'''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,
)


lowercase__  :      List[Any]        =					logging.getLogger(__name__)







@dataclass
class lowerCamelCase							:




  '''simple docstring'''



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







@dataclass
class lowerCamelCase							:




  '''simple docstring'''



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






def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  Any					):


      '''simple docstring'''


      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       (      ):


      '''simple docstring'''


      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(_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_            =			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(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					):
                  assert hasattr(_UpperCamelCase							,    _UpperCamelCase					), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
                  setattr(_UpperCamelCase							,    _UpperCamelCase							,    getattr(_UpperCamelCase							,    _UpperCamelCase					)					)

      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=_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_            =			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_            =			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(_UpperCamelCase					)
      if model_args.freeze_encoder:
            freeze_params(model.get_encoder()					)
            assert_all_frozen(model.get_encoder()					)

      UpperCAmelCase_            =			SeqaSeqDataset

      # Get datasets
      UpperCAmelCase_            =			(
          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_            =			(
          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_            =			(
          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_            =			(
          build_compute_metrics_fn(data_args.task							,    _UpperCamelCase					) if training_args.predict_with_generate else None
      )
      UpperCAmelCase_            =			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_            =			{}
      # 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'''							,    _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_            =			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'''							,    _UpperCamelCase							,    training_args.output_dir					)
                  all_metrics.update(_UpperCamelCase					)

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

            UpperCAmelCase_            =			trainer.predict(test_dataset=_UpperCamelCase							,    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'''							,    _UpperCamelCase							,    training_args.output_dir					)
                  all_metrics.update(_UpperCamelCase					)

                  if training_args.predict_with_generate:
                        UpperCAmelCase_            =			tokenizer.batch_decode(
                            test_output.predictions							,    skip_special_tokens=_UpperCamelCase							,    clean_up_tokenization_spaces=_UpperCamelCase					)
                        UpperCAmelCase_            =			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  :  str					):


      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()






'''simple docstring'''
import comet  # From: unbabel-comet
import torch

import datasets


lowercase__  :      str        =					datasets.logging.get_logger(__name__)

lowercase__  :      Dict        =					"\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

lowercase__  :      str        =					"\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

lowercase__  :      str        =					"\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,		_KWARGS_DESCRIPTION					)
class lowerCamelCase							(	datasets.Metric					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Any:
        return datasets.MetricInfo(
            description=_DESCRIPTION  ,							citation=_CITATION  ,							homepage='''https://unbabel.github.io/COMET/html/index.html'''  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(
                {
                    '''sources''': datasets.Value('''string'''  ,							id='''sequence'''  ),
                    '''predictions''': datasets.Value('''string'''  ,							id='''sequence'''  ),
                    '''references''': datasets.Value('''string'''  ,							id='''sequence'''  ),
                }  )  ,							codebase_urls=['''https://github.com/Unbabel/COMET''']  ,							reference_urls=[
                '''https://github.com/Unbabel/COMET''',
                '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
                '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
            ]  ,							)






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Union[str, Any]  )				->Any:
        if self.config_name == "default":
              UpperCAmelCase_            =  comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''  )  )
        else:
              UpperCAmelCase_            =  comet.load_from_checkpoint(comet.download_model(self.config_name  )  )






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Union[str, Any]=None  ,							UpperCAmelCase__			:       int=False  )				->Optional[Any]:
        if gpus is None:
              UpperCAmelCase_            =  1 if torch.cuda.is_available() else 0
        UpperCAmelCase_            =  {'''src''': sources, '''mt''': predictions, '''ref''': references}
        UpperCAmelCase_            =  [dict(zip(UpperCAmelCase__  ,							UpperCAmelCase__  )  ) for t in zip(*data.values()  )]
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.scorer.predict(UpperCAmelCase__  ,							gpus=UpperCAmelCase__  ,							progress_bar=UpperCAmelCase__  )
        return {"mean_score": mean_score, "scores": scores}





