



"""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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							Optional[int] ):
						"""simple docstring"""






						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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							Any     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = tmp_path / "cache"
						snake_case   :			str						    = {"text": "string"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												snake_case   :			Optional[int]						    = 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			int						    = tmp_path / "cache"
						snake_case   :			Any						    = {"text": "string"}
						snake_case   :			Optional[Any]						    = features.copy() if features else default_expected_features
						snake_case   :			Union[str, Any]						    = (
						    Features({feature: Value(lowerCamelCase_ ) for feature, dtype in features.items()} ) if features is not None else None
						)
						snake_case   :			List[str]						    = TextDatasetReader(lowerCamelCase_     ,		features=lowerCamelCase_     ,		cache_dir=lowerCamelCase_ ).read()
						_check_text_dataset(lowerCamelCase_     ,		lowerCamelCase_ )

@pytest.mark.parametrize("split"     ,		[None, NamedSplit("train" ), "train", "test"] )
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = tmp_path / "cache"
						snake_case   :			Union[str, Any]						    = {"text": "string"}
						snake_case   :			Union[str, 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						if issubclass(lowerCamelCase_     ,		lowerCamelCase_ ):
												snake_case   :			List[str]						    = text_path
						elif issubclass(lowerCamelCase_     ,		lowerCamelCase_ ):
												snake_case   :			str						    = [text_path]
						snake_case   :			int						    = tmp_path / "cache"
						snake_case   :			int						    = {"text": "string"}
						snake_case   :			Optional[Any]						    = TextDatasetReader(lowerCamelCase_     ,		cache_dir=lowerCamelCase_ ).read()
						_check_text_dataset(lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							str=("train",) ):
						"""simple docstring"""






						assert isinstance(lowerCamelCase_     ,		lowerCamelCase_ )
						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		(			lowerCamelCase_:							Any     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							Dict ):
						"""simple docstring"""






						snake_case   :			Dict						    = tmp_path / "cache"
						snake_case   :			str						    = {"text": "string"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												snake_case   :			Union[str, Any]						    = 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = tmp_path / "cache"
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						snake_case   :			Union[str, Any]						    = {"text": "string"}
						snake_case   :			Tuple						    = features.copy() if features else default_expected_features
						snake_case   :			Dict						    = (
						    Features({feature: Value(lowerCamelCase_ ) for feature, dtype in features.items()} ) if features is not None else None
						)
						snake_case   :			Tuple						    = 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Any     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						if split:
												snake_case   :			Optional[Any]						    = {split: text_path}
						else:
												snake_case   :			Optional[Any]						    = "train"
												snake_case   :			List[str]						    = {"train": text_path, "test": text_path}
						snake_case   :			Optional[int]						    = tmp_path / "cache"
						snake_case   :			str						    = {"text": "string"}
						snake_case   :			Optional[int]						    = 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() )








"""simple docstring"""







import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


A		=						importlib.util.find_spec('s3fs') is not None

if _has_safs:
			from .safilesystem import SaFileSystem  # noqa: F401

A		=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
			if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
						warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
			fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						if "://" in dataset_path:
												snake_case   :			List[str]						    = dataset_path.split("://" )[1]
						return dataset_path

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							fsspec.AbstractFileSystem ):
						"""simple docstring"""






						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							fsspec.AbstractFileSystem     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			int						    = not is_remote_filesystem(lowerCamelCase_ )
						if is_local:
												# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
												shutil.move(fs._strip_protocol(lowerCamelCase_ )     ,		fs._strip_protocol(lowerCamelCase_ ) )
						else:
												fs.mv(lowerCamelCase_     ,		lowerCamelCase_     ,		recursive=lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						if hasattr(fsspec.asyn     ,		"reset_lock" ):
												# for future fsspec>2022.05.0
												fsspec.asyn.reset_lock()
						else:
												snake_case   :			Union[str, Any]						    = None
												snake_case   :			Optional[int]						    = None
												snake_case   :			List[str]						    = threading.Lock()



