


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   _lowercase(					__a				:						Optional[int]    ,     __a				:						Optional[int]     ):
							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   _lowercase(					__a				:						str    ,     __a				:						List[str]    ,     __a				:						Tuple     ):
							a__      =tmp_path / 'cache'
							a__      ={'text': 'string'}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														a__      =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   _lowercase(					__a				:						int    ,     __a				:						Optional[Any]    ,     __a				:						Optional[Any]     ):
							a__      =tmp_path / 'cache'
							a__      ={'text': 'string'}
							a__      =features.copy() if features else default_expected_features
							a__      =(
							    Features({feature: Value(UpperCamelCase__     ) for feature, dtype in features.items()}     ) if features is not None else None
							)
							a__      =TextDatasetReader(UpperCamelCase__    ,     features=UpperCamelCase__    ,     cache_dir=UpperCamelCase__     ).read()
							_check_text_dataset(UpperCamelCase__    ,     UpperCamelCase__     )






@pytest.mark.parametrize('split'    ,     [None, NamedSplit('train'     ), 'train', 'test']     )
def   _lowercase(					__a				:						int    ,     __a				:						str    ,     __a				:						Any     ):
							a__      =tmp_path / 'cache'
							a__      ={'text': 'string'}
							a__      =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   _lowercase(					__a				:						List[str]    ,     __a				:						Any    ,     __a				:						Dict     ):






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






def   _lowercase(					__a				:						int    ,     __a				:						str    ,     __a				:						int=("train",)     ):
							assert isinstance(UpperCamelCase__    ,     UpperCamelCase__     )
							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   _lowercase(					__a				:						Tuple    ,     __a				:						Dict    ,     __a				:						Optional[Any]     ):
							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=UpperCamelCase__    ,     keep_in_memory=UpperCamelCase__     ).read()
							_check_text_datasetdict(UpperCamelCase__    ,     UpperCamelCase__     )






@pytest.mark.parametrize(
    'features'    ,     [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]    ,     )
def   _lowercase(					__a				:						Optional[int]    ,     __a				:						List[Any]    ,     __a				:						List[str]     ):
							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(UpperCamelCase__     ) for feature, dtype in features.items()}     ) if features is not None else None
							)
							a__      =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   _lowercase(					__a				:						str    ,     __a				:						Dict    ,     __a				:						int     ):






							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(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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase_     )
class 					lowercase							(			lowercase_     ):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				__SCREAMING_SNAKE_CASE   :					str			     =     field(default='''text-classification'''      ,    metadata={'''include_in_asdict_even_if_is_default''': True}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''text''': Value('''string'''     )}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''labels''': ClassLabel}     )
				__SCREAMING_SNAKE_CASE   :					str			     =     "text"
				__SCREAMING_SNAKE_CASE   :					str			     =     "labels"




				def        a				(   self ,  snake_case			):
											if self.label_column not in features:
																		raise ValueError(F'''Column {self.label_column} is not present in features.'''			)
											if not isinstance(features[self.label_column] ,  snake_case			):
																		raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''			)
											snake_case_     							=       copy.deepcopy(self			)
											snake_case_     							=       self.label_schema.copy()
											snake_case_     							=       features[self.label_column]
											snake_case_     							=       label_schema
											return task_template




				@property
				def        a				(   self			):
											return {
											    self.text_column: "text",
											    self.label_column: "labels",
											}




