



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

@pytest.mark.parametrize('split' ,[None, NamedSplit('train' ), 'train', 'test'] )
def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )   ->   str:
  __lowerCamelCase : int					 =				tmp_path / 'cache'
  __lowerCamelCase : int					 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  __lowerCamelCase : Optional[int]					 =				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  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )   ->   Tuple:
  if issubclass(_lowerCAmelCase ,_lowerCAmelCase ):
    __lowerCamelCase : Dict					 =				csv_path
  elif issubclass(_lowerCAmelCase ,_lowerCAmelCase ):
    __lowerCamelCase : int					 =				[csv_path]
  __lowerCamelCase : Tuple					 =				tmp_path / 'cache'
  __lowerCamelCase : Dict					 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  __lowerCamelCase : Tuple					 =				CsvDatasetReader(_lowerCAmelCase ,cache_dir=_lowerCAmelCase ).read()
  _check_csv_dataset(_lowerCAmelCase ,_lowerCAmelCase )

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

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   List[Any]:
  __lowerCamelCase : List[Any]					 =				tmp_path / 'cache'
  __lowerCamelCase : str					 =				os.path.join(_lowerCAmelCase ,'tmp.csv' )
  __lowerCamelCase : str					 =				CsvDatasetReader({'train': csv_path} ,cache_dir=_lowerCAmelCase ).read()
  CsvDatasetWriter(dataset['train'] ,_lowerCAmelCase ,num_proc=1 ).write()

  __lowerCamelCase : str					 =				iter_csv_file(_lowerCAmelCase )
  __lowerCamelCase : str					 =				iter_csv_file(_lowerCAmelCase )

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

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   Optional[int]:
  __lowerCamelCase : Optional[int]					 =				tmp_path / 'cache'
  __lowerCamelCase : List[str]					 =				os.path.join(_lowerCAmelCase ,'tmp.csv' )
  __lowerCamelCase : str					 =				CsvDatasetReader({'train': csv_path} ,cache_dir=_lowerCAmelCase ).read()
  CsvDatasetWriter(dataset['train'] ,_lowerCAmelCase ,num_proc=2 ).write()

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

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


def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   Dict:
  __lowerCamelCase : Any					 =				tmp_path / 'cache'
  __lowerCamelCase : str					 =				os.path.join(_lowerCAmelCase ,'tmp.csv' )
  __lowerCamelCase : Tuple					 =				CsvDatasetReader({'train': csv_path} ,cache_dir=_lowerCAmelCase ).read()
  with pytest.raises(_lowerCAmelCase ):
    CsvDatasetWriter(dataset['train'] ,_lowerCAmelCase ,num_proc=0 )











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCamelCase							       =		{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							       =		['BarthezTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							       =		['BarthezTokenizerFast']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_barthez import BarthezTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_barthez_fast import BarthezTokenizerFast

else:
   import sys

   _UpperCamelCase							       =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






