





"""simple docstring"""





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			):
       """simple docstring"""




       @slow
       @require_torch
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'       ,					'prajjwal1/bert-tiny' )
         lowerCAmelCase__     :Tuple											=				BertTokenizer.from_pretrained('bert-base-uncased' )

         lowerCAmelCase__     :str											=				bertabert.config.encoder.vocab_size
         lowerCAmelCase__     :Tuple											=				tokenizer.sep_token_id
         lowerCAmelCase__     :int											=				tokenizer.cls_token_id
         lowerCAmelCase__     :Any											=				1_2_8

         lowerCAmelCase__     :List[str]											=				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__     :Optional[Any]											=				train_dataset.select(range(3_2 ) )
         lowerCAmelCase__     :Tuple											=				val_dataset.select(range(1_6 ) )

         lowerCAmelCase__     :str											=				4

         def _map_to_encoder_decoder_inputs(__UpperCAmelCase ):
           # Tokenizer will automatically set [BOS] <text> [EOS]
           lowerCAmelCase__     :int											=				tokenizer(batch['article']       ,					padding='max_length'       ,					truncation=__UpperCAmelCase       ,					max_length=5_1_2 )
           lowerCAmelCase__     :int											=				tokenizer(batch['highlights']       ,					padding='max_length'       ,					truncation=__UpperCAmelCase       ,					max_length=1_2_8 )
           lowerCAmelCase__     :Any											=				inputs.input_ids
           lowerCAmelCase__     :Union[str, Any]											=				inputs.attention_mask

           lowerCAmelCase__     :Dict											=				outputs.input_ids
           lowerCAmelCase__     :Optional[Any]											=				outputs.input_ids.copy()
           lowerCAmelCase__     :List[str]											=				[
               [-1_0_0 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(__UpperCAmelCase ) == 5_1_2 for x in inputs.input_ids )
           assert all(len(__UpperCAmelCase ) == 1_2_8 for x in outputs.input_ids )

           return batch

         def _compute_metrics(__UpperCAmelCase ):
           lowerCAmelCase__     :Optional[Any]											=				pred.label_ids
           lowerCAmelCase__     :List[Any]											=				pred.predictions

           # all unnecessary tokens are removed
           lowerCAmelCase__     :Tuple											=				tokenizer.batch_decode(__UpperCAmelCase       ,					skip_special_tokens=__UpperCAmelCase )
           lowerCAmelCase__     :Any											=				tokenizer.batch_decode(__UpperCAmelCase       ,					skip_special_tokens=__UpperCAmelCase )

           lowerCAmelCase__     :Dict											=				sum([int(pred_str[i] == label_str[i] ) for i in range(len(__UpperCAmelCase ) )] ) / len(__UpperCAmelCase )

           return {"accuracy": accuracy}

         # map train dataset
         lowerCAmelCase__     :str											=				train_dataset.map(
             _map_to_encoder_decoder_inputs       ,					batched=__UpperCAmelCase       ,					batch_size=__UpperCAmelCase       ,					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[Any]											=				val_dataset.map(
             _map_to_encoder_decoder_inputs       ,					batched=__UpperCAmelCase       ,					batch_size=__UpperCAmelCase       ,					remove_columns=['article', 'highlights']       ,					)
         val_dataset.set_format(
             type='torch'       ,					columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']       ,					)

         lowerCAmelCase__     :str											=				self.get_auto_remove_tmp_dir()

         lowerCAmelCase__     :List[str]											=				SeqaSeqTrainingArguments(
             output_dir=__UpperCAmelCase       ,					per_device_train_batch_size=__UpperCAmelCase       ,					per_device_eval_batch_size=__UpperCAmelCase       ,					predict_with_generate=__UpperCAmelCase       ,					evaluation_strategy='steps'       ,					do_train=__UpperCAmelCase       ,					do_eval=__UpperCAmelCase       ,					warmup_steps=0       ,					eval_steps=2       ,					logging_steps=2       ,					)

         # instantiate trainer
         lowerCAmelCase__     :Any											=				SeqaSeqTrainer(
             model=__UpperCAmelCase       ,					args=__UpperCAmelCase       ,					compute_metrics=_compute_metrics       ,					train_dataset=__UpperCAmelCase       ,					eval_dataset=__UpperCAmelCase       ,					tokenizer=__UpperCAmelCase       ,					)

         # start training
         trainer.train()






from sklearn.metrics import mean_squared_error

import datasets


__A								=       "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

__A								=       "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


__A								=       "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"







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

			"""simple docstring"""







			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION     ,		citation=_CITATION     ,		inputs_description=_KWARGS_DESCRIPTION     ,		features=datasets.Features(self._get_feature_types()				)     ,		reference_urls=[
							        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
							    ]     ,		)


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							if self.config_name == "multilist":
											return {
											    "predictions": datasets.Sequence(datasets.Value('float'				)				),
											    "references": datasets.Sequence(datasets.Value('float'				)				),
											}
							else:
											return {
											    "predictions": datasets.Value('float'				),
											    "references": datasets.Value('float'				),
											}






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None     ,		lowerCamelCase__="uniform_average"     ,		lowerCamelCase__=True				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       mean_squared_error(
							    lowerCamelCase__     ,		lowerCamelCase__     ,		sample_weight=lowerCamelCase__     ,		multioutput=lowerCamelCase__     ,		squared=lowerCamelCase__				)

							return {"mse": mse}


