'''simple docstring'''






import pytest

from datasets import inspect_metric, list_metrics, load_metric







@pytest.fixture
def 		A_       (	_lowerCAmelCase   :    str							):



      """simple docstring"""




      monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"				,	set()							)







@pytest.fixture
def 		A_       (	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      class  UpperCAmelCase__						:



          def __init__(	self      :       Union[str, Any],__A      :       Dict     ):
                _lowerCamelCase				:   List[str]								=   metric_id


      class  UpperCAmelCase__						:
          lowerCAmelCase_				    =		[MetricMock(UpperCamelCase_ ) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]



          def   lowerCamelCase_  (	self      :       str     ):
                return self._metrics


      monkeypatch.setattr("datasets.inspect.huggingface_hub"				,	HfhMock()							)







@pytest.mark.parametrize(
    "func, args"				,	[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]							)
def 		A_       (	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    Dict							):



      """simple docstring"""




      if "tmp_path" in args:
            _lowerCamelCase				:   Dict								=   tuple(arg if arg != "tmp_path" else tmp_path for arg in args							)
      with pytest.warns(_lowerCAmelCase				,	match="https://huggingface.co/docs/evaluate"							):
            func(*_lowerCAmelCase							)

'''simple docstring'''






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

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


if is_sentencepiece_available():
 from .tokenization_albert import AlbertTokenizer
else:
 UpperCAmelCase_       :   Any   =       None

UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)
UpperCAmelCase_       :   Optional[Any]   =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_       :   List[Any]   =       {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_       :   str   =       {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

UpperCAmelCase_       :   str   =       '▁'

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		AlbertTokenizer



    def __init__(	self      :       Dict,__A      :       str=None,__A      :       Union[str, Any]=None,__A      :       Union[str, Any]=True,__A      :       int=True,__A      :       List[Any]=False,__A      :       Any="[CLS]",__A      :       List[str]="[SEP]",__A      :       Tuple="<unk>",__A      :       Dict="[SEP]",__A      :       Dict="<pad>",__A      :       List[str]="[CLS]",__A      :       Any="[MASK]",**__A      :       Tuple,):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          _lowerCamelCase				:   Union[str, Any]								=   (
              AddedToken(__A,lstrip=__A,rstrip=__A,normalized=__A     )
              if isinstance(__A,__A     )
              else mask_token
          )

          super().__init__(
              __A,tokenizer_file=__A,do_lower_case=__A,remove_space=__A,keep_accents=__A,bos_token=__A,eos_token=__A,unk_token=__A,sep_token=__A,pad_token=__A,cls_token=__A,mask_token=__A,**__A,)

          _lowerCamelCase				:   Dict								=   do_lower_case
          _lowerCamelCase				:   List[str]								=   remove_space
          _lowerCamelCase				:   Any								=   keep_accents
          _lowerCamelCase				:   Tuple								=   vocab_file
          _lowerCamelCase				:   Optional[int]								=   False if not self.vocab_file else True



    def   lowerCamelCase_  (	self      :       List[str],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   List[Any]								=   [self.sep_token_id]
          _lowerCamelCase				:   Any								=   [self.cls_token_id]
          if token_ids_a is None:
                return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep



    def   lowerCamelCase_  (	self      :       List[str],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   Optional[Any]								=   [self.sep_token_id]
          _lowerCamelCase				:   Union[str, 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     ) * [0] + len(token_ids_a + sep     ) * [1]



    def   lowerCamelCase_  (	self      :       str,__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
          _lowerCamelCase				:   Union[str, Any]								=   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,)