





"""simple docstring"""




import os
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


a						: int     	=			logging.get_logger(__name__)

a						: Tuple     	=			'▁'

a						: int     	=			{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

a						: Any     	=			{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

a						: List[Any]     	=			{'vinai/bartpho-syllable': 1024}


class        __UpperCamelCase				(						SCREAMING_SNAKE_CASE_						):
							lowerCamelCase   :					int      					=VOCAB_FILES_NAMES
							lowerCamelCase   :					str      					=PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase   :					Optional[Any]      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase   :					List[Any]      					=["input_ids", "attention_mask"]






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="</s>"      ,					lowerCAmelCase__="</s>"      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="<unk>"      ,					lowerCAmelCase__="<pad>"      ,					lowerCAmelCase__="<mask>"      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> Optional[int]:
								# Mask token behave like a normal word, i.e. include the space before it
								a      :		List[str]	=				AddedToken(a__      ,					lstrip=a__      ,					rstrip=a__      ) if isinstance(a__      ,					a__      ) else mask_token

								a      :		str	=				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=a__      ,					eos_token=a__      ,					unk_token=a__      ,					sep_token=a__      ,					cls_token=a__      ,					pad_token=a__      ,					mask_token=a__      ,					sp_model_kwargs=self.sp_model_kwargs      ,					**a__      ,					)

								a      :		List[str]	=				vocab_file
								a      :		str	=				monolingual_vocab_file
								a      :		List[str]	=				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(a__      )      )

								# Load the reduced vocab

								# Keep order of special tokens for backward compatibility
								a      :		Optional[Any]	=				{}
								a      :		Tuple	=				0
								for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
									if str(a__      ) not in self.fairseq_tokens_to_ids:
										a      :		Dict	=				cnt
										cnt += 1
								with open(a__      ,					"r"      ,					encoding="utf-8"      ) as f:
									for line in f.readlines():
										a      :		str	=				line.strip().split()[0]
										a      :		Any	=				len(self.fairseq_tokens_to_ids      )
								if str(a__      ) not in self.fairseq_tokens_to_ids:
									a      :		str	=				len(self.fairseq_tokens_to_ids      )

								a      :		List[Any]	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






							def __getstate__(   self      )	-> Union[str, Any]:
								a      :		Dict	=				self.__dict__.copy()
								a      :		str	=				None
								a      :		Dict	=				self.sp_model.serialized_model_proto()
								return state






							def __setstate__(   self      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								a      :		Tuple	=				d

								# for backward compatibility
								if not hasattr(self      ,					"sp_model_kwargs"      ):
									a      :		int	=				{}

								a      :		Any	=				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Optional[int]:

								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								a      :		Optional[int]	=				[self.cls_token_id]
								a      :		Any	=				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      )	-> List[Any]:

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

								if token_ids_a is None:
									return [1] + ([0] * len(a__      )) + [1]
								return [1] + ([0] * len(a__      )) + [1, 1] + ([0] * len(a__      )) + [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[Any]:
								a      :		Union[str, Any]	=				[self.sep_token_id]
								a      :		int	=				[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]






							@property
							def     __a   (   self      )	-> List[Any]:
								return len(self.fairseq_ids_to_tokens      )






							def     __a   (   self      )	-> Dict:
								a      :		Optional[Any]	=				{self.convert_ids_to_tokens(a__      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Optional[int]:
								return self.sp_model.encode(a__      ,					out_type=a__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> int:
								if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
								else:
									return self.unk_token_id






							def     __a   (   self      ,					lowerCAmelCase__      )	-> str:
								return self.fairseq_ids_to_tokens[index]






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								a      :		Optional[int]	=				"""""".join(a__      ).replace(a__      ,					" "      ).strip()
								return out_string






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Dict:
								if not os.path.isdir(a__      ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
									return
								a      :		Optional[int]	=				os.path.join(
								    a__      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
								a      :		Optional[Any]	=				os.path.join(
								    a__      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]      ,					)

								if os.path.abspath(self.vocab_file      ) != os.path.abspath(a__      ) and os.path.isfile(self.vocab_file      ):
									copyfile(self.vocab_file      ,					a__      )
								elif not os.path.isfile(self.vocab_file      ):
									with open(a__      ,					"wb"      ) as fi:
										a      :		List[Any]	=				self.sp_model.serialized_model_proto()
										fi.write(a__      )

								if os.path.abspath(self.monolingual_vocab_file      ) != os.path.abspath(
								    a__      ) and os.path.isfile(self.monolingual_vocab_file      ):
									copyfile(self.monolingual_vocab_file      ,					a__      )
								elif not os.path.isfile(self.monolingual_vocab_file      ):
									with open(a__      ,					"w"      ,					encoding="utf-8"      ) as fp:
										for token in self.fairseq_tokens_to_ids:
											if token not in self.all_special_tokens:
												fp.write(f"""{str(a__      )} \n"""      )

								return out_vocab_file, out_monolingual_vocab_file



"""simple docstring"""




from scipy.stats import pearsonr

import datasets


_a      :		str           =   '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


_a      :		List[str]           =   '\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


_a      :		List[Any]           =   '\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,	_KWARGS_DESCRIPTION		)
class 						__A							( datasets.Metric		):






     def     __A  (		self  ):
       return datasets.MetricInfo(
           description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
               {
                   """predictions""": datasets.Value("""float"""  ),
                   """references""": datasets.Value("""float"""  ),
               }  )				,     reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]				,     )






     def     __A  (		self				,     a__				,     a__				,     a__=False  ):
       if return_pvalue:
         _lowerCAmelCase						:       List[Any]  			=     pearsonr(a__				,     a__  )
         return {"pearsonr": results[0], "p-value": results[1]}
       else:
         return {"pearsonr": float(pearsonr(a__				,     a__  )[0]  )}




