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




@dataclass
class    a__				:
     lowerCamelCase  :					str							=field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}			)
     lowerCamelCase  :					Optional[str]							=field(
         default="summarization"							,       metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}							,       )
     lowerCamelCase  :					Optional[int]							=field(
         default=1_0_2_4							,       metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }							,       )
     lowerCamelCase  :					Optional[int]							=field(
         default=1_2_8							,       metadata={
             "help": (
                 "The maximum total sequence length for target text after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }							,       )
     lowerCamelCase  :					Optional[int]							=field(
         default=1_4_2							,       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``."
             )
         }							,       )
     lowerCamelCase  :					Optional[int]							=field(
         default=1_4_2							,       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."
             )
         }							,       )
     lowerCamelCase  :					Optional[int]							=field(default=-1							,       metadata={"help": "# training examples. -1 means use all."}			)
     lowerCamelCase  :					Optional[int]							=field(default=-1							,       metadata={"help": "# validation examples. -1 means use all."}			)
     lowerCamelCase  :					Optional[int]							=field(default=-1							,       metadata={"help": "# test examples. -1 means use all."}			)
     lowerCamelCase  :					Optional[str]							=field(default=UpperCAmelCase__							,       metadata={"help": "Source language id for translation."}			)
     lowerCamelCase  :					Optional[str]							=field(default=UpperCAmelCase__							,       metadata={"help": "Target language id for translation."}			)
     lowerCamelCase  :					Optional[int]							=field(default=UpperCAmelCase__							,       metadata={"help": "# num_beams to use for evaluation."}			)
     lowerCamelCase  :					bool							=field(
         default=UpperCAmelCase__							,       metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}							,       )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[str]:
    logger.info(f"""***** {split} metrics *****"""    )
    for key in sorted(metrics.keys()    ):
        logger.info(f"""  {key} = {metrics[key]}"""    )
    save_json(UpperCamelCase__    ,  os.path.join(UpperCamelCase__    ,  f"""{split}_results.json"""    )    )






def __lowerCAmelCase			(							)     ->   List[str]:
    # 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(UpperCamelCase__    )

    # 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'''    ,  UpperCamelCase__    )

    # 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(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ):
            assert hasattr(UpperCamelCase__    ,  UpperCamelCase__    ), f"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
            setattr(UpperCamelCase__    ,  UpperCamelCase__    ,  getattr(UpperCamelCase__    ,  UpperCamelCase__    )    )

    __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=UpperCamelCase__    ,  cache_dir=model_args.cache_dir    ,  )

    # use task specific params
    use_task_specific_params(UpperCamelCase__    ,  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(UpperCamelCase__    ,  (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(UpperCamelCase__    ,  UpperCamelCase__    ):
            __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(UpperCamelCase__    )
    if model_args.freeze_encoder:
        freeze_params(model.get_encoder()    )
        assert_all_frozen(model.get_encoder()    )

    __lowerCamelCase          =  SeqaSeqDataset

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

    __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'''    ,  UpperCamelCase__    ,  training_args.output_dir    )
            all_metrics.update(UpperCamelCase__    )

            # 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'''    ,  UpperCamelCase__    ,  training_args.output_dir    )
            all_metrics.update(UpperCamelCase__    )

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

        __lowerCamelCase          =  trainer.predict(test_dataset=UpperCamelCase__    ,  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'''    ,  UpperCamelCase__    ,  training_args.output_dir    )
            all_metrics.update(UpperCamelCase__    )

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

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

    return all_metrics






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Optional[Any]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  set({'''(''', '''[''', '''{'''}    )
    __lowerCamelCase          =  set({''')''', ''']''', '''}'''}    )
    __lowerCamelCase          =  {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

    for i in range(len(UpperCamelCase__    )    ):
        if s[i] in open_brackets:
            stack.append(s[i]    )

        elif s[i] in closed_brackets and (
            len(UpperCamelCase__    ) == 0 or (len(UpperCamelCase__    ) > 0 and open_to_closed[stack.pop()] != s[i])
        ):
            return False

    return len(UpperCamelCase__    ) == 0






def __lowerCAmelCase			(							)     ->   Union[str, Any]:
    __lowerCamelCase          =  input('''Enter sequence of brackets: '''    )
    if is_balanced(UpperCamelCase__    ):
        print(UpperCamelCase__    ,  '''is balanced'''    )
    else:
        print(UpperCamelCase__    ,  '''is not balanced'''    )


if __name__ == "__main__":
  main()
