


"""simple docstring"""


import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def   a__			(					__lowercase  ,      __lowercase )						->						List[str]:
				assert isinstance(__lowercase  ,      __lowercase )
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				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__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Optional[Any]:
				_A       	=      tmp_path / "cache"
				_A       	=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								_A       	=      CsvDatasetReader(__lowercase  ,      cache_dir=__lowercase  ,      keep_in_memory=__lowercase ).read()
				_check_csv_dataset(__lowercase  ,      __lowercase )






@pytest.mark.parametrize(
    "features"  ,      [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,      )
def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						List[str]:
				_A       	=      tmp_path / "cache"
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				_A       	=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				_A       	=      features.copy() if features else default_expected_features
				_A       	=      (
				    Features({feature: Value(__lowercase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				_A       	=      CsvDatasetReader(__lowercase  ,      features=__lowercase  ,      cache_dir=__lowercase ).read()
				_check_csv_dataset(__lowercase  ,      __lowercase )






@pytest.mark.parametrize("split"  ,      [None, NamedSplit("train" ), "train", "test"] )
def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Any:
				_A       	=      tmp_path / "cache"
				_A       	=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				_A       	=      CsvDatasetReader(__lowercase  ,      cache_dir=__lowercase  ,      split=__lowercase ).read()
				_check_csv_dataset(__lowercase  ,      __lowercase )
				assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"  ,      [str, list] )
def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Dict:
				if issubclass(__lowercase  ,      __lowercase ):
								_A       	=      csv_path
				elif issubclass(__lowercase  ,      __lowercase ):
								_A       	=      [csv_path]
				_A       	=      tmp_path / "cache"
				_A       	=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				_A       	=      CsvDatasetReader(__lowercase  ,      cache_dir=__lowercase ).read()
				_check_csv_dataset(__lowercase  ,      __lowercase )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=("train",) )						->						str:
				assert isinstance(__lowercase  ,      __lowercase )
				for split in splits:
								_A       	=      dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								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__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						List[Any]:
				_A       	=      tmp_path / "cache"
				_A       	=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								_A       	=      CsvDatasetReader({"train": csv_path}  ,      cache_dir=__lowercase  ,      keep_in_memory=__lowercase ).read()
				_check_csv_datasetdict(__lowercase  ,      __lowercase )






@pytest.mark.parametrize(
    "features"  ,      [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,      )
def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						List[Any]:
				_A       	=      tmp_path / "cache"
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				_A       	=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				_A       	=      features.copy() if features else default_expected_features
				_A       	=      (
				    Features({feature: Value(__lowercase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				_A       	=      CsvDatasetReader({"train": csv_path}  ,      features=__lowercase  ,      cache_dir=__lowercase ).read()
				_check_csv_datasetdict(__lowercase  ,      __lowercase )






@pytest.mark.parametrize("split"  ,      [None, NamedSplit("train" ), "train", "test"] )
def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				if split:
								_A       	=      {split: csv_path}
				else:
								_A       	=      {"train": csv_path, "test": csv_path}
				_A       	=      tmp_path / "cache"
				_A       	=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
				_A       	=      CsvDatasetReader(__lowercase  ,      cache_dir=__lowercase ).read()
				_check_csv_datasetdict(__lowercase  ,      __lowercase  ,      splits=list(path.keys() ) )
				assert all(dataset[split].split == split for split in path.keys() )






def   a__			(					__lowercase )						->						Union[str, Any]:
				with open(__lowercase  ,      encoding="utf-8" ) as csvfile:
								yield from csv.reader(__lowercase )






def   a__			(					__lowercase  ,      __lowercase )						->						Optional[int]:
				_A       	=      tmp_path / "cache"
				_A       	=      os.path.join(__lowercase  ,      "tmp.csv" )
				_A       	=      CsvDatasetReader({"train": csv_path}  ,      cache_dir=__lowercase ).read()
				CsvDatasetWriter(dataset["train"]  ,      __lowercase  ,      num_proc=1 ).write()

				_A       	=      iter_csv_file(__lowercase )
				_A       	=      iter_csv_file(__lowercase )

				for rowa, rowa in zip(__lowercase  ,      __lowercase ):
								assert rowa == rowa






def   a__			(					__lowercase  ,      __lowercase )						->						Optional[Any]:
				_A       	=      tmp_path / "cache"
				_A       	=      os.path.join(__lowercase  ,      "tmp.csv" )
				_A       	=      CsvDatasetReader({"train": csv_path}  ,      cache_dir=__lowercase ).read()
				CsvDatasetWriter(dataset["train"]  ,      __lowercase  ,      num_proc=2 ).write()

				_A       	=      iter_csv_file(__lowercase )
				_A       	=      iter_csv_file(__lowercase )

				for rowa, rowa in zip(__lowercase  ,      __lowercase ):
								assert rowa == rowa






def   a__			(					__lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      tmp_path / "cache"
				_A       	=      os.path.join(__lowercase  ,      "tmp.csv" )
				_A       	=      CsvDatasetReader({"train": csv_path}  ,      cache_dir=__lowercase ).read()
				with pytest.raises(__lowercase ):
								CsvDatasetWriter(dataset["train"]  ,      __lowercase  ,      num_proc=0 )



"""simple docstring"""


import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


a_											=     2 << 20  # 2mb
a_											=     get_logger(__name__)
class snake_case   (	_UpperCamelCase):







		def 			a_			(       self   :							Tuple		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      self._options.get_all_options().get("runner"		)
						return runner in [None, "DirectRunner", "PortableRunner"]






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=False )						->						Any:
				_A       	=      FileSystems
				if fs.exists(__lowercase ):
								if force_upload:
												logger.info(f"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.""" )
								else:
												logger.info(f"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.""" )
												return
				with fs.create(__lowercase ) as remote_file:
								with open(__lowercase  ,      "rb" ) as local_file:
												_A       	=      local_file.read(__lowercase )
												while chunk:
																remote_file.write(__lowercase )
																_A       	=      local_file.read(__lowercase )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=False )						->						Union[str, Any]:
				_A       	=      FileSystems
				if os.path.exists(__lowercase ):
								if force_download:
												logger.info(f"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.""" )
								else:
												logger.info(f"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False.""" )
												return
				with fs.open(__lowercase ) as remote_file:
								with open(__lowercase  ,      "wb" ) as local_file:
												_A       	=      remote_file.read(__lowercase )
												while chunk:
																local_file.write(__lowercase )
																_A       	=      remote_file.read(__lowercase )