'''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : List[str]							):
     assert isinstance(__snake_case      ,		__snake_case							)
     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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[Any]      ,		__snake_case       : List[str]							):
     _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(__snake_case      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@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    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Any      ,		__snake_case       : Optional[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(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader(__snake_case      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





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





@pytest.mark.parametrize('path_type'      ,		[str, list]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : int      ,		__snake_case       : Optional[int]							):
     if issubclass(__snake_case      ,		__snake_case							):
          _A								=     csv_path
     elif issubclass(__snake_case      ,		__snake_case							):
          _A								=     [csv_path]
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : int      ,		__snake_case       : int=("train",)							):
     assert isinstance(__snake_case      ,		__snake_case							)
     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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Any      ,		__snake_case       : 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({'train': csv_path}      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]      ,		__snake_case       : 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(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader({'train': csv_path}      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : int							):
     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(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case      ,		splits=list(path.keys()							)							)
     assert all(dataset[split].split == split for split in path.keys()							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]							):
     with open(__snake_case      ,		encoding='utf-8'							) as csvfile:
          yield from csv.reader(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=1							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

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





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=2							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

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





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Tuple							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     with pytest.raises(__snake_case							):
          CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=0							)




'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   def __init__(			self			: Any,   UpperCamelCase__			: NestedDataStructureLike[PathLike],   UpperCamelCase__			: Optional[NamedSplit] = None,   UpperCamelCase__			: Optional[Features] = None,   UpperCamelCase__			: str = None,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Optional[Any],   )     -> Tuple:
        super().__init__(
            UpperCamelCase__,   split=UpperCamelCase__,   features=UpperCamelCase__,   cache_dir=UpperCamelCase__,   keep_in_memory=UpperCamelCase__,   streaming=UpperCamelCase__,   num_proc=UpperCamelCase__,   **UpperCamelCase__,   )
        _A								=     path_or_paths if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else {self.split: path_or_paths}
        _A								=     Csv(
            cache_dir=UpperCamelCase__,   data_files=UpperCamelCase__,   features=UpperCamelCase__,   **UpperCamelCase__,   )

   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        # Build iterable dataset
        if self.streaming:
             _A								=     self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
             _A								=     None
             _A								=     None
             _A								=     None
             _A								=     None

             self.builder.download_and_prepare(
                 download_config=UpperCamelCase__,   download_mode=UpperCamelCase__,   verification_mode=UpperCamelCase__,   base_path=UpperCamelCase__,   num_proc=self.num_proc,   )
             _A								=     self.builder.as_dataset(
                 split=self.split,   verification_mode=UpperCamelCase__,   in_memory=self.keep_in_memory    )
        return dataset







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Optional[Any],   UpperCamelCase__			: Dataset,   UpperCamelCase__			: Union[PathLike, BinaryIO],   UpperCamelCase__			: Optional[int] = None,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Dict,   )     -> Any:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

        _A								=     dataset
        _A								=     path_or_buf
        _A								=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _A								=     num_proc
        _A								=     'utf-8'
        _A								=     to_csv_kwargs
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        _A								=     self.to_csv_kwargs.pop('path_or_buf',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('header',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('index',   UpperCamelCase__    )

        if isinstance(self.path_or_buf,   (str, bytes, os.PathLike)    ):
             with open(self.path_or_buf,   'wb+'    ) as buffer:
                  _A								=     self._write(file_obj=UpperCamelCase__,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        else:
             _A								=     self._write(file_obj=self.path_or_buf,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        return written
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: Optional[int]    )     -> str:
        _A		,					_A		,					_A		,					_A								=     args

        _A								=     query_table(
            table=self.dataset.data,   key=slice(UpperCamelCase__,   offset + self.batch_size    ),   indices=self.dataset._indices,   )
        _A								=     batch.to_pandas().to_csv(
            path_or_buf=UpperCamelCase__,   header=header if (offset == 0) else False,   index=UpperCamelCase__,   **UpperCamelCase__    )
        return csv_str.encode(self.encoding    )

   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: BinaryIO,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Dict,   **UpperCamelCase__			: Optional[int]    )     -> int:
        _A								=     0

        if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0,   len(self.dataset    ),   self.batch_size    ),   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                  _A								=     self._batch_csv((offset, header, index, to_csv_kwargs)    )
                  written += file_obj.write(UpperCamelCase__    )

        else:
             _A		,					_A								=     len(self.dataset    ), self.batch_size
             with multiprocessing.Pool(self.num_proc    ) as pool:
                  for csv_str in logging.tqdm(
                      pool.imap(
                          self._batch_csv,   [(offset, header, index, to_csv_kwargs) for offset in range(0,   UpperCamelCase__,   UpperCamelCase__    )],   ),   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                       written += file_obj.write(UpperCamelCase__    )

        return written



