


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	lowerCamelCase__			(      _a      ):




   @slow
   @require_torch
   def 							lowerCamelCase_   (   self						:						Any							):




         '''simple docstring'''







         lowerCamelCase__:						Tuple							   = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""    ,    """prajjwal1/bert-tiny"""							)
         lowerCamelCase__:						Union[str, Any]							   = BertTokenizer.from_pretrained("""bert-base-uncased"""							)

         lowerCamelCase__:						Optional[int]							   = bertabert.config.encoder.vocab_size
         lowerCamelCase__:						Any							   = tokenizer.sep_token_id
         lowerCamelCase__:						Optional[int]							   = tokenizer.cls_token_id
         lowerCamelCase__:						List[Any]							   = 128

         lowerCamelCase__:						int							   = datasets.load_dataset("""cnn_dailymail"""    ,    """3.0.0"""    ,    split="""train[:1%]"""							)
         lowerCamelCase__:						str							   = datasets.load_dataset("""cnn_dailymail"""    ,    """3.0.0"""    ,    split="""validation[:1%]"""							)

         lowerCamelCase__:						str							   = train_dataset.select(range(32							)							)
         lowerCamelCase__:						int							   = val_dataset.select(range(16							)							)

         lowerCamelCase__:						Optional[Any]							   = 4

         def _map_to_encoder_decoder_inputs(__a						:						Union[str, Any]							):
               # Tokenizer will automatically set [BOS] <text> [EOS]
               lowerCamelCase__:						List[str]							   = tokenizer(batch["""article"""]    ,    padding="""max_length"""    ,    truncation=_SCREAMING_SNAKE_CASE    ,    max_length=512							)
               lowerCamelCase__:						str							   = tokenizer(batch["""highlights"""]    ,    padding="""max_length"""    ,    truncation=_SCREAMING_SNAKE_CASE    ,    max_length=128							)
               lowerCamelCase__:						List[str]							   = inputs.input_ids
               lowerCamelCase__:						Optional[Any]							   = inputs.attention_mask

               lowerCamelCase__:						Optional[Any]							   = outputs.input_ids
               lowerCamelCase__:						Optional[int]							   = outputs.input_ids.copy()
               lowerCamelCase__:						Any							   = [
                   [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
               ]
               lowerCamelCase__:						Dict							   = outputs.attention_mask

               assert all(len(_SCREAMING_SNAKE_CASE							) == 512 for x in inputs.input_ids							)
               assert all(len(_SCREAMING_SNAKE_CASE							) == 128 for x in outputs.input_ids							)

               return batch

         def _compute_metrics(__a						:						Tuple							):
               lowerCamelCase__:						List[str]							   = pred.label_ids
               lowerCamelCase__:						Dict							   = pred.predictions

               # all unnecessary tokens are removed
               lowerCamelCase__:						List[str]							   = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,    skip_special_tokens=_SCREAMING_SNAKE_CASE							)
               lowerCamelCase__:						List[str]							   = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,    skip_special_tokens=_SCREAMING_SNAKE_CASE							)

               lowerCamelCase__:						str							   = sum([int(pred_str[i] == label_str[i]							) for i in range(len(_SCREAMING_SNAKE_CASE							)							)]							) / len(_SCREAMING_SNAKE_CASE							)

               return {"accuracy": accuracy}

         # map train dataset
         lowerCamelCase__:						Union[str, Any]							   = train_dataset.map(
             _map_to_encoder_decoder_inputs    ,    batched=_SCREAMING_SNAKE_CASE    ,    batch_size=_SCREAMING_SNAKE_CASE    ,    remove_columns=["""article""", """highlights"""]    ,    )
         train_dataset.set_format(
             type="""torch"""    ,    columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]    ,    )

         # same for validation dataset
         lowerCamelCase__:						List[str]							   = val_dataset.map(
             _map_to_encoder_decoder_inputs    ,    batched=_SCREAMING_SNAKE_CASE    ,    batch_size=_SCREAMING_SNAKE_CASE    ,    remove_columns=["""article""", """highlights"""]    ,    )
         val_dataset.set_format(
             type="""torch"""    ,    columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]    ,    )

         lowerCamelCase__:						Optional[int]							   = self.get_auto_remove_tmp_dir()

         lowerCamelCase__:						List[Any]							   = SeqaSeqTrainingArguments(
             output_dir=_SCREAMING_SNAKE_CASE    ,    per_device_train_batch_size=_SCREAMING_SNAKE_CASE    ,    per_device_eval_batch_size=_SCREAMING_SNAKE_CASE    ,    predict_with_generate=_SCREAMING_SNAKE_CASE    ,    evaluation_strategy="""steps"""    ,    do_train=_SCREAMING_SNAKE_CASE    ,    do_eval=_SCREAMING_SNAKE_CASE    ,    warmup_steps=0    ,    eval_steps=2    ,    logging_steps=2    ,    )

         # instantiate trainer
         lowerCamelCase__:						int							   = SeqaSeqTrainer(
             model=_SCREAMING_SNAKE_CASE    ,    args=_SCREAMING_SNAKE_CASE    ,    compute_metrics=_compute_metrics    ,    train_dataset=_SCREAMING_SNAKE_CASE    ,    eval_dataset=_SCREAMING_SNAKE_CASE    ,    tokenizer=_SCREAMING_SNAKE_CASE    ,    )

         # start training
         trainer.train()












import numpy as np

import datasets


__a							     =					'\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

__a							     =					'\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

__a							     =					'\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'

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






    """simple docstring"""





    def 				a__ ( self    )					->						Union[str, Any]:
         return datasets.MetricInfo(
             description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
                 {
                     '''X''': datasets.Sequence(datasets.Value('''float'''  ,id='''sequence'''    )  ,id='''X'''    ),
                 }    )  ,)





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         # convert to numpy arrays
         UpperCAmelCase_     :				str  =     np.array(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     np.array(_SCREAMING_SNAKE_CASE    )

         # Assert that arrays are 2D
         if len(X.shape    ) != 2:
              raise ValueError('''Expected `X` to be a 2D vector'''    )
         if len(reference_distribution.shape    ) != 2:
              raise ValueError('''Expected `reference_distribution` to be a 2D vector'''    )
         if reference_distribution.shape[0] < 2:
              raise ValueError(
                  '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''    )

         # Get mahalanobis distance for each prediction
         UpperCAmelCase_     :				List[str]  =     X - np.mean(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Dict  =     np.cov(reference_distribution.T    )
         try:
              UpperCAmelCase_     :				Any  =     np.linalg.inv(_SCREAMING_SNAKE_CASE    )
         except np.linalg.LinAlgError:
              UpperCAmelCase_     :				List[str]  =     np.linalg.pinv(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     np.dot(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     np.dot(_SCREAMING_SNAKE_CASE  ,X_minus_mu.T    ).diagonal()

         return {"mahalanobis": mahal_dist}