

from scipy.stats import pearsonr

import datasets


_lowerCamelCase    :       Any        =							"\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"


_lowerCamelCase    :       Dict        =							"\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"


_lowerCamelCase    :       Union[str, 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  __UpperCAmelCase					(	datasets.Metric						):



   def 	__magic_name__   (							self							:					str						):
     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 	__magic_name__   (							self							:					List[str],		__A							:					Tuple,		__A							:					str,		__A							:					int=False						):
     if return_pvalue:
       UpperCAmelCase	:							List[str]						=			pearsonr(__A,		__A						)
       return {"pearsonr": results[0], "p-value": results[1]}
     else:
       return {"pearsonr": float(pearsonr(__A,		__A						)[0]						)}








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

from tokenizers import processors

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


if is_sentencepiece_available():
     from .tokenization_nllb import NllbTokenizer
else:
     _lowerCamelCase    :       List[Any]        =							None


_lowerCamelCase    :       str        =							logging.get_logger(__name__)


_lowerCamelCase    :       str        =							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_lowerCamelCase    :       str        =							{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCamelCase    :       Any        =							{
    "facebook/nllb-large-en-ro": 1_0_2_4,
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
_lowerCamelCase    :       int        =							["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]





class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					VOCAB_FILES_NAMES
   UpperCamelCase       					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase       					=					PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase       					=					["""input_ids""", """attention_mask"""]
   UpperCamelCase       					=					NllbTokenizer

   UpperCamelCase       					=					[]
   UpperCamelCase       					=					[]



   def __init__(							self							:					Optional[Any],		__A							:					Tuple=None,		__A							:					int=None,		__A							:					List[Any]="<s>",		__A							:					Tuple="</s>",		__A							:					Any="</s>",		__A							:					Optional[Any]="<s>",		__A							:					Tuple="<unk>",		__A							:					str="<pad>",		__A							:					Dict="<mask>",		__A							:					Optional[Any]=None,		__A							:					List[Any]=None,		__A							:					List[Any]=None,		__A							:					str=False,		**__A							:					Tuple,		):
     # Mask token behave like a normal word, i.e. include the space before it
     UpperCAmelCase	:							int						=			AddedToken(__A,		lstrip=__A,		rstrip=__A						) if isinstance(__A,		__A						) else mask_token
     UpperCAmelCase	:							str						=			legacy_behaviour
     super().__init__(
         vocab_file=__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,		src_lang=__A,		tgt_lang=__A,		additional_special_tokens=__A,		legacy_behaviour=__A,		**__A,		)

     UpperCAmelCase	:							Optional[int]						=			vocab_file
     UpperCAmelCase	:							Optional[Any]						=			False if not self.vocab_file else True

     UpperCAmelCase	:							Optional[int]						=			FAIRSEQ_LANGUAGE_CODES.copy()

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

     self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}						)
     UpperCAmelCase	:							List[Any]						=			{
         lang_code: self.convert_tokens_to_ids(__A						) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     UpperCAmelCase	:							List[Any]						=			src_lang if src_lang is not None else '''eng_Latn'''
     UpperCAmelCase	:							List[Any]						=			self.convert_tokens_to_ids(self._src_lang						)
     UpperCAmelCase	:							List[str]						=			tgt_lang
     self.set_src_lang_special_tokens(self._src_lang						)



   @property
   def 	__magic_name__   (							self							:					Optional[int]						):
     return self._src_lang



   @src_lang.setter
   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					str						):
     UpperCAmelCase	:							Tuple						=			new_src_lang
     self.set_src_lang_special_tokens(self._src_lang						)



   def 	__magic_name__   (							self							:					Any,		__A							:					List[int],		__A							:					Optional[List[int]] = None						):
     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 	__magic_name__   (							self							:					Tuple,		__A							:					List[int],		__A							:					Optional[List[int]] = None						):
     UpperCAmelCase	:							Union[str, Any]						=			[self.sep_token_id]
     UpperCAmelCase	:							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 	__magic_name__   (							self							:					str,		__A							:					Optional[int],		__A							:					str,		__A							:					Optional[str],		__A							:					Optional[str],		**__A							:					Union[str, Any]						):
     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	:							Optional[Any]						=			src_lang
     UpperCAmelCase	:							Optional[Any]						=			self(__A,		add_special_tokens=__A,		return_tensors=__A,		**__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.convert_tokens_to_ids(__A						)
     UpperCAmelCase	:							int						=			tgt_lang_id
     return inputs



   def 	__magic_name__   (							self							:					List[str],		__A							:					List[str],		__A							:					str = "eng_Latn",		__A							:					Optional[List[str]] = None,		__A							:					str = "fra_Latn",		**__A							:					Tuple,		):
     UpperCAmelCase	:							Any						=			src_lang
     UpperCAmelCase	:							Tuple						=			tgt_lang
     return super().prepare_seqaseq_batch(__A,		__A,		**__A						)



   def 	__magic_name__   (							self							:					List[str]						):
     return self.set_src_lang_special_tokens(self.src_lang						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     return self.set_tgt_lang_special_tokens(self.tgt_lang						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					List[str]						):
     UpperCAmelCase	:							int						=			self.convert_tokens_to_ids(__A						)

     if self.legacy_behaviour:
       UpperCAmelCase	:							Tuple						=			[]
       UpperCAmelCase	:							Optional[int]						=			[self.eos_token_id, self.cur_lang_code]
     else:
       UpperCAmelCase	:							Any						=			[self.cur_lang_code]
       UpperCAmelCase	:							Optional[Any]						=			[self.eos_token_id]

     UpperCAmelCase	:							Dict						=			self.convert_ids_to_tokens(self.prefix_tokens						)
     UpperCAmelCase	:							Union[str, Any]						=			self.convert_ids_to_tokens(self.suffix_tokens						)

     UpperCAmelCase	:							int						=			processors.TemplateProcessing(
         single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,		pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,		special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,		self.prefix_tokens + self.suffix_tokens						)						),		)



   def 	__magic_name__   (							self							:					Dict,		__A							:					str						):
     UpperCAmelCase	:							Optional[int]						=			self.convert_tokens_to_ids(__A						)
     if self.legacy_behaviour:
       UpperCAmelCase	:							List[Any]						=			[]
       UpperCAmelCase	:							Any						=			[self.eos_token_id, self.cur_lang_code]
     else:
       UpperCAmelCase	:							int						=			[self.cur_lang_code]
       UpperCAmelCase	:							List[Any]						=			[self.eos_token_id]

     UpperCAmelCase	:							str						=			self.convert_ids_to_tokens(self.prefix_tokens						)
     UpperCAmelCase	:							List[Any]						=			self.convert_ids_to_tokens(self.suffix_tokens						)

     UpperCAmelCase	:							List[str]						=			processors.TemplateProcessing(
         single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,		pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,		special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,		self.prefix_tokens + self.suffix_tokens						)						),		)





   def 	__magic_name__   (							self							:					Dict,		__A							:					str,		__A							:					Optional[str] = None						):
     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	:							Tuple						=			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,)





