




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    (	lowercase_			,    lowercase_						)       ->	List[Any]:



       """simple docstring"""

       assert isinstance(lowercase_			,    lowercase_						)
       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    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Any:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__							=   TextDatasetReader(lowercase_			,    cache_dir=lowercase_			,    keep_in_memory=lowercase_						).read()
       _check_text_dataset(lowercase_			,    lowercase_						)







@pytest.mark.parametrize(
    '''features'''			,    [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]			,    )
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       A__							=   features.copy() if features else default_expected_features
       A__							=   (
           Features({feature: Value(lowercase_						) for feature, dtype in features.items()}						) if features is not None else None
       )
       A__							=   TextDatasetReader(lowercase_			,    features=lowercase_			,    cache_dir=lowercase_						).read()
       _check_text_dataset(lowercase_			,    lowercase_						)







@pytest.mark.parametrize('''split'''			,    [None, NamedSplit('''train'''						), '''train''', '''test''']						)
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       A__							=   TextDatasetReader(lowercase_			,    cache_dir=lowercase_			,    split=lowercase_						).read()
       _check_text_dataset(lowercase_			,    lowercase_						)
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type'''			,    [str, list]						)
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       if issubclass(lowercase_			,    lowercase_						):
              A__							=   text_path
       elif issubclass(lowercase_			,    lowercase_						):
              A__							=   [text_path]
       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       A__							=   TextDatasetReader(lowercase_			,    cache_dir=lowercase_						).read()
       _check_text_dataset(lowercase_			,    lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=("train",)						)       ->	Union[str, Any]:



       """simple docstring"""

       assert isinstance(lowercase_			,    lowercase_						)
       for split in splits:
              A__							=   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    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Dict:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__							=   TextDatasetReader({'''train''': text_path}			,    cache_dir=lowercase_			,    keep_in_memory=lowercase_						).read()
       _check_text_datasetdict(lowercase_			,    lowercase_						)







@pytest.mark.parametrize(
    '''features'''			,    [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]			,    )
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
       A__							=   {'''text''': '''string'''}
       A__							=   features.copy() if features else default_expected_features
       A__							=   (
           Features({feature: Value(lowercase_						) for feature, dtype in features.items()}						) if features is not None else None
       )
       A__							=   TextDatasetReader({'''train''': text_path}			,    features=lowercase_			,    cache_dir=lowercase_						).read()
       _check_text_datasetdict(lowercase_			,    lowercase_						)







@pytest.mark.parametrize('''split'''			,    [None, NamedSplit('''train'''						), '''train''', '''test''']						)
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowerCamelCase     :		Optional[Any]								=       {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Any								=       [
      """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TrOCRForCausalLM""",
      """TrOCRPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
  from .processing_trocr import TrOCRProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


