




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    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      Optional[int]				)   ->					List[Any]:
      assert isinstance(__UpperCAmelCase     ,     __UpperCAmelCase				)
      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    UpperCAmelCase_				( __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      Optional[int]     ,     __UpperCAmelCase		:      Any				)   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_      		=     {'text': 'string'}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_      		=     TextDatasetReader(__UpperCAmelCase     ,     cache_dir=__UpperCAmelCase     ,     keep_in_memory=__UpperCAmelCase				).read()
      _check_text_dataset(__UpperCAmelCase     ,     __UpperCAmelCase				)

@pytest.mark.parametrize(
    'features'     ,     [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]     ,     )
def    UpperCAmelCase_				( __UpperCAmelCase		:      Any     ,     __UpperCAmelCase		:      Optional[int]     ,     __UpperCAmelCase		:      Optional[int]				)   ->					Tuple:
      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_      		=     {'text': 'string'}
      SCREAMING_SNAKE_CASE_      		=     features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_      		=     (
          Features({feature: Value(__UpperCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_      		=     TextDatasetReader(__UpperCAmelCase     ,     features=__UpperCAmelCase     ,     cache_dir=__UpperCAmelCase				).read()
      _check_text_dataset(__UpperCAmelCase     ,     __UpperCAmelCase				)

@pytest.mark.parametrize('split'     ,     [None, NamedSplit('train'				), 'train', 'test']				)
def    UpperCAmelCase_				( __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      Optional[int]     ,     __UpperCAmelCase		:      Optional[int]				)   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_      		=     {'text': 'string'}
      SCREAMING_SNAKE_CASE_      		=     TextDatasetReader(__UpperCAmelCase     ,     cache_dir=__UpperCAmelCase     ,     split=__UpperCAmelCase				).read()
      _check_text_dataset(__UpperCAmelCase     ,     __UpperCAmelCase				)
      assert dataset.split == split if split else "train"

@pytest.mark.parametrize('path_type'     ,     [str, list]				)
def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      int     ,     __UpperCAmelCase		:      Optional[Any]				)   ->					List[str]:
      if issubclass(__UpperCAmelCase     ,     __UpperCAmelCase				):
            SCREAMING_SNAKE_CASE_      		=     text_path
      elif issubclass(__UpperCAmelCase     ,     __UpperCAmelCase				):
            SCREAMING_SNAKE_CASE_      		=     [text_path]
      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_      		=     {'text': 'string'}
      SCREAMING_SNAKE_CASE_      		=     TextDatasetReader(__UpperCAmelCase     ,     cache_dir=__UpperCAmelCase				).read()
      _check_text_dataset(__UpperCAmelCase     ,     __UpperCAmelCase				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      Tuple=("train",)				)   ->					Dict:
      assert isinstance(__UpperCAmelCase     ,     __UpperCAmelCase				)
      for split in splits:
            SCREAMING_SNAKE_CASE_      		=     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    UpperCAmelCase_				( __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      Optional[int]				)   ->					str:
      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_      		=     {'text': 'string'}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_      		=     TextDatasetReader({'train': text_path}     ,     cache_dir=__UpperCAmelCase     ,     keep_in_memory=__UpperCAmelCase				).read()
      _check_text_datasetdict(__UpperCAmelCase     ,     __UpperCAmelCase				)

@pytest.mark.parametrize(
    'features'     ,     [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]     ,     )
def    UpperCAmelCase_				( __UpperCAmelCase		:      Any     ,     __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      int				)   ->					List[Any]:
      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'cache'
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      SCREAMING_SNAKE_CASE_      		=     {'text': 'string'}
      SCREAMING_SNAKE_CASE_      		=     features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_      		=     (
          Features({feature: Value(__UpperCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_      		=     TextDatasetReader({'train': text_path}     ,     features=__UpperCAmelCase     ,     cache_dir=__UpperCAmelCase				).read()
      _check_text_datasetdict(__UpperCAmelCase     ,     __UpperCAmelCase				)





@pytest.mark.parametrize('split'     ,     [None, NamedSplit('train'				), 'train', 'test']				)
def    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      List[str]				)   ->					Union[str, Any]:
      if split:
            SCREAMING_SNAKE_CASE_      		=     {split: text_path}
      else:
            SCREAMING_SNAKE_CASE_      		=     'train'
            SCREAMING_SNAKE_CASE_      		=     {'train': text_path, 'test': text_path}
      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_      		=     {'text': 'string'}
      SCREAMING_SNAKE_CASE_      		=     TextDatasetReader(__UpperCAmelCase     ,     cache_dir=__UpperCAmelCase				).read()
      _check_text_datasetdict(__UpperCAmelCase     ,     __UpperCAmelCase     ,     splits=list(path.keys()				)				)
      assert all(dataset[split].split == split for split in path.keys()				)





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowerCamelCase__					:  Union[str, Any]      							=				logging.get_logger(__name__)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Dict							,							*_lowerCAmelCase    :						Optional[Any]							,							**_lowerCAmelCase    :						Any		):
       warnings.warn(
           'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use LayoutLMv2ImageProcessor instead.'							,							_lowerCAmelCase							,							)
       super().__init__(*_lowerCAmelCase							,							**_lowerCAmelCase		)