


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



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



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



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



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



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



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






from typing import Dict

from .base import GenericTensor, Pipeline






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     int:




           """simple docstring"""

           if tokenize_kwargs is None:
               _lowercase :							Any   	=	{}

           if truncation is not None:
               if "truncation" in tokenize_kwargs:
                   raise ValueError(
                       'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)')
               _lowercase :							List[Any]   	=	truncation

           _lowercase :							Optional[Any]   	=	tokenize_kwargs

           _lowercase :							Tuple   	=	{}
           if return_tensors is not None:
               _lowercase :							Union[str, Any]   	=	return_tensors

           return preprocess_params, {}, postprocess_params
       def     UpperCamelCase  (      self,   lowerCamelCase,   **lowerCamelCase) ->     Dict[str, GenericTensor]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.framework
           _lowercase :							Dict   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
           return model_inputs
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							str   	=	self.model(**lowerCamelCase)
           return model_outputs
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False) ->     Dict:




           """simple docstring"""

           if return_tensors:
               return model_outputs[0]
           if self.framework == "pt":
               return model_outputs[0].tolist()
           elif self.framework == "tf":
               return model_outputs[0].numpy().tolist()




       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return super().__call__(*lowerCamelCase,   **lowerCamelCase)


