__)


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



@dataclass
class        a				:
    _snake_case     :  str     							=							field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}   )
    _snake_case     :  Optional[str]     							=							field(
        default='summarization'	,							metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}	,							)
    _snake_case     :  Optional[int]     							=							field(
        default=10_24	,							metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }	,							)
    _snake_case     :  Optional[int]     							=							field(
        default=1_28	,							metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }	,							)
    _snake_case     :  Optional[int]     							=							field(
        default=1_42	,							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``.'
            )
        }	,							)
    _snake_case     :  Optional[int]     							=							field(
        default=1_42	,							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.'
            )
        }	,							)
    _snake_case     :  Optional[int]     							=							field(default=-1	,							metadata={'help': '# training examples. -1 means use all.'}   )
    _snake_case     :  Optional[int]     							=							field(default=-1	,							metadata={'help': '# validation examples. -1 means use all.'}   )
    _snake_case     :  Optional[int]     							=							field(default=-1	,							metadata={'help': '# test examples. -1 means use all.'}   )
    _snake_case     :  Optional[str]     							=							field(default=lowerCAmelCase_	,							metadata={'help': 'Source language id for translation.'}   )
    _snake_case     :  Optional[str]     							=							field(default=lowerCAmelCase_	,							metadata={'help': 'Target language id for translation.'}   )
    _snake_case     :  Optional[int]     							=							field(default=lowerCAmelCase_	,							metadata={'help': '# num_beams to use for evaluation.'}   )
    _snake_case     :  bool     							=							field(
        default=lowerCAmelCase_	,							metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}	,							)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







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






def     __UpperCAmelCase					(			):







  """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.

  _UpperCAmelCase						 =      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.
    _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
  else:
    _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      parser.parse_args_into_dataclasses()

  check_output_dir(lowercase		)

  # 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"""   ,lowercase		)

  # 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.

  _UpperCAmelCase						 =      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   ,)

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

  _UpperCAmelCase						 =      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   ,)
  _UpperCAmelCase						 =      AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path   ,from_tf=""".ckpt""" in model_args.model_name_or_path   ,config=lowercase   ,cache_dir=model_args.cache_dir   ,)

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

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

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(lowercase   ,(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(lowercase   ,lowercase		):
      _UpperCAmelCase						 =      tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      _UpperCAmelCase						 =      tokenizer.convert_tokens_to_ids(data_args.tgt_lang		)

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

  _UpperCAmelCase						 =      SeqaSeqDataset

  # Get datasets
  _UpperCAmelCase						 =      (
      dataset_class(
          lowercase   ,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
  )
  _UpperCAmelCase						 =      (
      dataset_class(
          lowercase   ,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
  )
  _UpperCAmelCase						 =      (
      dataset_class(
          lowercase   ,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
  _UpperCAmelCase						 =      (
      build_compute_metrics_fn(data_args.task   ,lowercase		) if training_args.predict_with_generate else None
  )
  _UpperCAmelCase						 =      SeqaSeqTrainer(
      model=lowercase   ,args=lowercase   ,data_args=lowercase   ,train_dataset=lowercase   ,eval_dataset=lowercase   ,data_collator=SeqaSeqDataCollator(
          lowercase   ,lowercase   ,model.config.decoder_start_token_id   ,training_args.tpu_num_cores		)   ,compute_metrics=lowercase   ,tokenizer=lowercase   ,)

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

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

    trainer.save_model()  # this also saves the tokenizer

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

      # 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 ***"""		)

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

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

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

    _UpperCAmelCase						 =      trainer.predict(test_dataset=lowercase   ,metric_key_prefix="""test"""		)
    _UpperCAmelCase						 =      test_output.metrics
    _UpperCAmelCase						 =      data_args.n_test

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

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

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

  return all_metrics






def     __UpperCAmelCase					(			lowercase		):







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


if __name__ == "__main__":
    main()
