
from sklearn.metrics import mean_absolute_error

import datasets


lowerCamelCase_  :       List[str]					      =     """\
@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}
}
"""

lowerCamelCase_  :       Union[str, Any]					      =     """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowerCamelCase_  :       int					      =     """
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.

Returns:
    mae : mean absolute error.
        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.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

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

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""

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






			'''simple docstring'''

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							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 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							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       :				List[Any]  ,		snake_case_       :				str  ,		snake_case_       :				Dict  ,		snake_case_       :				Dict=None  ,		snake_case_       :				Optional[Any]="uniform_average"					):
							UpperCamelCase_:							List[str]		=  mean_absolute_error(snake_case_  ,		snake_case_  ,		sample_weight=snake_case_  ,		multioutput=snake_case_					)

							return {"mae": mae_score}








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							lowerCamelCase_  :       Optional[Any]					      =     None


lowerCamelCase_  :       str					      =     logging.get_logger(__name__)

lowerCamelCase_  :       List[str]					      =     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :       List[str]					      =     {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_  :       Dict					      =     {
    """facebook/xglm-564M""": 20_48,
}

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[Any]    					=       VOCAB_FILES_NAMES
			__UpperCamelCase					:  int    					=       PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase					:  List[str]    					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase					:  Dict    					=       ["""input_ids""", """attention_mask"""]
			__UpperCamelCase					:  Union[str, Any]    					=       XGLMTokenizer

			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=None  ,		snake_case_       :				Any=None  ,		snake_case_       :				Optional[Any]="<s>"  ,		snake_case_       :				Union[str, Any]="</s>"  ,		snake_case_       :				Optional[Any]="</s>"  ,		snake_case_       :				Dict="<s>"  ,		snake_case_       :				int="<unk>"  ,		snake_case_       :				Dict="<pad>"  ,		**snake_case_       :				List[str]  ,		):
							# Compatibility with the original tokenizer
							UpperCamelCase_:							List[Any]		=  7
							UpperCamelCase_:							int		=  [f'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

							UpperCamelCase_:							Any		=  kwargs.get("""additional_special_tokens"""  ,		[]					)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    snake_case_  ,		tokenizer_file=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		unk_token=snake_case_  ,		pad_token=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							Dict		=  vocab_file
							UpperCamelCase_:							Union[str, Any]		=  False if not self.vocab_file else True

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):

							if token_ids_a is None:
											return [self.sep_token_id] + token_ids_a
							UpperCamelCase_:							Optional[Any]		=  [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):
							UpperCamelCase_:							Any		=  [self.sep_token_id]

							if token_ids_a is None:
											return len(sep + token_ids_a					) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a					) * [0]




			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				str  ,		snake_case_       :				Optional[str] = None					):
							if not self.can_save_slow_tokenizer:
											raise ValueError(
											    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
											    """tokenizer."""					)

							if not os.path.isdir(snake_case_					):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''					)
											return
							UpperCamelCase_:							Optional[int]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(snake_case_					):
											copyfile(self.vocab_file  ,		snake_case_					)

							return (out_vocab_file,)






