


'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     '''▁'''

__snake_case							      =     {'''vocab_file''': '''sentencepiece.bpe.model'''}

__snake_case							      =     {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__snake_case							      =     {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
__snake_case							      =     ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				VOCAB_FILES_NAMES
		_a     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a     =				PRETRAINED_VOCAB_FILES_MAP
		_a     =				['input_ids', 'attention_mask']

		_a     =				[]
		_a     =				[]




		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_="<s>"    ,   UpperCamelCase_="</s>"    ,   UpperCamelCase_="</s>"    ,   UpperCamelCase_="<s>"    ,   UpperCamelCase_="<unk>"    ,   UpperCamelCase_="<pad>"    ,   UpperCamelCase_="<mask>"    ,   UpperCamelCase_=None    ,   UpperCamelCase_=None    ,   UpperCamelCase_=None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_=None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= AddedToken(UpperCamelCase_    ,   lstrip=UpperCamelCase_    ,   rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_    ,   UpperCamelCase_							) else mask_token

							UpperCamelCase__   :int												= {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=UpperCamelCase_    ,   eos_token=UpperCamelCase_    ,   unk_token=UpperCamelCase_    ,   sep_token=UpperCamelCase_    ,   cls_token=UpperCamelCase_    ,   pad_token=UpperCamelCase_    ,   mask_token=UpperCamelCase_    ,   tokenizer_file=UpperCamelCase_    ,   src_lang=UpperCamelCase_    ,   tgt_lang=UpperCamelCase_    ,   additional_special_tokens=UpperCamelCase_    ,   sp_model_kwargs=self.sp_model_kwargs    ,   **UpperCamelCase_    ,   )

							UpperCamelCase__   :int												= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(str(UpperCamelCase_							)							)
							UpperCamelCase__   :Optional[int]												= vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							UpperCamelCase__   :Dict												= {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							UpperCamelCase__   :Tuple												= 1

							UpperCamelCase__   :int												= len(self.sp_model							)
							UpperCamelCase__   :Dict												= {
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase_							)
							}
							UpperCamelCase__   :List[Any]												= {v: k for k, v in self.lang_code_to_id.items()}
							UpperCamelCase__   :Any												= len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

							self.fairseq_tokens_to_ids.update(self.lang_code_to_id							)
							UpperCamelCase__   :Union[str, Any]												= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							UpperCamelCase__   :Union[str, Any]												= list(self.lang_code_to_id.keys()							)

							if additional_special_tokens is not None:
												# Only add those special tokens if they are not already there.
												self._additional_special_tokens.extend(
												    [t for t in additional_special_tokens if t not in self._additional_special_tokens]							)

							UpperCamelCase__   :Any												= src_lang if src_lang is not None else '''en_XX'''
							UpperCamelCase__   :Optional[Any]												= self.lang_code_to_id[self._src_lang]
							UpperCamelCase__   :Union[str, Any]												= tgt_lang
							self.set_src_lang_special_tokens(self._src_lang							)




		def __getstate__(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.__dict__.copy()
							UpperCamelCase__   :int												= None
							UpperCamelCase__   :Dict												= self.sp_model.serialized_model_proto()
							return state




		def __setstate__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= d

							# for backward compatibility
							if not hasattr(self    ,   '''sp_model_kwargs'''							):
												UpperCamelCase__   :Optional[int]												= {}

							UpperCamelCase__   :Optional[Any]												= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self._src_lang




		@src_lang.setter
		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= new_src_lang
							self.set_src_lang_special_tokens(self._src_lang							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False							):

							'''simple docstring'''


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

							UpperCamelCase__   :List[str]												= [1] * len(self.prefix_tokens							)
							UpperCamelCase__   :int												= [1] * len(self.suffix_tokens							)
							if token_ids_a is None:
												return prefix_ones + ([0] * len(UpperCamelCase_							)) + suffix_ones
							return prefix_ones + ([0] * len(UpperCamelCase_							)) + ([0] * len(UpperCamelCase_							)) + suffix_ones




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							if token_ids_a is None:
												return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= [self.sep_token_id]
							UpperCamelCase__   :List[Any]												= [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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							if src_lang is None or tgt_lang is None:
												raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''							)
							UpperCamelCase__   :Tuple												= src_lang
							UpperCamelCase__   :Optional[Any]												= self(UpperCamelCase_    ,   add_special_tokens=UpperCamelCase_    ,   return_tensors=UpperCamelCase_    ,   **UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= self.convert_tokens_to_ids(UpperCamelCase_							)
							UpperCamelCase__   :Dict												= tgt_lang_id
							return inputs




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= {self.convert_ids_to_tokens(UpperCamelCase_							): i for i in range(self.vocab_size							)}
							vocab.update(self.added_tokens_encoder							)
							return vocab




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							return self.sp_model.encode(UpperCamelCase_    ,   out_type=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							UpperCamelCase__   :Any												= self.sp_model.PieceToId(UpperCamelCase_							)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= ''''''.join(UpperCamelCase_							).replace(UpperCamelCase_    ,   ''' '''							).strip()
							return out_string




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							if not os.path.isdir(UpperCamelCase_							):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
												return
							UpperCamelCase__   :int												= 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_							) and os.path.isfile(self.vocab_file							):
												copyfile(self.vocab_file    ,   UpperCamelCase_							)
							elif not os.path.isfile(self.vocab_file							):
												with open(UpperCamelCase_    ,   '''wb'''							) as fi:
																	UpperCamelCase__   :Any												= self.sp_model.serialized_model_proto()
																	fi.write(UpperCamelCase_							)

							return (out_vocab_file,)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = "en_XX"    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = "ro_RO"    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= src_lang
							UpperCamelCase__   :Optional[Any]												= tgt_lang
							return super().prepare_seqaseq_batch(UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.set_src_lang_special_tokens(self.src_lang							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.set_tgt_lang_special_tokens(self.tgt_lang							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.lang_code_to_id[src_lang]
							UpperCamelCase__   :int												= []
							UpperCamelCase__   :Union[str, Any]												= [self.eos_token_id, self.cur_lang_code]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.lang_code_to_id[lang]
							UpperCamelCase__   :Optional[Any]												= []
							UpperCamelCase__   :Tuple												= [self.eos_token_id, self.cur_lang_code]
"""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]  )}




