

import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case							:     Tuple 						=	logging.get_logger(__name__)
__snake_case							:     Any 						=	{"""vocab_file""": """spiece.model"""}

__snake_case							:     int 						=	{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

__snake_case							:     Any 						=	{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

__snake_case							:     List[str] 						=	"""▁"""



class    A__(a_    ):
						"""simple docstring"""






						_A  :							str   			=	VOCAB_FILES_NAMES
						_A  :							Optional[int]   			=	PRETRAINED_VOCAB_FILES_MAP
						_A  :							Dict   			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A  :							Optional[Any]   			=	['''input_ids''', '''token_type_ids''']





						def __init__(  self       ,     _lowercase       ,     _lowercase=False       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="<unk>"       ,     _lowercase="[SEP]"       ,     _lowercase="<pad>"       ,     _lowercase="[CLS]"       ,     _lowercase="[MASK]"       ,     _lowercase = None       ,     **_lowercase       ,     )      ->				None:
									# Mask token behave like a normal word, i.e. include the space before it and
									# is included in the raw text, there should be a match in a non-normalized sentence.
									a_		:		List[str]				    =				(
									    AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase       ,     normalized=_lowercase  )
									    if isinstance(_lowercase       ,     _lowercase  )
									    else mask_token
									)

									a_		:		List[str]				    =				{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    do_lower_case=_lowercase       ,     remove_space=_lowercase       ,     keep_accents=_lowercase       ,     unk_token=_lowercase       ,     sep_token=_lowercase       ,     pad_token=_lowercase       ,     cls_token=_lowercase       ,     mask_token=_lowercase       ,     sp_model_kwargs=self.sp_model_kwargs       ,     **_lowercase       ,     )

									a_		:		Tuple				    =				do_lower_case
									a_		:		str				    =				remove_space
									a_		:		List[Any]				    =				keep_accents
									a_		:		Union[str, Any]				    =				vocab_file

									a_		:		Union[str, Any]				    =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(_lowercase  )





						@property
						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									return len(self.sp_model  )





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		str				    =				{self.convert_ids_to_tokens(_lowercase  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab





						def __getstate__(  self  )      ->				List[Any]:
									a_		:		List[Any]				    =				self.__dict__.copy()
									a_		:		str				    =				None
									return state





						def __setstate__(  self       ,     _lowercase  )      ->				Dict:
									a_		:		int				    =				d

									# for backward compatibility
									if not hasattr(self       ,     """sp_model_kwargs"""  ):
												a_		:		List[str]				    =				{}

									a_		:		str				    =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(self.vocab_file  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[str]:
									if self.remove_space:
												a_		:		Tuple				    =				""" """.join(inputs.strip().split()  )
									else:
												a_		:		Union[str, Any]				    =				inputs
									a_		:		int				    =				outputs.replace("""``"""       ,     """\""""  ).replace("""''"""       ,     """\""""  )

									if not self.keep_accents:
												a_		:		Dict				    =				unicodedata.normalize("""NFKD"""       ,     _lowercase  )
												a_		:		Optional[int]				    =				"""""".join([c for c in outputs if not unicodedata.combining(_lowercase  )]  )
									if self.do_lower_case:
												a_		:		int				    =				outputs.lower()

									return outputs





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[str]:
									a_		:		Tuple				    =				self.preprocess_text(_lowercase  )
									a_		:		Optional[Any]				    =				self.sp_model.encode(_lowercase       ,     out_type=_lowercase  )
									a_		:		Optional[Any]				    =				[]
									for piece in pieces:
												if len(_lowercase  ) > 1 and piece[-1] == str(""","""  ) and piece[-2].isdigit():
															a_		:		List[Any]				    =				self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowercase       ,     """"""  )  )
															if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																		if len(cur_pieces[0]  ) == 1:
																					a_		:		Dict				    =				cur_pieces[1:]
																		else:
																					a_		:		Dict				    =				cur_pieces[0][1:]
															cur_pieces.append(piece[-1]  )
															new_pieces.extend(_lowercase  )
												else:
															new_pieces.append(_lowercase  )

