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    :      List[Any]	 =						None


_lowerCamelCase    :      List[Any]	 =						logging.get_logger(__name__)

_lowerCamelCase    :      Optional[Any]	 =						{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase    :      int	 =						{
    '''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    :      int	 =						{
    '''facebook/xglm-564M''': 20_48,
}


class   lowercase			(   a   ):
							lowercase__  :				Union[str, Any]									=							VOCAB_FILES_NAMES
							lowercase__  :				str									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :				Optional[int]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :				Dict									=							["""input_ids""", """attention_mask"""]
							lowercase__  :				str									=							XGLMTokenizer




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				7
												SCREAMING_SNAKE_CASE				=				[F"<madeupword{i}>" for i in range(self.num_madeup_words					)]

												SCREAMING_SNAKE_CASE				=				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__(
												    _UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							**_UpperCamelCase		,							)

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




							def 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[int]		,							_UpperCamelCase      :    Optional[List[int]] = None					)    -> List[int]:



												'''simple docstring'''

												if token_ids_a is None:
																	return [self.sep_token_id] + token_ids_a
												SCREAMING_SNAKE_CASE				=				[self.sep_token_id]
												return sep + token_ids_a + sep + sep + token_ids_a




							def 		__snake_case(  self      :    str		,							_UpperCamelCase      :    List[int]		,							_UpperCamelCase      :    Optional[List[int]] = None					)    -> List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				[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 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Optional[str] = None					)    -> Tuple[str]:



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

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

												return (out_vocab_file,)





from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase    :      Any	 =						'''\
@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    :      str	 =						'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


_lowerCamelCase    :      List[str]	 =						'''
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   lowercase			(   datasets.Metric   ):




							def 		__snake_case(  self      :    int					)    -> str:



												'''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 		__snake_case(  self      :    int					)    -> Tuple:



												'''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 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    str=None		,							_UpperCamelCase      :    Any="uniform_average"					)    -> int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				mean_absolute_error(_UpperCamelCase		,							_UpperCamelCase		,							sample_weight=_UpperCamelCase		,							multioutput=_UpperCamelCase					)

												return {"mae": mae_score}




