






"""simple docstring"""







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 _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      assert isinstance(_snake_case				,	_snake_case							)
      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 _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""text""": """string"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            UpperCAmelCase          =	TextDatasetReader(_snake_case				,	cache_dir=_snake_case				,	keep_in_memory=_snake_case							).read()
      _check_text_dataset(_snake_case				,	_snake_case							)

@pytest.mark.parametrize(
    """features"""				,	[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,	)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

@pytest.mark.parametrize("""split"""				,	[None, NamedSplit("""train"""							), """train""", """test"""]							)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

@pytest.mark.parametrize("""path_type"""				,	[str, list]							)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if issubclass(_snake_case				,	_snake_case							):
            UpperCAmelCase          =	text_path
      elif issubclass(_snake_case				,	_snake_case							):
            UpperCAmelCase          =	[text_path]
      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""text""": """string"""}
      UpperCAmelCase          =	TextDatasetReader(_snake_case				,	cache_dir=_snake_case							).read()
      _check_text_dataset(_snake_case				,	_snake_case							)

def _a			(		_snake_case				,	_snake_case				,	_snake_case=("train",)							):







      """simple docstring"""






      assert isinstance(_snake_case				,	_snake_case							)
      for split in splits:
            UpperCAmelCase          =	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 _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

@pytest.mark.parametrize(
    """features"""				,	[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,	)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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






@pytest.mark.parametrize("""split"""				,	[None, NamedSplit("""train"""							), """train""", """test"""]							)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase					      =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""RoFormerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RoFormerForCausalLM""",
      """RoFormerForMaskedLM""",
      """RoFormerForMultipleChoice""",
      """RoFormerForQuestionAnswering""",
      """RoFormerForSequenceClassification""",
      """RoFormerForTokenClassification""",
      """RoFormerLayer""",
      """RoFormerModel""",
      """RoFormerPreTrainedModel""",
      """load_tf_weights_in_roformer""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRoFormerForCausalLM""",
      """TFRoFormerForMaskedLM""",
      """TFRoFormerForMultipleChoice""",
      """TFRoFormerForQuestionAnswering""",
      """TFRoFormerForSequenceClassification""",
      """TFRoFormerForTokenClassification""",
      """TFRoFormerLayer""",
      """TFRoFormerModel""",
      """TFRoFormerPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FlaxRoFormerForMaskedLM""",
      """FlaxRoFormerForMultipleChoice""",
      """FlaxRoFormerForQuestionAnswering""",
      """FlaxRoFormerForSequenceClassification""",
      """FlaxRoFormerForTokenClassification""",
      """FlaxRoFormerModel""",
      """FlaxRoFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



