



'''simple docstring'''





from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
 from fairscale.optim import OSS


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,*__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		List[Any]:
      super().__init__(*__UpperCAmelCase  ,**__UpperCAmelCase      )

      if config is None:
          assert isinstance(self.model  ,__UpperCAmelCase      ), (
              "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
              F""" {self.model.__class__}"""
          )
          lowerCAmelCase__ :  str							      =					self.model.config
      else:
          lowerCAmelCase__ :  Dict							      =					config

      lowerCAmelCase__ :  Dict							      =					data_args
      lowerCAmelCase__ :  str							      =					self.config.tgt_vocab_size if isinstance(self.config  ,__UpperCAmelCase      ) else self.config.vocab_size

      if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
          assert self.config.pad_token_id is not None, (
              "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
              " calculation or doing label smoothing."
          )

      if self.config.pad_token_id is None and self.config.eos_token_id is not None:
          logger.warning(
              F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
              """ padding.."""      )

      if self.args.label_smoothing == 0:
          lowerCAmelCase__ :  Optional[int]							      =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id      )
      else:
          # dynamically import label_smoothed_nll_loss
          from utils import label_smoothed_nll_loss

          lowerCAmelCase__ :  Any							      =					label_smoothed_nll_loss




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      if self.optimizer is None:
          lowerCAmelCase__ :  str							      =					["""bias""", """LayerNorm.weight"""]
          lowerCAmelCase__ :  Any							      =					[
              {
                  """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay      )],
                  """weight_decay""": self.args.weight_decay,
              },
              {
                  """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay      )],
                  """weight_decay""": 0.0,
              },
          ]
          lowerCAmelCase__ :  Tuple							      =					Adafactor if self.args.adafactor else AdamW
          if self.args.adafactor:
              lowerCAmelCase__ :  Union[str, Any]							      =					Adafactor
              lowerCAmelCase__ :  int							      =					{"""scale_parameter""": False, """relative_step""": False}
          else:
              lowerCAmelCase__ :  List[Any]							      =					AdamW
              lowerCAmelCase__ :  Dict							      =					{
                  """betas""": (self.args.adam_betaa, self.args.adam_betaa),
                  """eps""": self.args.adam_epsilon,
              }
          lowerCAmelCase__ :  Tuple							      =					self.args.learning_rate
          if self.sharded_ddp:
              lowerCAmelCase__ :  Optional[int]							      =					OSS(
                  params=__UpperCAmelCase  ,optim=__UpperCAmelCase  ,**__UpperCAmelCase  ,)
          else:
              lowerCAmelCase__ :  Union[str, Any]							      =					optimizer_cls(__UpperCAmelCase  ,**__UpperCAmelCase      )

      if self.lr_scheduler is None:
          lowerCAmelCase__ :  int							      =					self._get_lr_scheduler(__UpperCAmelCase      )
      else:  # ignoring --lr_scheduler
          logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  int							      =					arg_to_scheduler[self.args.lr_scheduler]
      if self.args.lr_scheduler == "constant":
          lowerCAmelCase__ :  str							      =					schedule_func(self.optimizer      )
      elif self.args.lr_scheduler == "constant_w_warmup":
          lowerCAmelCase__ :  Union[str, Any]							      =					schedule_func(self.optimizer  ,num_warmup_steps=self.args.warmup_steps      )
      else:
          lowerCAmelCase__ :  str							      =					schedule_func(
              self.optimizer  ,num_warmup_steps=self.args.warmup_steps  ,num_training_steps=__UpperCAmelCase      )
      return scheduler




  def 							UpperCAmelCase_	(    self      )    ->		Optional[torch.utils.data.Sampler]:
      if isinstance(self.train_dataset  ,torch.utils.data.IterableDataset      ):
          return None
      elif is_torch_tpu_available():
          return get_tpu_sampler(self.train_dataset      )
      else:
          if self.args.sortish_sampler:
              self.train_dataset.make_sortish_sampler(
                  self.args.per_device_train_batch_size  ,distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)  ,)

          return (
              RandomSampler(self.train_dataset      )
              if self.args.local_rank == -1
              else DistributedSampler(self.train_dataset      )
          )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		List[str]:
      if self.args.label_smoothing == 0:
          if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
              # force training to ignore pad token
              lowerCAmelCase__ :  Union[str, Any]							      =					model(**__UpperCAmelCase  ,use_cache=__UpperCAmelCase      )[0]
              lowerCAmelCase__ :  Dict							      =					self.loss_fn(logits.view(-1  ,logits.shape[-1]      )  ,labels.view(-1      )      )
          else:
              # compute usual loss via models
              lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					model(**__UpperCAmelCase  ,labels=__UpperCAmelCase  ,use_cache=__UpperCAmelCase      )[:2]
      else:
          # compute label smoothed loss
          lowerCAmelCase__ :  List[Any]							      =					model(**__UpperCAmelCase  ,use_cache=__UpperCAmelCase      )[0]
          lowerCAmelCase__ :  Tuple							      =					torch.nn.functional.log_softmax(__UpperCAmelCase  ,dim=-1      )
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Tuple							      =					self.loss_fn(__UpperCAmelCase  ,__UpperCAmelCase  ,self.args.label_smoothing  ,ignore_index=self.config.pad_token_id      )
      return loss, logits




