
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase     							=       '▁'

__lowerCAmelCase     							=       {'vocab_file': 'spiece.model'}

__lowerCAmelCase     							=       {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

__lowerCAmelCase     							=       {
    'google/pegasus-xsum': 512,
}


__lowerCAmelCase     							=       logging.get_logger(__name__)


class 			_lowerCAmelCase    (    A__      ):







							'''simple docstring'''

							lowerCAmelCase_											=						VOCAB_FILES_NAMES

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

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase="<pad>"  ,  UpperCAmelCase="</s>"  ,  UpperCAmelCase="<unk>"  ,  UpperCAmelCase="<mask_2>"  ,  UpperCAmelCase="<mask_1>"  ,  UpperCAmelCase=None  ,  UpperCAmelCase=103  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )     ->   None:
														_snake_case   =  offset
														if additional_special_tokens is not None:
																					if not isinstance(lowerCAmelCase__  ,  lowerCAmelCase__  ):
																												raise TypeError(
																												    f"""additional_special_tokens should be of type {type(lowerCAmelCase__  )}, but is"""
																												    f""" {type(lowerCAmelCase__  )}"""  )

																					_snake_case   =  (
																					    ([mask_token_sent] + additional_special_tokens)
																					    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																					    else additional_special_tokens
																					)
																					# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																					additional_special_tokens_extended += [
																					    f"""<unk_{i}>""" for i in range(len(lowerCAmelCase__  )  ,  self.offset - 1  )
																					]

																					if len(set(lowerCAmelCase__  )  ) != len(lowerCAmelCase__  ):
																												raise ValueError(
																												    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																												    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""  )
																					_snake_case   =  additional_special_tokens_extended
														else:
																					_snake_case   =  [mask_token_sent] if mask_token_sent is not None else []
																					additional_special_tokens += [f"""<unk_{i}>""" for i in range(2  ,  self.offset  )]

														_snake_case   =  {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    eos_token=lowerCAmelCase__  ,  unk_token=lowerCAmelCase__  ,  mask_token=lowerCAmelCase__  ,  pad_token=lowerCAmelCase__  ,  mask_token_sent=lowerCAmelCase__  ,  offset=lowerCAmelCase__  ,  additional_special_tokens=lowerCAmelCase__  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **lowerCAmelCase__  ,  )
														_snake_case   =  mask_token_sent
														_snake_case   =  vocab_file
														_snake_case   =  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
														self.sp_model.Load(lowerCAmelCase__  )

														# add special tokens to encoder dict
														_snake_case   =  {
														    0: self.pad_token,
														    1: self.eos_token,
														}

														if self.mask_token_sent is not None:
																					self.encoder.update(
																					    {
																					        2: self.mask_token_sent,
																					        3: self.mask_token,
																					    }  )

														if self.offset > 0:
																					# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																					# mask_token_sent is already added to list -> so start at 1
																					self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1  ,  self.offset - 1  )}  )

														_snake_case   =  {v: k for k, v in self.encoder.items()}

							@property
							def        lowercase							(self  )     ->   int:
														return len(self.sp_model  ) + self.offset

							def        lowercase							(self  )     ->   Dict[str, int]:
														_snake_case   =  {self.convert_ids_to_tokens(lowerCAmelCase__  ): i for i in range(self.vocab_size  )}
														vocab.update(self.added_tokens_encoder  )
														return vocab

							def __getstate__(self  )     ->   Dict:
														_snake_case   =  self.__dict__.copy()
														_snake_case   =  None
														return state

							def __setstate__(self  ,  UpperCAmelCase  )     ->   Tuple:
														_snake_case   =  d

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

														_snake_case   =  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
														self.sp_model.Load(self.vocab_file  )

							def        lowercase							(self  ,  UpperCAmelCase  )     ->   List[str]:
														return self.sp_model.encode(lowerCAmelCase__  ,  out_type=lowerCAmelCase__  )

							def        lowercase							(self  ,  UpperCAmelCase  )     ->   int:
														if token in self.decoder:
																					return self.decoder[token]
														elif token in self.added_tokens_decoder:
																					return self.added_tokens_decoder[token]
														_snake_case   =  self.sp_model.piece_to_id(lowerCAmelCase__  )
														return sp_id + self.offset

							def        lowercase							(self  ,  UpperCAmelCase  )     ->   str:
														if index in self.encoder:
																					return self.encoder[index]
														elif index in self.added_tokens_encoder:
																					return self.added_tokens_encoder[index]
														else:
																					_snake_case   =  self.sp_model.IdToPiece(index - self.offset  )
														return token

