


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


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   """▁"""

UpperCAmelCase      =   {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase      =   {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase      =   {
    """facebook/xglm-564M""": 2048,
}







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  VOCAB_FILES_NAMES
 __snake_case												=  PRETRAINED_VOCAB_FILES_MAP
 __snake_case												=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case												=  ["input_ids", "attention_mask"]



 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase="<s>"		,				_UpperCAmelCase="</s>"		,				_UpperCAmelCase="</s>"		,				_UpperCAmelCase="<s>"		,				_UpperCAmelCase="<unk>"		,				_UpperCAmelCase="<pad>"		,				_UpperCAmelCase = None		,				**_UpperCAmelCase		,				):
       snake_case_	=				{} if sp_model_kwargs is None else sp_model_kwargs

       # Compatibility with the original tokenizer
       snake_case_	=				7
       snake_case_	=				[F'''<madeupword{i}>''' for i in range(self.num_madeup_words )]

       snake_case_	=				kwargs.get('''additional_special_tokens'''		,				[] )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]

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

       snake_case_	=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(str(_UpperCAmelCase ) )
       snake_case_	=				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'

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

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

       snake_case_	=				len(self.sp_model )
       snake_case_	=				{F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
       self.fairseq_tokens_to_ids.update(_UpperCAmelCase )

       snake_case_	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



 def __getstate__(     self ):
       snake_case_	=				self.__dict__.copy()
       snake_case_	=				None
       snake_case_	=				self.sp_model.serialized_model_proto()
       return state



 def __setstate__(     self		,				_UpperCAmelCase ):
       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.LoadFromSerializedProto(self.sp_model_proto )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):

       if token_ids_a is None:
             return [self.sep_token_id] + token_ids_a
       snake_case_	=				[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None		,				_UpperCAmelCase = False ):

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

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       snake_case_	=				[self.sep_token_id]

       if token_ids_a is None:
             return len(sep + token_ids_a ) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]



 @property
 def 						UpperCamelCase__					(     self ):
       return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				{self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return self.sp_model.encode(_UpperCAmelCase		,				out_type=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				''''''.join(_UpperCAmelCase ).replace(_UpperCAmelCase		,				''' ''' ).strip()
       return out_string





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       if not os.path.isdir(_UpperCAmelCase ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
             return
       snake_case_	=				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:
                   snake_case_	=				self.sp_model.serialized_model_proto()
                   fi.write(_UpperCAmelCase )

       return (out_vocab_file,)



import numpy as np

import datasets


UpperCAmelCase      =   """
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""

UpperCAmelCase      =   """\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
"""

UpperCAmelCase      =   """
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {'mahalanobis': array([0.5])}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,						_KWARGS_DESCRIPTION	)
class lowerCAmelCase_						(			datasets.Metric	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       return datasets.MetricInfo(
           description=_DESCRIPTION		,				citation=_CITATION		,				inputs_description=_KWARGS_DESCRIPTION		,				features=datasets.Features(
               {
                   '''X''': datasets.Sequence(datasets.Value('''float'''		,				id='''sequence''' )		,				id='''X''' ),
               } )		,				)





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       # convert to numpy arrays
       snake_case_	=				np.array(_UpperCAmelCase )
       snake_case_	=				np.array(_UpperCAmelCase )

       # Assert that arrays are 2D
       if len(X.shape ) != 2:
             raise ValueError('''Expected `X` to be a 2D vector''' )
       if len(reference_distribution.shape ) != 2:
             raise ValueError('''Expected `reference_distribution` to be a 2D vector''' )
       if reference_distribution.shape[0] < 2:
             raise ValueError(
                 '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''' )

       # Get mahalanobis distance for each prediction
       snake_case_	=				X - np.mean(_UpperCAmelCase )
       snake_case_	=				np.cov(reference_distribution.T )
       try:
             snake_case_	=				np.linalg.inv(_UpperCAmelCase )
       except np.linalg.LinAlgError:
             snake_case_	=				np.linalg.pinv(_UpperCAmelCase )
       snake_case_	=				np.dot(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				np.dot(_UpperCAmelCase		,				X_minus_mu.T ).diagonal()

       return {"mahalanobis": mahal_dist}