




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 		__A   (      UpperCamelCase__      ):

						@slow
						@require_torch
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

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

										__magic_name__						:						str										=bertabert.config.encoder.vocab_size
										__magic_name__						:						Union[str, Any]										=tokenizer.sep_token_id
										__magic_name__						:						Optional[Any]										=tokenizer.cls_token_id
										__magic_name__						:						str										=1_28

										__magic_name__						:						List[str]										=datasets.load_dataset("""cnn_dailymail"""     ,			"""3.0.0"""     ,			split="""train[:1%]"""			)
										__magic_name__						:						Union[str, Any]										=datasets.load_dataset("""cnn_dailymail"""     ,			"""3.0.0"""     ,			split="""validation[:1%]"""			)

										__magic_name__						:						Union[str, Any]										=train_dataset.select(range(32			)			)
										__magic_name__						:						str										=val_dataset.select(range(16			)			)

										__magic_name__						:						int										=4

										def _map_to_encoder_decoder_inputs(__snake_case :Optional[int]			):
														# Tokenizer will automatically set [BOS] <text> [EOS]
														__magic_name__						:						Dict										=tokenizer(batch["""article"""]     ,			padding="""max_length"""     ,			truncation=__snake_case     ,			max_length=5_12			)
														__magic_name__						:						Dict										=tokenizer(batch["""highlights"""]     ,			padding="""max_length"""     ,			truncation=__snake_case     ,			max_length=1_28			)
														__magic_name__						:						Optional[int]										=inputs.input_ids
														__magic_name__						:						Tuple										=inputs.attention_mask

														__magic_name__						:						Any										=outputs.input_ids
														__magic_name__						:						Tuple										=outputs.input_ids.copy()
														__magic_name__						:						Union[str, Any]										=[
														    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
														]
														__magic_name__						:						List[str]										=outputs.attention_mask

														assert all(len(__snake_case			) == 5_12 for x in inputs.input_ids			)
														assert all(len(__snake_case			) == 1_28 for x in outputs.input_ids			)

														return batch

										def _compute_metrics(__snake_case :Tuple			):
														__magic_name__						:						Tuple										=pred.label_ids
														__magic_name__						:						Any										=pred.predictions

														# all unnecessary tokens are removed
														__magic_name__						:						Optional[int]										=tokenizer.batch_decode(__snake_case     ,			skip_special_tokens=__snake_case			)
														__magic_name__						:						List[Any]										=tokenizer.batch_decode(__snake_case     ,			skip_special_tokens=__snake_case			)

														__magic_name__						:						Optional[Any]										=sum([int(pred_str[i] == label_str[i]			) for i in range(len(__snake_case			)			)]			) / len(__snake_case			)

														return {"accuracy": accuracy}

										# map train dataset
										__magic_name__						:						Dict										=train_dataset.map(
										    _map_to_encoder_decoder_inputs     ,			batched=__snake_case     ,			batch_size=__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
										__magic_name__						:						List[str]										=val_dataset.map(
										    _map_to_encoder_decoder_inputs     ,			batched=__snake_case     ,			batch_size=__snake_case     ,			remove_columns=["""article""", """highlights"""]     ,			)
										val_dataset.set_format(
										    type="""torch"""     ,			columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]     ,			)

										__magic_name__						:						Tuple										=self.get_auto_remove_tmp_dir()

										__magic_name__						:						Optional[int]										=SeqaSeqTrainingArguments(
										    output_dir=__snake_case     ,			per_device_train_batch_size=__snake_case     ,			per_device_eval_batch_size=__snake_case     ,			predict_with_generate=__snake_case     ,			evaluation_strategy="""steps"""     ,			do_train=__snake_case     ,			do_eval=__snake_case     ,			warmup_steps=0     ,			eval_steps=2     ,			logging_steps=2     ,			)

										# instantiate trainer
										__magic_name__						:						List[str]										=SeqaSeqTrainer(
										    model=__snake_case     ,			args=__snake_case     ,			compute_metrics=_compute_metrics     ,			train_dataset=__snake_case     ,			eval_dataset=__snake_case     ,			tokenizer=__snake_case     ,			)

										# start training
										trainer.train()










from sklearn.metrics import matthews_corrcoef

import datasets


UpperCAmelCase_		:					Dict          =  "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

UpperCAmelCase_		:					Any          =  "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

UpperCAmelCase_		:					Dict          =  "\\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"




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

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,			citation=_CITATION     ,			inputs_description=_KWARGS_DESCRIPTION     ,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""int32"""			),
										            """references""": datasets.Value("""int32"""			),
										        }			)     ,			reference_urls=[
										        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
										    ]     ,			)


						def      A__    (		self :Tuple     ,			__snake_case :str     ,			__snake_case :Tuple     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										return {
										    "matthews_correlation": float(matthews_corrcoef(__snake_case     ,			__snake_case     ,			sample_weight=__snake_case			)			),
										}