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					inputs.pop("""labels"""      )
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					self._compute_loss(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )
      return loss




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,)    ->		Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
      lowerCAmelCase__ :  Optional[Any]							      =					self._prepare_inputs(__UpperCAmelCase      )

      lowerCAmelCase__ :  Any							      =					{
          """max_length""": self.data_args.val_max_target_length
          if self.data_args is not None
          else self.config.max_length,
          """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
      }

      if self.args.predict_with_generate and not self.args.prediction_loss_only:
          lowerCAmelCase__ :  Tuple							      =					self.model.generate(
              inputs["""input_ids"""]  ,attention_mask=inputs["""attention_mask"""]  ,**__UpperCAmelCase  ,)
          # in case the batch is shorter than max length, the output should be padded
          if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
              lowerCAmelCase__ :  int							      =					self._pad_tensors_to_max_len(__UpperCAmelCase  ,gen_kwargs["""max_length"""]      )

      lowerCAmelCase__ :  Union[str, Any]							      =					inputs.pop("""labels"""      )
      with torch.no_grad():
          # compute loss on predict data
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					self._compute_loss(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )

      lowerCAmelCase__ :  Any							      =					loss.mean().detach()
      if self.args.prediction_loss_only:
          return (loss, None, None)

      lowerCAmelCase__ :  Union[str, Any]							      =					generated_tokens if self.args.predict_with_generate else logits

      if labels.shape[-1] < gen_kwargs["max_length"]:
          lowerCAmelCase__ :  Optional[int]							      =					self._pad_tensors_to_max_len(__UpperCAmelCase  ,gen_kwargs["""max_length"""]      )

      return (loss, logits, labels)






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Any:
      # If PAD token is not defined at least EOS token has to be defined
      lowerCAmelCase__ :  Dict							      =					self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

      if pad_token_id is None:
          raise ValueError(
              """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
              F""" padded to `max_length`={max_length}"""      )

      lowerCAmelCase__ :  int							      =					pad_token_id * torch.ones(
          (tensor.shape[0], max_length)  ,dtype=tensor.dtype  ,device=tensor.device      )
      lowerCAmelCase__ :  Optional[int]							      =					tensor
      return padded_tensor







'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_lowerCAmelCase      						=	'''\
@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}
}
'''

_lowerCAmelCase      						=	'''\
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.
'''

_lowerCAmelCase      						=	'''
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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    return float((preds == labels).mean()			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase="binary"			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					simple_accuracy(UpperCamelCase							,    UpperCamelCase			)
    lowerCAmelCase__ :  Tuple							      =					float(fa_score(y_true=UpperCamelCase							,    y_pred=UpperCamelCase							,    average=UpperCamelCase			)			)
    return {
        "accuracy": acc,
        "f1": fa,
    }
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[str]							      =					{}
    for id_pred, label in zip(UpperCamelCase							,    UpperCamelCase			):
        lowerCAmelCase__ :  str							      =					f"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
        lowerCAmelCase__ :  Dict							      =					id_pred["""prediction"""]
        if question_id in question_map:
            question_map[question_id].append((pred, label)			)
        else:
            lowerCAmelCase__ :  Optional[int]							      =					[(pred, label)]
    lowerCAmelCase__      ,   lowerCAmelCase__ :  int							      =					[], []
    for question, preds_labels in question_map.items():
        lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					zip(*UpperCamelCase			)
        lowerCAmelCase__ :  List[Any]							      =					fa_score(y_true=UpperCamelCase							,    y_pred=UpperCamelCase							,    average="""macro"""			)
        fas.append(UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					int(sum(pred == label for pred, label in preds_labels			) == len(UpperCamelCase			)			)
        ems.append(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					float(sum(UpperCamelCase			) / len(UpperCamelCase			)			)
    lowerCAmelCase__ :  List[Any]							      =					sum(UpperCamelCase			) / len(UpperCamelCase			)
    lowerCAmelCase__ :  Dict							      =					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        lowerCAmelCase_(				datasets.Metric     ):
  '''simple docstring'''




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


