





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     lowercase__       :

   '''simple docstring'''


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






@dataclass
class     lowercase__       :

   '''simple docstring'''


   a						:							str   		=		field(
       metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}				)
   a						:							Optional[str]   		=		field(
       default="summarization"			,				metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}			,				)
   a						:							Optional[int]   		=		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						:							Optional[int]   		=		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						:							Optional[int]   		=		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						:							Optional[int]   		=		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						:							Optional[int]   		=		field(default=-1			,				metadata={"help": "# training examples. -1 means use all."}				)
   a						:							Optional[int]   		=		field(default=-1			,				metadata={"help": "# validation examples. -1 means use all."}				)
   a						:							Optional[int]   		=		field(default=-1			,				metadata={"help": "# test examples. -1 means use all."}				)
   a						:							Optional[str]   		=		field(default=__lowerCamelCase			,				metadata={"help": "Source language id for translation."}				)
   a						:							Optional[str]   		=		field(default=__lowerCamelCase			,				metadata={"help": "Target language id for translation."}				)
   a						:							Optional[int]   		=		field(default=__lowerCamelCase			,				metadata={"help": "# num_beams to use for evaluation."}				)
   a						:							bool   		=		field(
       default=__lowerCamelCase			,				metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}			,				)




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]      )      ->	Any:
    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[Any]:
    # 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__							:				Dict          =      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__							:				Tuple          =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
    else:
        UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Optional[Any]          =      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.

    UpperCamelCase__							:				List[str]          =      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__							:				Dict          =      ('''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      )      )

    UpperCamelCase__							:				Optional[Any]          =      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__							:				Optional[int]          =      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:
        UpperCamelCase__							:				List[Any]          =      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      ):
            UpperCamelCase__							:				int          =      tokenizer.lang_code_to_id[data_args.tgt_lang]
        else:
            UpperCamelCase__							:				List[Any]          =      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()      )

    UpperCamelCase__							:				Optional[int]          =      SeqaSeqDataset

    # Get datasets
    UpperCamelCase__							:				List[str]          =      (
        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
    )
    UpperCamelCase__							:				Any          =      (
        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
    )
    UpperCamelCase__							:				int          =      (
        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
    UpperCamelCase__							:				Dict          =      (
        build_compute_metrics_fn(data_args.task		,   __UpperCAmelCase      ) if training_args.predict_with_generate else None
    )
    UpperCamelCase__							:				Optional[int]          =      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		,   )

    UpperCamelCase__							:				str          =      {}
    # Training
    if training_args.do_train:
        logger.info('''*** Train ***'''      )

        UpperCamelCase__							:				List[Any]          =      trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
        UpperCamelCase__							:				Optional[int]          =      train_result.metrics
        UpperCamelCase__							:				List[str]          =      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 ***'''      )

        UpperCamelCase__							:				Any          =      trainer.evaluate(metric_key_prefix='''val'''      )
        UpperCamelCase__							:				str          =      data_args.n_val
        UpperCamelCase__							:				str          =      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 ***'''      )

        UpperCamelCase__							:				List[Any]          =      trainer.predict(test_dataset=__UpperCAmelCase		,   metric_key_prefix='''test'''      )
        UpperCamelCase__							:				Tuple          =      test_output.metrics
        UpperCamelCase__							:				Optional[Any]          =      data_args.n_test

        if trainer.is_world_process_zero():
            UpperCamelCase__							:				str          =      round(metrics['''test_loss''']		,   4      )
            handle_metrics('''test'''		,   __UpperCAmelCase		,   training_args.output_dir      )
            all_metrics.update(__UpperCAmelCase      )

            if training_args.predict_with_generate:
                UpperCamelCase__							:				Dict          =      tokenizer.batch_decode(
                    test_output.predictions		,   skip_special_tokens=__UpperCAmelCase		,   clean_up_tokenization_spaces=__UpperCAmelCase      )
                UpperCamelCase__							:				Union[str, Any]          =      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:			Union[str, Any]      )      ->	Any:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	bool:
    return str(__UpperCAmelCase      ) == str(__UpperCAmelCase      )[::-1]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	int:
    return int(__UpperCAmelCase      ) + int(str(__UpperCAmelCase      )[::-1]      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 1_0000      )      ->	int:
    UpperCamelCase__							:				Optional[Any]          =      []
    for num in range(1		,   __UpperCAmelCase      ):
        UpperCamelCase__							:				str          =      0
        UpperCamelCase__							:				Any          =      num
        while iterations < 50:
            UpperCamelCase__							:				List[Any]          =      sum_reverse(__UpperCAmelCase      )
            iterations += 1
            if is_palindrome(__UpperCAmelCase      ):
                break
        else:
            lychrel_nums.append(__UpperCAmelCase      )
    return len(__UpperCAmelCase      )


if __name__ == "__main__":
  print(F'''{solution() = }''')

