from sklearn.metrics import mean_absolute_error

import datasets


__SCREAMING_SNAKE_CASE						:    Any     = '\\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'

__SCREAMING_SNAKE_CASE						:    Optional[Any]     = '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


__SCREAMING_SNAKE_CASE						:    List[str]     = '\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\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'



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



							def        UpperCamelCase       (						self				):
													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_absolute_error.html"
													    ]      ,				)



							def        UpperCamelCase       (						self				):
													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        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_=None      ,				lowercase_="uniform_average"				):
													_snake_case						:     Dict				=    mean_absolute_error(lowercase_      ,				lowercase_      ,				sample_weight=lowercase_      ,				multioutput=lowercase_				)

													return {"mae": mae_score}

"""simple docstring"""

from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRContextEncoderTokenizer
	lowercase_        =	DPRContextEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRQuestionEncoderTokenizer
	lowercase_        =	DPRQuestionEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRReaderTokenizer
	lowercase_        =	DPRReaderTokenizerFast
	lowercase_        =	True




	@slow
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			str		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			int		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    tokenizer.encode('text sequence ' * 4		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Union[str, Any]		=    [[101] + text_a + [102] + text_a + [102] + text_a]
			lowerCamelCase     :			List[str]		=    BatchEncoding({'input_ids': input_ids}					)

			lowerCamelCase     :			Optional[int]		=    [[0] * len(input_ids[0]					)]
			lowerCamelCase     :			str		=    [[0] * len(input_ids[0]					)]
			lowerCamelCase     :			List[Any]		=    [0]
			lowerCamelCase     :			Tuple		=    DPRReaderOutput(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)

			lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    8, 9
			lowerCamelCase     :			Optional[int]		=    10
			lowerCamelCase     :			int		=    10
			lowerCamelCase     :			List[Any]		=    tokenizer.decode_best_spans(UpperCAmelCase_		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].start_index		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].end_index		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].doc_id		, 0					)




	@slow
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Union[str, Any]		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			List[str]		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('text sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    [101] + text_a + [102] + text_a + [102] + text_a
			lowerCamelCase     :			Optional[Any]		=    tokenizer(questions=['question sequence']		, titles=['title sequence']		, texts=['text sequence']					)
			self.assertIn('input_ids'		, UpperCAmelCase_					)
			self.assertIn('attention_mask'		, UpperCAmelCase_					)
			self.assertListEqual(encoded_input['input_ids'][0]		, UpperCAmelCase_					)






