

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  UpperCAmelCase_   (_lowerCAmelCase :		Optional[int]     ,			_lowerCAmelCase :		List[str]		):
			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  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		Tuple     ,			_lowerCAmelCase :		Any		):
			__UpperCamelCase    :    List[Any]								=      tmp_path / "cache"
			__UpperCamelCase    :    str								=      {"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    :    List[str]								=      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  UpperCAmelCase_   (_lowerCAmelCase :		Optional[int]     ,			_lowerCAmelCase :		str     ,			_lowerCAmelCase :		Any		):
			__UpperCamelCase    :    Optional[int]								=      tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			__UpperCamelCase    :    int								=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase    :    Optional[int]								=      features.copy() if features else default_expected_features
			__UpperCamelCase    :    Union[str, Any]								=      (
			    Features({feature: Value(_lowerCAmelCase		) for feature, dtype in features.items()}		) if features is not None else None
			)
			__UpperCamelCase    :    Any								=      CsvDatasetReader(_lowerCAmelCase     ,			features=_lowerCAmelCase     ,			cache_dir=_lowerCAmelCase		).read()
			_check_csv_dataset(_lowerCAmelCase     ,			_lowerCAmelCase		)







@pytest.mark.parametrize("split"     ,			[None, NamedSplit("train"		), "train", "test"]		)
def  UpperCAmelCase_   (_lowerCAmelCase :		str     ,			_lowerCAmelCase :		Tuple     ,			_lowerCAmelCase :		Optional[Any]		):
			__UpperCamelCase    :    Union[str, Any]								=      tmp_path / "cache"
			__UpperCamelCase    :    str								=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase    :    Tuple								=      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  UpperCAmelCase_   (_lowerCAmelCase :		List[Any]     ,			_lowerCAmelCase :		Union[str, Any]     ,			_lowerCAmelCase :		Union[str, Any]		):
			if issubclass(_lowerCAmelCase     ,			_lowerCAmelCase		):
						__UpperCamelCase    :    int								=      csv_path
			elif issubclass(_lowerCAmelCase     ,			_lowerCAmelCase		):
						__UpperCamelCase    :    List[str]								=      [csv_path]
			__UpperCamelCase    :    str								=      tmp_path / "cache"
			__UpperCamelCase    :    str								=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase    :    int								=      CsvDatasetReader(_lowerCAmelCase     ,			cache_dir=_lowerCAmelCase		).read()
			_check_csv_dataset(_lowerCAmelCase     ,			_lowerCAmelCase		)







def  UpperCAmelCase_   (_lowerCAmelCase :		Tuple     ,			_lowerCAmelCase :		Optional[Any]     ,			_lowerCAmelCase :		Dict=("train",)		):
			assert isinstance(_lowerCAmelCase     ,			_lowerCAmelCase		)
			for split in splits:
						__UpperCamelCase    :    Union[str, Any]								=      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  UpperCAmelCase_   (_lowerCAmelCase :		List[Any]     ,			_lowerCAmelCase :		Dict     ,			_lowerCAmelCase :		str		):
			__UpperCamelCase    :    Union[str, Any]								=      tmp_path / "cache"
			__UpperCamelCase    :    Union[str, Any]								=      {"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    :    str								=      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  UpperCAmelCase_   (_lowerCAmelCase :		Any     ,			_lowerCAmelCase :		Optional[int]     ,			_lowerCAmelCase :		Any		):
			__UpperCamelCase    :    Any								=      tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			__UpperCamelCase    :    Dict								=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase    :    int								=      features.copy() if features else default_expected_features
			__UpperCamelCase    :    Tuple								=      (
			    Features({feature: Value(_lowerCAmelCase		) for feature, dtype in features.items()}		) if features is not None else None
			)
			__UpperCamelCase    :    str								=      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  UpperCAmelCase_   (_lowerCAmelCase :		Any     ,			_lowerCAmelCase :		Any     ,			_lowerCAmelCase :		int		):
			if split:
						__UpperCamelCase    :    int								=      {split: csv_path}
			else:
						__UpperCamelCase    :    Tuple								=      {"train": csv_path, "test": csv_path}
			__UpperCamelCase    :    Any								=      tmp_path / "cache"
			__UpperCamelCase    :    str								=      {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase    :    Optional[int]								=      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  UpperCAmelCase_   (_lowerCAmelCase :		Tuple		):
			with open(_lowerCAmelCase     ,			encoding="utf-8"		) as csvfile:
						yield from csv.reader(_lowerCAmelCase		)







def  UpperCAmelCase_   (_lowerCAmelCase :		List[Any]     ,			_lowerCAmelCase :		Any		):
			__UpperCamelCase    :    List[str]								=      tmp_path / "cache"
			__UpperCamelCase    :    Optional[int]								=      os.path.join(_lowerCAmelCase     ,			"tmp.csv"		)
			__UpperCamelCase    :    Any								=      CsvDatasetReader({"train": csv_path}     ,			cache_dir=_lowerCAmelCase		).read()
			CsvDatasetWriter(dataset["train"]     ,			_lowerCAmelCase     ,			num_proc=1		).write()

			__UpperCamelCase    :    Optional[Any]								=      iter_csv_file(_lowerCAmelCase		)
			__UpperCamelCase    :    Any								=      iter_csv_file(_lowerCAmelCase		)

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







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		Optional[Any]		):
			__UpperCamelCase    :    Dict								=      tmp_path / "cache"
			__UpperCamelCase    :    Tuple								=      os.path.join(_lowerCAmelCase     ,			"tmp.csv"		)
			__UpperCamelCase    :    List[str]								=      CsvDatasetReader({"train": csv_path}     ,			cache_dir=_lowerCAmelCase		).read()
			CsvDatasetWriter(dataset["train"]     ,			_lowerCAmelCase     ,			num_proc=2		).write()

			__UpperCamelCase    :    Any								=      iter_csv_file(_lowerCAmelCase		)
			__UpperCamelCase    :    Optional[int]								=      iter_csv_file(_lowerCAmelCase		)

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







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


lowercase     :       dict[str, float]        =       {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

lowercase     :       dict[str, float]        =       {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_60_54_01_99E-27,
}







def  UpperCAmelCase_   (_lowerCAmelCase :		str     ,			_lowerCAmelCase :		str     ,			_lowerCAmelCase :		float		):
			if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
						__UpperCamelCase    :    Dict								=      (
						    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
						    F'''Supported values are: {", ".join(_lowerCAmelCase		)}'''
						)
						raise ValueError(_lowerCAmelCase		)
			return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()