


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=True      ,      SCREAMING_SNAKE_CASE="pt"      )->						Union[str, Any]:




      """simple docstring"""

      snake_case_ =   {'''add_prefix_space''': True} if isinstance(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ) and not line.startswith(''' '''      ) else {}
      snake_case_ =   padding_side
      return tokenizer(
          [line]      ,      max_length=SCREAMING_SNAKE_CASE      ,      padding='''max_length''' if pad_to_max_length else None      ,      truncation=SCREAMING_SNAKE_CASE      ,      return_tensors=SCREAMING_SNAKE_CASE      ,      add_special_tokens=SCREAMING_SNAKE_CASE      ,      **SCREAMING_SNAKE_CASE      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      )->						str:




      """simple docstring"""

      snake_case_ =   input_ids.ne(SCREAMING_SNAKE_CASE      ).any(dim=0      )
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase="train"	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=""	,   ):
       super().__init__()
       snake_case_ =   Path(_UpperCAmelCase      ).joinpath(type_path + '''.source'''      )
       snake_case_ =   Path(_UpperCAmelCase      ).joinpath(type_path + '''.target'''      )
       snake_case_ =   self.get_char_lens(self.src_file      )
       snake_case_ =   max_source_length
       snake_case_ =   max_target_length
       assert min(self.src_lens      ) > 0, F'''found empty line in {self.src_file}'''
       snake_case_ =   tokenizer
       snake_case_ =   prefix
       if n_obs is not None:
             snake_case_ =   self.src_lens[:n_obs]
       snake_case_ =   src_lang
       snake_case_ =   tgt_lang



 def __len__(			self      ):
       return len(self.src_lens      )



 def __getitem__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   index + 1  # linecache starts at 1
       snake_case_ =   self.prefix + linecache.getline(str(self.src_file      )	,   _UpperCAmelCase      ).rstrip('''\n'''      )
       snake_case_ =   linecache.getline(str(self.tgt_file      )	,   _UpperCAmelCase      ).rstrip('''\n'''      )
       assert source_line, F'''empty source line for index {index}'''
       assert tgt_line, F'''empty tgt line for index {index}'''

       # Need to add eos token manually for T5
       if isinstance(self.tokenizer	,   _UpperCAmelCase      ):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       snake_case_ =   (
           self.tokenizer.question_encoder if isinstance(self.tokenizer	,   _UpperCAmelCase      ) else self.tokenizer
       )
       snake_case_ =   self.tokenizer.generator if isinstance(self.tokenizer	,   _UpperCAmelCase      ) else self.tokenizer

       snake_case_ =   encode_line(_UpperCAmelCase	,   _UpperCAmelCase	,   self.max_source_length	,   '''right'''      )
       snake_case_ =   encode_line(_UpperCAmelCase	,   _UpperCAmelCase	,   self.max_target_length	,   '''right'''      )

       snake_case_ =   source_inputs['''input_ids'''].squeeze()
       snake_case_ =   target_inputs['''input_ids'''].squeeze()
       snake_case_ =   source_inputs['''attention_mask'''].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }



 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase      ):
       return [len(_UpperCAmelCase      ) for x in Path(_UpperCAmelCase      ).open().readlines()]





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   torch.stack([x['''input_ids'''] for x in batch]      )
       snake_case_ =   torch.stack([x['''attention_mask'''] for x in batch]      )
       snake_case_ =   torch.stack([x['''decoder_input_ids'''] for x in batch]      )
       snake_case_ =   (
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer	,   _UpperCAmelCase      )
           else self.tokenizer.pad_token_id
       )
       snake_case_ =   (
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer	,   _UpperCAmelCase      )
           else self.tokenizer.pad_token_id
       )
       snake_case_ =   trim_batch(_UpperCAmelCase	,   _UpperCAmelCase      )
       snake_case_ ,					snake_case_ =   trim_batch(_UpperCAmelCase	,   _UpperCAmelCase	,   attention_mask=_UpperCAmelCase      )
       snake_case_ =   {
           '''input_ids''': source_ids,
           '''attention_mask''': source_mask,
           '''decoder_input_ids''': y,
       }
       return batch





UpperCAmelCase    =					getLogger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE      )      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						None:




      """simple docstring"""

      snake_case_ =   get_git_info()
      save_json(SCREAMING_SNAKE_CASE      ,      os.path.join(SCREAMING_SNAKE_CASE      ,      '''git_log.json'''      )      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=4      ,      **SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE      ,      '''w'''      ) as f:
            json.dump(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      indent=SCREAMING_SNAKE_CASE      ,      **SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Union[str, Any]:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE      ) as f:
            return json.load(SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					()->						List[str]:




      """simple docstring"""

      snake_case_ =   git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE      )
      snake_case_ =   {
          '''repo_id''': str(SCREAMING_SNAKE_CASE      ),
          '''repo_sha''': str(repo.head.object.hexsha      ),
          '''repo_branch''': str(repo.active_branch      ),
          '''hostname''': str(socket.gethostname()      ),
      }
      return repo_infos






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						List:




      """simple docstring"""

      return list(map(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE      ,      '''wb'''      ) as f:
            return pickle.dump(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      def remove_articles(SCREAMING_SNAKE_CASE      ):
            return re.sub(R'''\b(a|an|the)\b'''      ,      ''' '''      ,      SCREAMING_SNAKE_CASE      )

      def white_space_fix(SCREAMING_SNAKE_CASE      ):
            return " ".join(text.split()      )

      def remove_punc(SCREAMING_SNAKE_CASE      ):
            snake_case_ =   set(string.punctuation      )
            return "".join(ch for ch in text if ch not in exclude      )

      def lower(SCREAMING_SNAKE_CASE      ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE      )      )      )      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

      snake_case_ =   normalize_answer(SCREAMING_SNAKE_CASE      ).split()
      snake_case_ =   normalize_answer(SCREAMING_SNAKE_CASE      ).split()
      snake_case_ =   Counter(SCREAMING_SNAKE_CASE      ) & Counter(SCREAMING_SNAKE_CASE      )
      snake_case_ =   sum(common.values()      )
      if num_same == 0:
            return 0
      snake_case_ =   1.0 * num_same / len(SCREAMING_SNAKE_CASE      )
      snake_case_ =   1.0 * num_same / len(SCREAMING_SNAKE_CASE      )
      snake_case_ =   (2 * precision * recall) / (precision + recall)
      return fa






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      return normalize_answer(SCREAMING_SNAKE_CASE      ) == normalize_answer(SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      assert len(SCREAMING_SNAKE_CASE      ) == len(SCREAMING_SNAKE_CASE      )
      snake_case_ =   0
      for hypo, pred in zip(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ):
            em += exact_match_score(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
      if len(SCREAMING_SNAKE_CASE      ) > 0:
            em /= len(SCREAMING_SNAKE_CASE      )
      return {"em": em}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      return model_prefix.startswith('''rag'''      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   {p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      snake_case_ =   '''dropout_rate'''
      for p in extra_params:
            if getattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ):
                  if not hasattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ) and not hasattr(SCREAMING_SNAKE_CASE      ,      equivalent_param[p]      ):
                        logger.info('''config doesn\'t have a `{}` attribute'''.format(SCREAMING_SNAKE_CASE      )      )
                        delattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
                        continue
                  snake_case_ =   p if hasattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ) else equivalent_param[p]
                  setattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      getattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )      )
                  delattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
      return hparams, config



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}