




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					)  ->       Optional[Any]:
   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					)  ->       int:
   snake_case				:    str           =	tmp_path / """cache"""
   snake_case				:    Dict           =	{"""text""": """string"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      snake_case				:    Tuple           =	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					)  ->       Tuple:
   snake_case				:    Union[str, Any]           =	tmp_path / """cache"""
   snake_case				:    List[str]           =	{"""text""": """string"""}
   snake_case				:    int           =	features.copy() if features else default_expected_features
   snake_case				:    Tuple           =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   snake_case				:    Union[str, Any]           =	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					)  ->       List[str]:
   snake_case				:    List[str]           =	tmp_path / """cache"""
   snake_case				:    List[str]           =	{"""text""": """string"""}
   snake_case				:    List[Any]           =	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					)  ->       Optional[int]:
   if issubclass(lowercase						,lowercase					):
      snake_case				:    Any           =	text_path
   elif issubclass(lowercase						,lowercase					):
      snake_case				:    str           =	[text_path]
   snake_case				:    Any           =	tmp_path / """cache"""
   snake_case				:    int           =	{"""text""": """string"""}
   snake_case				:    Dict           =	TextDatasetReader(lowercase						,cache_dir=lowercase					).read()
   _check_text_dataset(lowercase						,lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=("train",)					)  ->       List[str]:
   assert isinstance(lowercase						,lowercase					)
   for split in splits:
      snake_case				:    Optional[Any]           =	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					)  ->       Optional[int]:
   snake_case				:    Tuple           =	tmp_path / """cache"""
   snake_case				:    Optional[int]           =	{"""text""": """string"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      snake_case				:    List[Any]           =	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					)  ->       List[Any]:
   snake_case				:    int           =	tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   snake_case				:    Optional[int]           =	{"""text""": """string"""}
   snake_case				:    str           =	features.copy() if features else default_expected_features
   snake_case				:    int           =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   snake_case				:    List[Any]           =	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					)  ->       Dict:
   if split:
      snake_case				:    Any           =	{split: text_path}
   else:
      snake_case				:    Dict           =	"""train"""
      snake_case				:    int           =	{"""train""": text_path, """test""": text_path}
   snake_case				:    Dict           =	tmp_path / """cache"""
   snake_case				:    Optional[int]           =	{"""text""": """string"""}
   snake_case				:    List[Any]           =	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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	Optional[Any]				=   {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Tuple				=   ['RobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[str]				=   [
           'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaForCausalLM',
           'RobertaForMaskedLM',
           'RobertaForMultipleChoice',
           'RobertaForQuestionAnswering',
           'RobertaForSequenceClassification',
           'RobertaForTokenClassification',
           'RobertaModel',
           'RobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaForCausalLM',
           'TFRobertaForMaskedLM',
           'TFRobertaForMultipleChoice',
           'TFRobertaForQuestionAnswering',
           'TFRobertaForSequenceClassification',
           'TFRobertaForTokenClassification',
           'TFRobertaMainLayer',
           'TFRobertaModel',
           'TFRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'FlaxRobertaForCausalLM',
           'FlaxRobertaForMaskedLM',
           'FlaxRobertaForMultipleChoice',
           'FlaxRobertaForQuestionAnswering',
           'FlaxRobertaForSequenceClassification',
           'FlaxRobertaForTokenClassification',
           'FlaxRobertaModel',
           'FlaxRobertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase	:	str				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





