



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 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					assert isinstance(_lowerCAmelCase				,		_lowerCAmelCase)
					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 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				tmp_path / 'cache'
					UpperCamelCase_											=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										UpperCamelCase_											=				CsvDatasetReader(_lowerCAmelCase				,		cache_dir=_lowerCAmelCase				,		keep_in_memory=_lowerCAmelCase).read()
					_check_csv_dataset(_lowerCAmelCase				,		_lowerCAmelCase)



@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 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				tmp_path / 'cache'
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					UpperCamelCase_											=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					UpperCamelCase_											=				features.copy() if features else default_expected_features
					UpperCamelCase_											=				(
					    Features({feature: Value(_lowerCAmelCase) for feature, dtype in features.items()}) if features is not None else None
					)
					UpperCamelCase_											=				CsvDatasetReader(_lowerCAmelCase				,		features=_lowerCAmelCase				,		cache_dir=_lowerCAmelCase).read()
					_check_csv_dataset(_lowerCAmelCase				,		_lowerCAmelCase)



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



@pytest.mark.parametrize("path_type"				,		[str, list])
def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					if issubclass(_lowerCAmelCase				,		_lowerCAmelCase):
										UpperCamelCase_											=				csv_path
					elif issubclass(_lowerCAmelCase				,		_lowerCAmelCase):
										UpperCamelCase_											=				[csv_path]
					UpperCamelCase_											=				tmp_path / 'cache'
					UpperCamelCase_											=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					UpperCamelCase_											=				CsvDatasetReader(_lowerCAmelCase				,		cache_dir=_lowerCAmelCase).read()
					_check_csv_dataset(_lowerCAmelCase				,		_lowerCAmelCase)



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase=("train",)):
					assert isinstance(_lowerCAmelCase				,		_lowerCAmelCase)
					for split in splits:
										UpperCamelCase_											=				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 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				tmp_path / 'cache'
					UpperCamelCase_											=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										UpperCamelCase_											=				CsvDatasetReader({"train": csv_path}				,		cache_dir=_lowerCAmelCase				,		keep_in_memory=_lowerCAmelCase).read()
					_check_csv_datasetdict(_lowerCAmelCase				,		_lowerCAmelCase)



@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 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				tmp_path / 'cache'
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					UpperCamelCase_											=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					UpperCamelCase_											=				features.copy() if features else default_expected_features
					UpperCamelCase_											=				(
					    Features({feature: Value(_lowerCAmelCase) for feature, dtype in features.items()}) if features is not None else None
					)
					UpperCamelCase_											=				CsvDatasetReader({"train": csv_path}				,		features=_lowerCAmelCase				,		cache_dir=_lowerCAmelCase).read()
					_check_csv_datasetdict(_lowerCAmelCase				,		_lowerCAmelCase)



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



def 				_lowerCAmelCase      (_lowerCAmelCase):
					with open(_lowerCAmelCase				,		encoding="utf-8") as csvfile:
										yield from csv.reader(_lowerCAmelCase)



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				tmp_path / 'cache'
					UpperCamelCase_											=				os.path.join(_lowerCAmelCase				,		"tmp.csv")
					UpperCamelCase_											=				CsvDatasetReader({"train": csv_path}				,		cache_dir=_lowerCAmelCase).read()
					CsvDatasetWriter(dataset["train"]				,		_lowerCAmelCase				,		num_proc=1).write()

					UpperCamelCase_											=				iter_csv_file(_lowerCAmelCase)
					UpperCamelCase_											=				iter_csv_file(_lowerCAmelCase)

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



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				tmp_path / 'cache'
					UpperCamelCase_											=				os.path.join(_lowerCAmelCase				,		"tmp.csv")
					UpperCamelCase_											=				CsvDatasetReader({"train": csv_path}				,		cache_dir=_lowerCAmelCase).read()
					CsvDatasetWriter(dataset["train"]				,		_lowerCAmelCase				,		num_proc=2).write()

					UpperCamelCase_											=				iter_csv_file(_lowerCAmelCase)
					UpperCamelCase_											=				iter_csv_file(_lowerCAmelCase)

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






def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				tmp_path / 'cache'
					UpperCamelCase_											=				os.path.join(_lowerCAmelCase				,		"tmp.csv")
					UpperCamelCase_											=				CsvDatasetReader({"train": csv_path}				,		cache_dir=_lowerCAmelCase).read()
					with pytest.raises(_lowerCAmelCase):
										CsvDatasetWriter(dataset["train"]				,		_lowerCAmelCase				,		num_proc=0)





from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				HfArgumentParser(_lowerCAmelCase)
					try:
										UpperCamelCase_											=				parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										UpperCamelCase_											=				"Arg --no_{0} is no longer used, please use --no-{0} instead."
										UpperCamelCase_											=				" ".join(str(_lowerCAmelCase).split(" ")[:-1])
										UpperCamelCase_											=				""
										UpperCamelCase_											=				eval(str(_lowerCAmelCase).split(" ")[-1])
										UpperCamelCase_											=				[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:])
															else:
																				wrong_args.append(_lowerCAmelCase)
										if len(_lowerCAmelCase) > 0:
															UpperCamelCase_											=				full_error_msg + begin_error_msg + str(_lowerCAmelCase)
										raise ValueError(_lowerCAmelCase)

					UpperCamelCase_											=				PyTorchBenchmark(args=_lowerCAmelCase)
					benchmark.run()


if __name__ == "__main__":
	main()