							def        lowercase							(self  ,  UpperCAmelCase  )     ->   List[Any]:
														_snake_case   =  []
														_snake_case   =  ''''''
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												out_string += self.sp_model.decode(lowerCAmelCase__  ) + token
																												_snake_case   =  []
																					else:
																												current_sub_tokens.append(lowerCAmelCase__  )
														out_string += self.sp_model.decode(lowerCAmelCase__  )
														return out_string.strip()

							def        lowercase							(self  ,  UpperCAmelCase=False  )     ->   Dict:
														return 1

							def        lowercase							(self  ,  UpperCAmelCase  )     ->   str:
														_snake_case   =  set(self.all_special_ids  )  # call it once instead of inside list comp
														all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

														return [1 if x in all_special_ids else 0 for x in seq]

							def        lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = False  )     ->   List[int]:
														if already_has_special_tokens:
																					return self._special_token_mask(lowerCAmelCase__  )
														elif token_ids_a is None:
																					return self._special_token_mask(lowerCAmelCase__  ) + [1]
														else:
																					return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]

							def        lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=None  )     ->   List[int]:
														if token_ids_a is None:
																					return token_ids_a + [self.eos_token_id]
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return token_ids_a + token_ids_a + [self.eos_token_id]





							def        lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )     ->   Tuple[str]:
														if not os.path.isdir(lowerCAmelCase__  ):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
																					return
														_snake_case   =  os.path.join(
														    lowerCAmelCase__  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

														return (out_vocab_file,)



import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


__lowerCAmelCase	: Optional[int]  				='\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n'

__lowerCAmelCase	: Optional[Any]  				='\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n'

__lowerCAmelCase	: Dict  				='\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "precision": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\')  # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'precision@10\': 1.0}\n\n'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return float((preds == labels).mean()      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		simple_accuracy(lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		float(fa_score(y_true=lowercase__    ,       y_pred=lowercase__      )      )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		en_sentvecs.shape[0]

						# mean centering
						__SCREAMING_SNAKE_CASE						:     Tuple					=		en_sentvecs - np.mean(lowercase__    ,       axis=0      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		in_sentvecs - np.mean(lowercase__    ,       axis=0      )

						__SCREAMING_SNAKE_CASE						:     str					=		cdist(lowercase__    ,       lowercase__    ,       '''cosine'''      )
						__SCREAMING_SNAKE_CASE						:     int					=		np.array(range(lowercase__      )      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		sim.argsort(axis=1      )[:, :10]
						__SCREAMING_SNAKE_CASE						:     str					=		np.any(preds == actual[:, None]    ,       axis=1      )
						return float(matches.mean()      )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION       )
class        _lowercase      (			datasets.Metric       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Tuple	)		->			Tuple:
										if self.config_name not in [
										    "wnli",
										    "copa",
										    "sna",
										    "csqa",
										    "wstp",
										    "inltkh",
										    "bbca",
										    "cvit-mkb-clsr",
										    "iitp-mr",
										    "iitp-pr",
										    "actsa-sc",
										    "md",
										    "wiki-ner",
										]:
																raise KeyError(
																    '''You should supply a configuration name selected in '''
																    '''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
																    '''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
																    '''"wiki-ner"]'''	)
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
										        {
										            '''predictions''': datasets.Value('''int64'''	)
										            if self.config_name != '''cvit-mkb-clsr'''
										            else datasets.Sequence(datasets.Value('''float32'''	)	),
										            '''references''': datasets.Value('''int64'''	)
										            if self.config_name != '''cvit-mkb-clsr'''
										            else datasets.Sequence(datasets.Value('''float32'''	)	),
										        }	)			,			codebase_urls=[]			,			reference_urls=[]			,			format='''numpy''' if self.config_name != '''cvit-mkb-clsr''' else None			,			)



				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Tuple	)		->			str:
										if self.config_name == "cvit-mkb-clsr":
																return {"precision@10": precision_at_aa(lowerCAmelCase__			,			lowerCAmelCase__	)}
										elif self.config_name in ["wiki-ner"]:
																return acc_and_fa(lowerCAmelCase__			,			lowerCAmelCase__	)
										elif self.config_name in [
										    "wnli",
										    "copa",
										    "sna",
										    "csqa",
										    "wstp",
										    "inltkh",
										    "bbca",
										    "iitp-mr",
										    "iitp-pr",
										    "actsa-sc",
										    "md",
										]:
																return {"accuracy": simple_accuracy(lowerCAmelCase__			,			lowerCAmelCase__	)}
										else:
																raise KeyError(
																    '''You should supply a configuration name selected in '''
																    '''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
																    '''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
																    '''"wiki-ner"]'''	)