									return new_pieces





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Dict:
									return self.sp_model.PieceToId(_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				int:
									return self.sp_model.IdToPiece(_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[Any]:
									a_		:		str				    =				[]
									a_		:		Any				    =				""""""
									a_		:		str				    =				False
									for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
															if not prev_is_special:
																		out_string += " "
															out_string += self.sp_model.decode(_lowercase  ) + token
															a_		:		Optional[Any]				    =				True
															a_		:		Any				    =				[]
												else:
															current_sub_tokens.append(_lowercase  )
															a_		:		Optional[Any]				    =				False
									out_string += self.sp_model.decode(_lowercase  )
									return out_string.strip()





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = False       ,     _lowercase = None       ,     _lowercase = True       ,     **_lowercase       ,     )      ->				str:
									a_		:		Optional[Any]				    =				kwargs.pop("""use_source_tokenizer"""       ,     _lowercase  )

									a_		:		Tuple				    =				self.convert_ids_to_tokens(_lowercase       ,     skip_special_tokens=_lowercase  )

									# To avoid mixing byte-level and unicode for byte-level BPT
									# we need to build string separately for added tokens and byte-level tokens
									# cf. https://github.com/huggingface/transformers/issues/1133
									a_		:		List[str]				    =				[]
									a_		:		List[Any]				    =				[]
									for token in filtered_tokens:
												if skip_special_tokens and token in self.all_special_ids:
															continue
												if token in self.added_tokens_encoder:
															if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(_lowercase  )  )
																		a_		:		int				    =				[]
															sub_texts.append(_lowercase  )
												else:
															current_sub_text.append(_lowercase  )
									if current_sub_text:
												sub_texts.append(self.convert_tokens_to_string(_lowercase  )  )

									# Mimic the behavior of the Rust tokenizer:
									# No space after <unk>
									if spaces_between_special_tokens:
												a_		:		Any				    =				re.sub(r"""(<unk>) """       ,     r"""\1"""       ,     """ """.join(_lowercase  )  )
									else:
												a_		:		Optional[Any]				    =				"""""".join(_lowercase  )

									a_		:		Tuple				    =				(
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
												a_		:		Optional[int]				    =				self.clean_up_tokenization(_lowercase  )
												return clean_text
									else:
												return text





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				List[int]:
									a_		:		List[str]				    =				[self.sep_token_id]
									a_		:		List[Any]				    =				[self.cls_token_id]
									if token_ids_a is None:
												return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None       ,     _lowercase = False  )      ->				List[int]:

									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=_lowercase       ,     token_ids_a=_lowercase       ,     already_has_special_tokens=_lowercase  )

									if token_ids_a is not None:
												return [1] + ([0] * len(_lowercase  )) + [1] + ([0] * len(_lowercase  )) + [1]
									return [1] + ([0] * len(_lowercase  )) + [1]





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				List[int]:
									a_		:		Dict				    =				[self.sep_token_id]
									a_		:		Tuple				    =				[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  ) * [0] + len(token_ids_a + sep  ) * [1]





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				Tuple[str]:
									if not os.path.isdir(_lowercase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
									a_		:		Optional[Any]				    =				os.path.join(
									    _lowercase       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(_lowercase  ) and os.path.isfile(self.vocab_file  ):
												copyfile(self.vocab_file       ,     _lowercase  )
									elif not os.path.isfile(self.vocab_file  ):
												with open(_lowercase       ,     """wb"""  ) as fi:
															a_		:		int				    =				self.sp_model.serialized_model_proto()
															fi.write(_lowercase  )

									return (out_vocab_file,)





from sklearn.metrics import mean_absolute_error

import datasets


__snake_case							:     List[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}
}
"""

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


__snake_case							:     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    A__(datasets.Metric    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									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  )      ->				Union[str, 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 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase=None       ,     _lowercase="uniform_average"  )      ->				Union[str, Any]:
									a_		:		Any				    =				mean_absolute_error(_lowercase       ,     _lowercase       ,     sample_weight=_lowercase       ,     multioutput=_lowercase  )

									return {"mae": mae_score}


