






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   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Optional[Any]:
  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   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Dict:
  UpperCamelCase_:					List[str]          =				tmp_path / 'cache'
  UpperCamelCase_:					List[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_:					List[Any]          =				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   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Any:
  UpperCamelCase_:					int          =				tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCamelCase_:					Optional[Any]          =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  UpperCamelCase_:					int          =				features.copy() if features else default_expected_features
  UpperCamelCase_:					Optional[Any]          =				(
      Features({feature: Value(_lowercase  ) for feature, dtype in features.items()}  ) if features is not None else None
  )
  UpperCamelCase_:					Dict          =				CsvDatasetReader(_lowercase	,    features=_lowercase	,    cache_dir=_lowercase  ).read()
  _check_csv_dataset(_lowercase	,    _lowercase  )

@pytest.mark.parametrize('split'	,    [None, NamedSplit('train'  ), 'train', 'test']  )
def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Optional[Any]:
  UpperCamelCase_:					int          =				tmp_path / 'cache'
  UpperCamelCase_:					Optional[int]          =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  UpperCamelCase_:					Tuple          =				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   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Optional[Any]:







  if issubclass(_lowercase	,    _lowercase  ):
    UpperCamelCase_:					List[str]          =				csv_path
  elif issubclass(_lowercase	,    _lowercase  ):
    UpperCamelCase_:					int          =				[csv_path]
  UpperCamelCase_:					List[str]          =				tmp_path / 'cache'
  UpperCamelCase_:					int          =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  UpperCamelCase_:					Union[str, Any]          =				CsvDatasetReader(_lowercase	,    cache_dir=_lowercase  ).read()
  _check_csv_dataset(_lowercase	,    _lowercase  )

def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__=("train",)  )       ->				str:
  assert isinstance(_lowercase	,    _lowercase  )
  for split in splits:
    UpperCamelCase_:					Optional[int]          =				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   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Tuple:
  UpperCamelCase_:					Dict          =				tmp_path / 'cache'
  UpperCamelCase_:					Optional[int]          =				{'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_:					Dict          =				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   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Optional[int]:
  UpperCamelCase_:					int          =				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_:					Optional[int]          =				features.copy() if features else default_expected_features
  UpperCamelCase_:					int          =				(
      Features({feature: Value(_lowercase  ) for feature, dtype in features.items()}  ) if features is not None else None
  )
  UpperCamelCase_:					List[Any]          =				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   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__	,    UpperCAmelCase__  )       ->				str:







  if split:
    UpperCamelCase_:					Union[str, Any]          =				{split: csv_path}
  else:
    UpperCamelCase_:					Tuple          =				{'train': csv_path, 'test': csv_path}
  UpperCamelCase_:					List[str]          =				tmp_path / 'cache'
  UpperCamelCase_:					Tuple          =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  UpperCamelCase_:					str          =				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   snake_case					(UpperCAmelCase__  )       ->				int:







  with open(_lowercase	,    encoding='utf-8'  ) as csvfile:
    yield from csv.reader(_lowercase  )

def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Optional[int]:
  UpperCamelCase_:					List[str]          =				tmp_path / 'cache'
  UpperCamelCase_:					Union[str, Any]          =				os.path.join(_lowercase	,    'tmp.csv'  )
  UpperCamelCase_:					Optional[int]          =				CsvDatasetReader({'train': csv_path}	,    cache_dir=_lowercase  ).read()
  CsvDatasetWriter(dataset['train']	,    _lowercase	,    num_proc=1  ).write()

  UpperCamelCase_:					List[str]          =				iter_csv_file(_lowercase  )
  UpperCamelCase_:					Union[str, Any]          =				iter_csv_file(_lowercase  )

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

def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Dict:
  UpperCamelCase_:					Any          =				tmp_path / 'cache'
  UpperCamelCase_:					List[Any]          =				os.path.join(_lowercase	,    'tmp.csv'  )
  UpperCamelCase_:					List[str]          =				CsvDatasetReader({'train': csv_path}	,    cache_dir=_lowercase  ).read()
  CsvDatasetWriter(dataset['train']	,    _lowercase	,    num_proc=2  ).write()

  UpperCamelCase_:					Any          =				iter_csv_file(_lowercase  )
  UpperCamelCase_:					List[Any]          =				iter_csv_file(_lowercase  )

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

def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Dict:
  UpperCamelCase_:					int          =				tmp_path / 'cache'
  UpperCamelCase_:					Optional[Any]          =				os.path.join(_lowercase	,    'tmp.csv'  )
  UpperCamelCase_:					List[Any]          =				CsvDatasetReader({'train': csv_path}	,    cache_dir=_lowercase  ).read()
  with pytest.raises(_lowercase  ):
    CsvDatasetWriter(dataset['train']	,    _lowercase	,    num_proc=0  )







from __future__ import annotations

from collections.abc import Generator




def    __A    (   _lowercase       ):

					'''simple docstring'''







					if not isinstance(_lowercase    ,	_lowercase       ) or n < 1:
										raise Exception('''Sequence only defined for positive integers'''       )

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_A									=       3 * n + 1
										yield n




def    __A    (   ):

					'''simple docstring'''







					_A									=       43
					_A									=       tuple(collatz_sequence(_lowercase       )       )
					print(_lowercase       )
					print(f"""Collatz sequence from {n} took {len(_lowercase       )} steps."""       )


if __name__ == "__main__":
				main()



