
"""simple docstring"""






import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__SCREAMING_SNAKE_CASE							=     pytest.mark.integration


__SCREAMING_SNAKE_CASE							=     {'comet'}
__SCREAMING_SNAKE_CASE							=     importlib.util.find_spec('fairseq') is not None

__SCREAMING_SNAKE_CASE							=     {'code_eval'}
__SCREAMING_SNAKE_CASE							=     os.name == 'nt'

__SCREAMING_SNAKE_CASE							=     {'bertscore', 'frugalscore', 'perplexity'}
__SCREAMING_SNAKE_CASE							=     importlib.util.find_spec('transformers') is not None




def       A_  (				__lowercase	):
 @wraps(__lowercase	)
 def wrapper(self						,	__lowercase	):
  if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
   self.skipTest('"test requires Fairseq"'	)
  else:
   test_case(self						,	__lowercase	)

 return wrapper




def       A_  (				__lowercase	):
 @wraps(__lowercase	)
 def wrapper(self						,	__lowercase	):
  if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
   self.skipTest('"test requires transformers"'	)
  else:
   test_case(self						,	__lowercase	)

 return wrapper




def       A_  (				__lowercase	):
 @wraps(__lowercase	)
 def wrapper(self						,	__lowercase	):
  if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
   self.skipTest('"test not supported on Windows"'	)
  else:
   test_case(self						,	__lowercase	)

 return wrapper




def       A_  (				):
 UpperCamelCase_     :					Union[str, Any]   	=[metric_dir.split(os.sep	)[-2] for metric_dir in glob.glob('./metrics/*/'	)]
 return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()       )
@for_all_test_methods(
    A__   ,							A__   ,							A__       )
@local
class 				a__					(						parameterized.TestCase       ):
     UpperCAmelCase__         =      {}
     UpperCAmelCase__         =      None



     @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'   )
     @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'   )
     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	='[...]'
      UpperCamelCase_     :					Dict   	=importlib.import_module(
          datasets.load.metric_module_factory(os.path.join('metrics'      ,  _lowerCamelCase   )   ).module_path   )
      UpperCamelCase_     :					List[str]   	=datasets.load.import_main_class(metric_module.__name__      ,  dataset=_lowerCamelCase   )
      # check parameters
      UpperCamelCase_     :					Union[str, Any]   	=inspect.signature(metric._compute   ).parameters
      self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()   )   )  # no **kwargs
      # run doctest
      with self.patch_intensive_calls(_lowerCamelCase      ,  metric_module.__name__   ):
       with self.use_local_metrics():
        try:
         UpperCamelCase_     :					Optional[int]   	=doctest.testmod(_lowerCamelCase      ,  verbose=_lowerCamelCase      ,  raise_on_error=_lowerCamelCase   )
        except doctest.UnexpectedException as e:
         raise e.exc_info[1]  # raise the exception that doctest caught
      self.assertEqual(results.failed      ,  0   )
      self.assertGreater(results.attempted      ,  1   )



     @slow
     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	='[...]'
      UpperCamelCase_     :					Dict   	=importlib.import_module(
          datasets.load.metric_module_factory(os.path.join('metrics'      ,  _lowerCamelCase   )   ).module_path   )
      # run doctest
      with self.use_local_metrics():
       UpperCamelCase_     :					int   	=doctest.testmod(_lowerCamelCase      ,  verbose=_lowerCamelCase      ,  raise_on_error=_lowerCamelCase   )
      self.assertEqual(results.failed      ,  0   )
      self.assertGreater(results.attempted      ,  1   )



     @contextmanager
     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      if metric_name in self.INTENSIVE_CALLS_PATCHER:
       with self.INTENSIVE_CALLS_PATCHER[metric_name](_lowerCamelCase   ):
        yield
      else:
       yield



     @contextmanager
     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      def load_local_metric(_lowerCamelCase :List[Any]      ,  *_lowerCamelCase :List[Any]      ,  **_lowerCamelCase :str   ):
       return load_metric(os.path.join('metrics'      ,  _lowerCamelCase   )      ,  *_lowerCamelCase      ,  **_lowerCamelCase   )

      with patch('datasets.load_metric'   ) as mock_load_metric:
       UpperCamelCase_     :					str   	=load_local_metric
       yield




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      def wrapper(_lowerCamelCase :Optional[int]   ):
       UpperCamelCase_     :					str   	=contextmanager(_lowerCamelCase   )
       UpperCamelCase_     :					Union[str, Any]   	=patcher
       return patcher

      return wrapper




@LocalMetricTest.register_intensive_calls_patcher('bleurt'	)
def       A_  (				__lowercase	):
 import tensorflow.compat.va as tf
 from bleurt.score import Predictor

 tf.flags.DEFINE_string('sv'						,	''						,	''	)  # handle pytest cli flags





 class 				a__					(						A__       ):



      def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :int   ):







       '''simple docstring'''



       assert len(input_dict['input_ids']   ) == 2
       return np.array([1.03, 1.04]   )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
 with patch('bleurt.score._create_predictor'	) as mock_create_predictor:
  UpperCamelCase_     :					int   	=MockedPredictor()
  yield




