"""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,
)


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







@dataclass
class 		__A							:
     _UpperCamelCase							:    str	      =	field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}	)
     _UpperCamelCase							:    Optional[str]	      =	field(
         default="summarization"					,			metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}					,			)
     _UpperCamelCase							:    Optional[int]	      =	field(
         default=1_024					,			metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }					,			)
     _UpperCamelCase							:    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."
             )
         }					,			)
     _UpperCamelCase							:    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``."
             )
         }					,			)
     _UpperCamelCase							:    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."
             )
         }					,			)
     _UpperCamelCase							:    Optional[int]	      =	field(default=-1					,			metadata={"help": "# training examples. -1 means use all."}	)
     _UpperCamelCase							:    Optional[int]	      =	field(default=-1					,			metadata={"help": "# validation examples. -1 means use all."}	)
     _UpperCamelCase							:    Optional[int]	      =	field(default=-1					,			metadata={"help": "# test examples. -1 means use all."}	)
     _UpperCamelCase							:    Optional[str]	      =	field(default=SCREAMING_SNAKE_CASE_					,			metadata={"help": "Source language id for translation."}	)
     _UpperCamelCase							:    Optional[str]	      =	field(default=SCREAMING_SNAKE_CASE_					,			metadata={"help": "Target language id for translation."}	)
     _UpperCamelCase							:    Optional[int]	      =	field(default=SCREAMING_SNAKE_CASE_					,			metadata={"help": "# num_beams to use for evaluation."}	)
     _UpperCamelCase							:    bool	      =	field(
         default=SCREAMING_SNAKE_CASE_					,			metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}					,			)







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





def 							SCREAMING_SNAKE_CASE  (    ) ->   Dict:
  # 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		: str						    =  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		: List[Any]						    =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
  else:
    _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  parser.parse_args_into_dataclasses()

  check_output_dir(_lowerCamelCase			)

  # 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"""    ,_lowerCamelCase			)

  # 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		: Tuple						    =  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		: List[Any]						    =  ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  for p in extra_model_params:
    if getattr(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			):
      assert hasattr(_lowerCamelCase    ,_lowerCamelCase			), f"({config.__class__.__name__}) doesn't have a `{p}` attribute"
      setattr(_lowerCamelCase    ,_lowerCamelCase    ,getattr(_lowerCamelCase    ,_lowerCamelCase			)			)

  _lowerCAmelCase		: Union[str, 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    ,)
  _lowerCAmelCase		: List[Any]						    =  AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path    ,from_tf=""".ckpt""" in model_args.model_name_or_path    ,config=_lowerCamelCase    ,cache_dir=model_args.cache_dir    ,)

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

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

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

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

  _lowerCAmelCase		: List[str]						    =  SeqaSeqDataset

  # Get datasets
  _lowerCAmelCase		: Dict						    =  (
      dataset_class(
          _lowerCamelCase    ,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		: List[Any]						    =  (
      dataset_class(
          _lowerCamelCase    ,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		: Dict						    =  (
      dataset_class(
          _lowerCamelCase    ,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		: Union[str, Any]						    =  (
      build_compute_metrics_fn(data_args.task    ,_lowerCamelCase			) if training_args.predict_with_generate else None
  )
  _lowerCAmelCase		: int						    =  SeqaSeqTrainer(
      model=_lowerCamelCase    ,args=_lowerCamelCase    ,data_args=_lowerCamelCase    ,train_dataset=_lowerCamelCase    ,eval_dataset=_lowerCamelCase    ,data_collator=SeqaSeqDataCollator(
          _lowerCamelCase    ,_lowerCamelCase    ,model.config.decoder_start_token_id    ,training_args.tpu_num_cores			)    ,compute_metrics=_lowerCamelCase    ,tokenizer=_lowerCamelCase    ,)

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

    _lowerCAmelCase		: Union[str, Any]						    =  trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
    _lowerCAmelCase		: List[str]						    =  train_result.metrics
    _lowerCAmelCase		: List[Any]						    =  data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

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

      # 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		: Optional[Any]						    =  trainer.evaluate(metric_key_prefix="""val"""			)
    _lowerCAmelCase		: List[Any]						    =  data_args.n_val
    _lowerCAmelCase		: Tuple						    =  round(metrics["""val_loss"""]    ,4			)

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

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

    _lowerCAmelCase		: List[Any]						    =  trainer.predict(test_dataset=_lowerCamelCase    ,metric_key_prefix="""test"""			)
    _lowerCAmelCase		: Tuple						    =  test_output.metrics
    _lowerCAmelCase		: List[str]						    =  data_args.n_test

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

      if training_args.predict_with_generate:
        _lowerCAmelCase		: Dict						    =  tokenizer.batch_decode(
            test_output.predictions    ,skip_special_tokens=_lowerCamelCase    ,clean_up_tokenization_spaces=_lowerCamelCase			)
        _lowerCAmelCase		: List[str]						    =  lmap(str.strip    ,_lowerCamelCase			)
        write_txt_file(_lowerCamelCase    ,os.path.join(training_args.output_dir    ,"""test_generations.txt"""			)			)

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

  return all_metrics





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict			) ->   Tuple:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
     main()





"""simple docstring"""




_a   :	Optional[Any] 					=     '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a   :	List[str] 					=     [{'type': 'code', 'content': INSTALL_CONTENT}]
_a   :	str 					=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




