






"""simple docstring"""

from sklearn.metrics import mean_squared_error

import datasets


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

lowercase__          = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


lowercase__          = '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''


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





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	Dict	):
				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    lowerCamelCase       (   self							:	str	):
				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    lowerCamelCase       (   self							:	Union[str, Any]						,			a_							:	str						,			a_							:	Optional[Any]						,			a_							:	List[Any]=None						,			a_							:	List[Any]="uniform_average"						,			a_							:	int=True	):
				lowerCAmelCase_   :							Optional[int]									=			mean_squared_error(
				    __lowerCamelCase						,			__lowerCamelCase						,			sample_weight=__lowerCamelCase						,			multioutput=__lowerCamelCase						,			squared=__lowerCamelCase	)

				return {"mse": mse}








import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Optional[Any]     	=  TransfoXLTokenizer
	snake_case__							:     List[Any]     	=  False
	snake_case__							:     Tuple     	=  False





	def 						_A (		self				:					str	):
		super().setUp()

		UpperCamelCase     :Dict          =	[
		    """<unk>""",
		    """[CLS]""",
		    """[SEP]""",
		    """want""",
		    """unwanted""",
		    """wa""",
		    """un""",
		    """running""",
		    """,""",
		    """low""",
		    """l""",
		]
		UpperCamelCase     :str          =	os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES["""vocab_file"""]	)
		with open(self.vocab_file ,  """w""" ,  encoding="""utf-8"""	) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]	)	)





	def 						_A (		self				:					List[str] ,  **__lowerCamelCase				:					Any	):
		UpperCamelCase     :Any          =	True
		return TransfoXLTokenizer.from_pretrained(self.tmpdirname ,  **__lowerCamelCase	)





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					int	):
		UpperCamelCase     :List[Any]          =	"""<unk> UNwanted , running"""
		UpperCamelCase     :int          =	"""<unk> unwanted, running"""
		return input_text, output_text





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :List[str]          =	TransfoXLTokenizer(vocab_file=self.vocab_file ,  lower_case=__lowerCamelCase	)

		UpperCamelCase     :Any          =	tokenizer.tokenize("""<unk> UNwanted , running"""	)
		self.assertListEqual(__lowerCamelCase ,  ["""<unk>""", """unwanted""", """,""", """running"""]	)

		self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase	) ,  [0, 4, 8, 7]	)





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :List[Any]          =	TransfoXLTokenizer(lower_case=__lowerCamelCase	)

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """	) ,  ["""hello""", """!""", """how""", """are""", """you""", """?"""]	)





	def 						_A (		self				:					Union[str, Any]	):
		UpperCamelCase     :int          =	TransfoXLTokenizer(lower_case=__lowerCamelCase	)

		self.assertListEqual(
		    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """	) ,  ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]	)





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :Any          =	TransfoXLTokenizer(lower_case=__lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	"""Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
		UpperCamelCase     :Optional[int]          =	[
		    """Hello""",
		    """(""",
		    """bracket""",
		    """)""",
		    """and""",
		    """side""",
		    """@-@""",
		    """scrolled""",
		    """[""",
		    """and""",
		    """]""",
		    """Henry""",
		    """'s""",
		    """$""",
		    """5""",
		    """@,@""",
		    """000""",
		    """with""",
		    """3""",
		    """@.@""",
		    """34""",
		    """m""",
		    """.""",
		    """What""",
		    """'s""",
		    """up""",
		    """!""",
		    """?""",
		]

		self.assertListEqual(tokenizer.tokenize(__lowerCamelCase	) ,  __lowerCamelCase	)

		self.assertEqual(tokenizer.convert_tokens_to_string(__lowerCamelCase	) ,  __lowerCamelCase	)





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     :Any          =	self.get_tokenizer()
		UpperCamelCase     :List[str]          =	len(__lowerCamelCase	)

		tokenizer.add_tokens(["""new1""", """new2"""]	)
		tokenizer.move_added_token("""new1""" ,  1	)

		# Check that moved token is not copied (duplicate)
		self.assertEqual(len(__lowerCamelCase	) ,  original_len + 2	)
		# Check that token is moved to specified id
		self.assertEqual(tokenizer.encode("""new1"""	) ,  [1]	)
		self.assertEqual(tokenizer.decode([1]	) ,  """new1"""	)