@LocalMetricTest.register_intensive_calls_patcher('bertscore'	)
def       A_  (				__lowercase	):
 import torch

 def bert_cos_score_idf(__lowercase						,	__lowercase						,	*__lowercase						,	**__lowercase	):
  return torch.tensor([[1.0, 1.0, 1.0]] * len(__lowercase	)	)

 # mock get_model which is supposed to do download a bert model
 # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
 with patch('bert_score.scorer.get_model'	), patch(
     'bert_score.scorer.bert_cos_score_idf'	) as mock_bert_cos_score_idf:
  UpperCamelCase_     :					int   	=bert_cos_score_idf
  yield




@LocalMetricTest.register_intensive_calls_patcher('comet'	)
def       A_  (				__lowercase	):
 def load_from_checkpoint(__lowercase	):





  class 				a__					:



       def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :Optional[Any]      ,  *_lowerCamelCase :Optional[int]      ,  **_lowerCamelCase :Tuple   ):







        '''simple docstring'''



        assert len(_lowerCamelCase   ) == 2
        UpperCamelCase_     :					Optional[int]   	=[0.19, 0.92]
        return scores, sum(_lowerCamelCase   ) / len(_lowerCamelCase   )


  return Model()

 # mock load_from_checkpoint which is supposed to do download a bert model
 # mock load_from_checkpoint which is supposed to do download a bert model
 with patch('comet.download_model'	) as mock_download_model:
  UpperCamelCase_     :					Union[str, Any]   	=None
  with patch('comet.load_from_checkpoint'	) as mock_load_from_checkpoint:
   UpperCamelCase_     :					Any   	=load_from_checkpoint
   yield





def       A_  (				):
 UpperCamelCase_     :					Any   	=load_metric(os.path.join('metrics'						,	'seqeval'	)	)
 UpperCamelCase_     :					Any   	='ERROR'
 UpperCamelCase_     :					int   	=F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
 with pytest.raises(__lowercase						,	match=re.escape(__lowercase	)	):
  metric.compute(predictions=[]						,	references=[]						,	scheme=__lowercase	)


"""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, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE							=     {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__SCREAMING_SNAKE_CASE							=     {
    'camembert-base': 512,
}

__SCREAMING_SNAKE_CASE							=     '▁'





class 				a__					(						A__       ):
     UpperCAmelCase__         =      VOCAB_FILES_NAMES
     UpperCAmelCase__         =      PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__         =      ['''input_ids''', '''attention_mask''']



     def __init__(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :List[Any]="<s>"      ,  _lowerCamelCase :List[Any]="</s>"      ,  _lowerCamelCase :Optional[int]="</s>"      ,  _lowerCamelCase :List[Any]="<s>"      ,  _lowerCamelCase :List[Any]="<unk>"      ,  _lowerCamelCase :Union[str, Any]="<pad>"      ,  _lowerCamelCase :int="<mask>"      ,  _lowerCamelCase :Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,  _lowerCamelCase :Optional[Dict[str, Any]] = None      ,  **_lowerCamelCase :Optional[int]      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      UpperCamelCase_     :					List[str]   	={} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  unk_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  additional_special_tokens=_lowerCamelCase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCamelCase      ,  )
      UpperCamelCase_     :					Optional[int]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(_lowerCamelCase   )   )
      UpperCamelCase_     :					str   	=vocab_file
      # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
      # sentencepiece vocabulary (this is the case for <s> and </s>
      UpperCamelCase_     :					Tuple   	={'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
      UpperCamelCase_     :					List[str]   	=len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					List[str]   	=len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					Any   	={v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase_     :					Any   	=[self.cls_token_id]
      UpperCamelCase_     :					str   	=[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



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

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



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=[self.sep_token_id]
      UpperCamelCase_     :					Dict   	=[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]



     @property
     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	={self.convert_ids_to_tokens(_lowerCamelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      elif self.sp_model.PieceToId(_lowerCamelCase   ) == 0:
       # Convert sentence piece unk token to fairseq unk token index
       return self.unk_token_id
      return self.fairseq_offset + self.sp_model.PieceToId(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[Any]   ):







      '''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 :int      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=[]
      UpperCamelCase_     :					Optional[Any]   	=''
      UpperCamelCase_     :					Optional[Any]   	=False
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        if not prev_is_special:
         out_string += " "
        out_string += self.sp_model.decode(_lowerCamelCase   ) + token
        UpperCamelCase_     :					Union[str, Any]   	=True
        UpperCamelCase_     :					Optional[Any]   	=[]
       else:
        current_sub_tokens.append(_lowerCamelCase   )
        UpperCamelCase_     :					Any   	=False
      out_string += self.sp_model.decode(_lowerCamelCase   )
      return out_string.strip()



     def __getstate__(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self.__dict__.copy()
      UpperCamelCase_     :					str   	=None
      return state



     def __setstate__(							self :Optional[Any]      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=d

      # for backward compatibility
      if not hasattr(self      ,  'sp_model_kwargs'   ):
       UpperCamelCase_     :					Any   	={}

      UpperCamelCase_     :					List[Any]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      if not os.path.isdir(_lowerCamelCase   ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
       return
      UpperCamelCase_     :					Any   	=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:
        UpperCamelCase_     :					int   	=self.sp_model.serialized_model_proto()
        fi.write(_lowerCamelCase   )

      return (out_vocab_file,)
