




'''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_		:     Optional[Any]          =		logging.getLogger(__name__)

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







@dataclass
class   _a							:
     SCREAMING_SNAKE_CASE_				:       str       							=		field(
         metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
     SCREAMING_SNAKE_CASE_				:       Optional[str]       							=		field(
         default="""summarization"""   ,   metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}   ,   )
     SCREAMING_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."""
             )
         }   ,   )
     SCREAMING_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."""
             )
         }   ,   )
     SCREAMING_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``."""
             )
         }   ,   )
     SCREAMING_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."""
             )
         }   ,   )
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		field(default=-1   ,   metadata={"""help""": """# training examples. -1 means use all."""}				)
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		field(default=-1   ,   metadata={"""help""": """# validation examples. -1 means use all."""}				)
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		field(default=-1   ,   metadata={"""help""": """# test examples. -1 means use all."""}				)
     SCREAMING_SNAKE_CASE_				:       Optional[str]       							=		field(default=__lowerCAmelCase   ,   metadata={"""help""": """Source language id for translation."""}				)
     SCREAMING_SNAKE_CASE_				:       Optional[str]       							=		field(default=__lowerCAmelCase   ,   metadata={"""help""": """Target language id for translation."""}				)
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		field(default=__lowerCAmelCase   ,   metadata={"""help""": """# num_beams to use for evaluation."""}				)
     SCREAMING_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       __a      (  _UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       List[Any]       )		->					Optional[int]:

     """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       __a      (  )		->					Union[str, Any]:

     """simple docstring"""




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

     _snake_case				= 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   ,		)

     _snake_case				= ("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       )       )

     _snake_case				= 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   ,		)
     _snake_case				= 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:
          _snake_case				= 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       ):
               _snake_case				= tokenizer.lang_code_to_id[data_args.tgt_lang]
          else:
               _snake_case				= 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()       )

     _snake_case				= SeqaSeqDataset

     # Get datasets
     _snake_case				= (
         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
     )
     _snake_case				= (
         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
     )
     _snake_case				= (
         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
     _snake_case				= (
         build_compute_metrics_fn(data_args.task   ,		_UpperCamelCase       ) if training_args.predict_with_generate else None
     )
     _snake_case				= 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   ,		)

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

          _snake_case				= trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
          _snake_case				= train_result.metrics
          _snake_case				= 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 ***"       )

          _snake_case				= trainer.evaluate(metric_key_prefix="val"       )
          _snake_case				= data_args.n_val
          _snake_case				= 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 ***"       )

          _snake_case				= trainer.predict(test_dataset=_UpperCamelCase   ,		metric_key_prefix="test"       )
          _snake_case				= test_output.metrics
          _snake_case				= data_args.n_test

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

               if training_args.predict_with_generate:
                    _snake_case				= tokenizer.batch_decode(
                        test_output.predictions   ,		skip_special_tokens=_UpperCamelCase   ,		clean_up_tokenization_spaces=_UpperCamelCase       )
                    _snake_case				= 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       __a      (  _UpperCamelCase:       Optional[Any]       )		->					str:

     """simple docstring"""




     main()


if __name__ == "__main__":
   main()








'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase_		:     Tuple          =		'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

UpperCamelCase_		:     Optional[Any]          =		'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

UpperCamelCase_		:     Dict          =		'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''





def       __a      (  _UpperCamelCase:       Tuple   ,		_UpperCamelCase:       int       )		->					Optional[Any]:

     """simple docstring"""




     return float((preds == labels).mean()       )





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       int="binary"       )		->					Any:

     """simple docstring"""




     _snake_case				= simple_accuracy(_UpperCamelCase   ,		_UpperCamelCase       )
     _snake_case				= float(fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average=_UpperCamelCase       )       )
     return {
         "accuracy": acc,
         "f1": fa,
     }





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       Any       )		->					Tuple:

     """simple docstring"""




     _snake_case				= {}
     for id_pred, label in zip(_UpperCamelCase   ,		_UpperCamelCase       ):
          _snake_case				= F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
          _snake_case				= id_pred["prediction"]
          if question_id in question_map:
               question_map[question_id].append((pred, label)       )
          else:
               _snake_case				= [(pred, label)]
     _snake_case				,   _snake_case				= [], []
     for question, preds_labels in question_map.items():
          _snake_case				,   _snake_case				= zip(*_UpperCamelCase       )
          _snake_case				= fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average="macro"       )
          fas.append(_UpperCamelCase       )
          _snake_case				= int(sum(pred == label for pred, label in preds_labels       ) == len(_UpperCamelCase       )       )
          ems.append(_UpperCamelCase       )
     _snake_case				= float(sum(_UpperCamelCase       ) / len(_UpperCamelCase       )       )
     _snake_case				= sum(_UpperCamelCase       ) / len(_UpperCamelCase       )
     _snake_case				= float(fa_score(y_true=_UpperCamelCase   ,		y_pred=[id_pred["prediction"] for id_pred in ids_preds]       )       )
     return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}





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






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          if self.config_name not in [
              "boolq",
              "cb",
              "copa",
              "multirc",
              "record",
              "rte",
              "wic",
              "wsc",
              "wsc.fixed",
              "axb",
              "axg",
          ]:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )
          return datasets.MetricInfo(
              description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(self._get_feature_types()       )   ,codebase_urls=[]   ,reference_urls=[]   ,format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None   ,)






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          if self.config_name == "record":
               return {
                   "predictions": {
                       "idx": {
                           "passage": datasets.Value("int64"       ),
                           "query": datasets.Value("int64"       ),
                       },
                       "prediction_text": datasets.Value("string"       ),
                   },
                   "references": {
                       "idx": {
                           "passage": datasets.Value("int64"       ),
                           "query": datasets.Value("int64"       ),
                       },
                       "answers": datasets.Sequence(datasets.Value("string"       )       ),
                   },
               }
          elif self.config_name == "multirc":
               return {
                   "predictions": {
                       "idx": {
                           "answer": datasets.Value("int64"       ),
                           "paragraph": datasets.Value("int64"       ),
                           "question": datasets.Value("int64"       ),
                       },
                       "prediction": datasets.Value("int64"       ),
                   },
                   "references": datasets.Value("int64"       ),
               }
          else:
               return {
                   "predictions": datasets.Value("int64"       ),
                   "references": datasets.Value("int64"       ),
               }






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          if self.config_name == "axb":
               return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )}
          elif self.config_name == "cb":
               return acc_and_fa(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,fa_avg="macro"       )
          elif self.config_name == "record":
               _snake_case				= [
                   {
                       "qas": [
                           {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                           for ref in references
                       ]
                   }
               ]
               _snake_case				= {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
               return evaluate_record(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )[0]
          elif self.config_name == "multirc":
               return evaluate_multirc(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
               return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )}
          else:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )


