

import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    "files"							,[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]							,)
def 	__snake_case		(      __UpperCamelCase     :      Any							,__UpperCamelCase     :      int  ):



      """simple docstring"""

      A_		   =							tmp_path_factory.mktemp("dset_infos_dir"  )
      if "full:README.md" in files:
            with open(dataset_infos_dir / "README.md"							,"w"  ) as f:
                  f.write("---\ndataset_info:\n  dataset_size: 42\n---"  )
      if "empty:README.md" in files:
            with open(dataset_infos_dir / "README.md"							,"w"  ) as f:
                  f.write(""  )
    # we want to support dataset_infos.json for backward compatibility
      if "dataset_infos.json" in files:
            with open(dataset_infos_dir / "dataset_infos.json"							,"w"  ) as f:
                  f.write("{\"default\": {\"dataset_size\": 42}}"  )
      A_		   =							DatasetInfosDict.from_directory(__lowercase  )
      assert dataset_infos
      assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    "dataset_info"							,[
        DatasetInfo(),
        DatasetInfo(
            description="foo"							,features=Features({"a": Value("int32"  )}  )							,builder_name="builder"							,config_name="config"							,version="1.0.0"							,splits=[{"name": "train"}]							,download_size=42							,),
    ]							,)
def 	__snake_case		(      __UpperCamelCase     :      Tuple							,__UpperCamelCase     :      DatasetInfo  ):



      """simple docstring"""

      A_		   =							str(__lowercase  )
      dataset_info.write_to_directory(__lowercase  )
      A_		   =							DatasetInfo.from_directory(__lowercase  )
      assert dataset_info == reloaded
      assert os.path.exists(os.path.join(__lowercase							,"dataset_info.json"  )  )

def 	__snake_case		(      ):



      """simple docstring"""

      A_		   =							DatasetInfo(
          description="foo"							,citation="bar"							,homepage="https://foo.bar"							,license="CC0"							,features=Features({"a": Value("int32"  )}  )							,post_processed={}							,supervised_keys=()							,task_templates=[]							,builder_name="builder"							,config_name="config"							,version="1.0.0"							,splits=[{"name": "train", "num_examples": 42}]							,download_checksums={}							,download_size=1337							,post_processing_size=442							,dataset_size=1234							,size_in_bytes=1337 + 442 + 1234							,)
      A_		   =							dataset_info._to_yaml_dict()
      assert sorted(__lowercase  ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML  )
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            assert key in dataset_info_yaml_dict
            assert isinstance(dataset_info_yaml_dict[key]							,(list, dict, int, str)  )
      A_		   =							yaml.safe_dump(__lowercase  )
      A_		   =							yaml.safe_load(__lowercase  )
      assert dataset_info_yaml_dict == reloaded

def 	__snake_case		(      ):



      """simple docstring"""

      A_		   =							DatasetInfo()
      A_		   =							dataset_info._to_yaml_dict()
      assert dataset_info_yaml_dict == {}



@pytest.mark.parametrize(
    "dataset_infos_dict"							,[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}  ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}  ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"							,features=Features({"a": Value("int32"  )}  )							,builder_name="builder"							,config_name="config"							,version="1.0.0"							,splits=[{"name": "train"}]							,download_size=42							,)
            }  ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42  ),
                "v2": DatasetInfo(dataset_size=1337  ),
            }  ),
    ]							,)
def 	__snake_case		(      __UpperCamelCase     :      int							,__UpperCamelCase     :      DatasetInfosDict  ):



      """simple docstring"""

      A_		   =							str(__lowercase  )
      dataset_infos_dict.write_to_directory(__lowercase  )
      A_		   =							DatasetInfosDict.from_directory(__lowercase  )

      # the config_name of the dataset_infos_dict take over the attribute
      for config_name, dataset_info in dataset_infos_dict.items():
            A_		   =							config_name
            # the yaml representation doesn't include fields like description or citation
            # so we just test that we can recover what we can from the yaml
            A_		   =							DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()  )
      assert dataset_infos_dict == reloaded

      if dataset_infos_dict:
            assert os.path.exists(os.path.join(__lowercase							,"README.md"  )  )






'''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:
  __SCREAMING_SNAKE_CASE     :List[Any]       =				None

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE     :List[str]       =				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE     :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''',
    },
}

__SCREAMING_SNAKE_CASE     :Optional[Any]       =				{
    '''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,
}

__SCREAMING_SNAKE_CASE     :Optional[int]       =				'''▁'''







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							VOCAB_FILES_NAMES
 _lowerCamelCase  : Any           =							PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  : Tuple           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  : int           =							AlbertTokenizer




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]=None	,			snake_case_       :    Optional[Any]=None	,			snake_case_       :    Optional[Any]=True	,			snake_case_       :    str=True	,			snake_case_       :    Tuple=False	,			snake_case_       :    List[Any]="[CLS]"	,			snake_case_       :    Union[str, Any]="[SEP]"	,			snake_case_       :    str="<unk>"	,			snake_case_       :    Union[str, Any]="[SEP]"	,			snake_case_       :    List[Any]="<pad>"	,			snake_case_       :    List[str]="[CLS]"	,			snake_case_       :    int="[MASK]"	,			**snake_case_       :    Any	,			):
      # 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.
      _UpperCAmelCase     							=   (
          AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_	,			normalized=snake_case_    )
          if isinstance(snake_case_	,			snake_case_    )
          else mask_token
      )

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

      _UpperCAmelCase     							=   do_lower_case
      _UpperCAmelCase     							=   remove_space
      _UpperCAmelCase     							=   keep_accents
      _UpperCAmelCase     							=   vocab_file
      _UpperCAmelCase     							=   False if not self.vocab_file else True




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [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      lowercase	(					self       :    Dict	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [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      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    str	,			snake_case_       :    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(snake_case_    ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
           return
      _UpperCAmelCase     							=   os.path.join(
          snake_case_	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

      if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ):
           copyfile(self.vocab_file	,			snake_case_    )

      return (out_vocab_file,)




