


'''simple docstring'''

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

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


if is_sentencepiece_available():
       from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
       __lowercase:		Dict    						=  None


__lowercase:		str    						=  logging.get_logger(__name__)

__lowercase:		Optional[int]    						=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__lowercase:		Optional[int]    						=  {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

__lowercase:		Union[str, Any]    						=  {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Union[str, Any]            =       VOCAB_FILES_NAMES
 _lowerCamelCase			:       Optional[int]            =       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase			:       str            =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase			:       List[str]            =       ['input_ids', 'attention_mask']
 _lowerCamelCase			:       Dict            =       XLMRobertaTokenizer

 def __init__(			self			:						List[str],       a_			:						List[str]=None,       a_			:						Any=None,       a_			:						Any="<s>",       a_			:						List[str]="</s>",       a_			:						int="</s>",       a_			:						Dict="<s>",       a_			:						Tuple="<unk>",       a_			:						List[str]="<pad>",       a_			:						str="<mask>",       **a_			:						str,       ):





    """simple docstring"""



    UpperCamelCase__			    =    AddedToken(a_,       lstrip=a_,       rstrip=a_       ) if isinstance(a_,       a_       ) else mask_token

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

    UpperCamelCase__			    =    vocab_file
    UpperCamelCase__			    =    False if not self.vocab_file else True

 def 		lowercase_    (			self			:						int,       a_			:						List[int],       a_			:						Optional[List[int]] = None       ):





    """simple docstring"""



    if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCamelCase__			    =    [self.cls_token_id]
    UpperCamelCase__			    =    [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

 def 		lowercase_    (			self			:						List[str],       a_			:						List[int],       a_			:						Optional[List[int]] = None       ):





    """simple docstring"""



    UpperCamelCase__			    =    [self.sep_token_id]
    UpperCamelCase__			    =    [self.cls_token_id]

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

 def 		lowercase_    (			self			:						Dict,       a_			:						str,       a_			:						Optional[str] = None       ):





    """simple docstring"""



    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(a_       ):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory.'       )
       return
    UpperCamelCase__			    =    os.path.join(
        a_,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

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

    return (out_vocab_file,)



from sklearn.metrics import mean_absolute_error

import datasets


a					:				Optional[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'

a					:				int       =      '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


a					:				Any       =      '\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   lowerCamelCase						(	datasets.Metric  ):

       def 					UpperCAmelCase					(      self       ):






        '''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_absolute_error.html'''
            ]						,    )

       def 					UpperCAmelCase					(      self       ):






        '''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 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__=None						,    a__="uniform_average"       ):






        '''simple docstring'''






        snake_case :			str							=   mean_absolute_error(a__						,    a__						,    sample_weight=a__						,    multioutput=a__       )

        return {"mae": mae_score}






