





import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Optional[Any]       )			->	List[str]:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       assert dataset.num_rows == 4
       assert dataset.num_columns == 1
       assert dataset.column_names == ["text"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Union[str, Any]						,					__A				:    Optional[int]       )			->	str:

       """simple docstring"""

       a_      :	int							=						tmp_path / 'cache'
       a_      :	Optional[Any]							=						{'text': 'string'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Dict							=						TextDatasetReader(__A						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_text_dataset(__A						,					__A       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Optional[Any]						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Optional[int]							=						tmp_path / 'cache'
       a_      :	Optional[int]							=						{'text': 'string'}
       a_      :	Any							=						features.copy() if features else default_expected_features
       a_      :	Dict							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	List[str]							=						TextDatasetReader(__A						,					features=__A						,					cache_dir=__A       ).read()
       _check_text_dataset(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int						,					__A				:    Dict       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	int							=						tmp_path / 'cache'
       a_      :	Any							=						{'text': 'string'}
       a_      :	List[Any]							=						TextDatasetReader(__A						,					cache_dir=__A						,					split=__A       ).read()
       _check_text_dataset(__A						,					__A       )
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('path_type'						,					[str, list]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    Optional[Any]						,					__A				:    Dict       )			->	int:

       """simple docstring"""

       if issubclass(__A						,					__A       ):
              a_      :	str							=						text_path
       elif issubclass(__A						,					__A       ):
              a_      :	Tuple							=						[text_path]
       a_      :	List[str]							=						tmp_path / 'cache'
       a_      :	str							=						{'text': 'string'}
       a_      :	Optional[int]							=						TextDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_text_dataset(__A						,					__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Optional[int]						,					__A				:    Optional[Any]=("train",)       )			->	Tuple:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       for split in splits:
              a_      :	int							=						dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 1
              assert dataset.column_names == ["text"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Any						,					__A				:    Optional[int]       )			->	str:

       """simple docstring"""

       a_      :	List[Any]							=						tmp_path / 'cache'
       a_      :	int							=						{'text': 'string'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Dict							=						TextDatasetReader({'train': text_path}						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_text_datasetdict(__A						,					__A       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Union[str, Any]						,					__A				:    Optional[int]       )			->	List[str]:

       """simple docstring"""

       a_      :	Optional[Any]							=						tmp_path / 'cache'
       # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
       a_      :	Optional[int]							=						{'text': 'string'}
       a_      :	Any							=						features.copy() if features else default_expected_features
       a_      :	Tuple							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	Union[str, Any]							=						TextDatasetReader({'train': text_path}						,					features=__A						,					cache_dir=__A       ).read()
       _check_text_datasetdict(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    int						,					__A				:    Optional[int]       )			->	Tuple:

       """simple docstring"""

       if split:
              a_      :	Dict							=						{split: text_path}
       else:
              a_      :	Dict							=						'train'
              a_      :	Any							=						{'train': text_path, 'test': text_path}
       a_      :	Optional[Any]							=						tmp_path / 'cache'
       a_      :	Dict							=						{'text': 'string'}
       a_      :	List[Any]							=						TextDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_text_datasetdict(__A						,					__A						,					splits=list(path.keys()       )       )
       assert all(dataset[split].split == split for split in path.keys()       )






from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_   :				Dict						      = [
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed
      elif pkg == "accelerate":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_accelerate_available

         # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
         # Transformers with PyTorch
         if not is_accelerate_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(F'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int=None       )			->	int:

       """simple docstring"""

       require_version(deps[pkg]						,					__A       )


