






'''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  lowerCAmelCase__		(					a_  :     List[Any]			,						a_  :     Union[str, Any]			)   -> str:
       assert isinstance(a_			,						a_			)
       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__		(					a_  :     Dict			,						a_  :     int			,						a_  :     Union[str, Any]			)   -> int:
       UpperCAmelCase__	:   Dict												=	tmp_path / '''cache'''
       UpperCAmelCase__	:   Optional[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[str]												=	CsvDatasetReader(a_			,						cache_dir=a_			,						keep_in_memory=a_			).read()
       _check_csv_dataset(a_			,						a_			)






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






@pytest.mark.parametrize('''split'''			,						[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def  lowerCAmelCase__		(					a_  :     Tuple			,						a_  :     Optional[int]			,						a_  :     List[Any]			)   -> Any:
       UpperCAmelCase__	:   List[str]												=	tmp_path / '''cache'''
       UpperCAmelCase__	:   Dict												=	{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       UpperCAmelCase__	:   str												=	CsvDatasetReader(a_			,						cache_dir=a_			,						split=a_			).read()
       _check_csv_dataset(a_			,						a_			)
       assert dataset.split == split if split else "train"






@pytest.mark.parametrize('''path_type'''			,						[str, list]			)
def  lowerCAmelCase__		(					a_  :     List[Any]			,						a_  :     Optional[Any]			,						a_  :     List[Any]			)   -> str:
       if issubclass(a_			,						a_			):
              UpperCAmelCase__	:   Union[str, Any]												=	csv_path
       elif issubclass(a_			,						a_			):
              UpperCAmelCase__	:   List[Any]												=	[csv_path]
       UpperCAmelCase__	:   Optional[Any]												=	tmp_path / '''cache'''
       UpperCAmelCase__	:   Optional[int]												=	{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       UpperCAmelCase__	:   Dict												=	CsvDatasetReader(a_			,						cache_dir=a_			).read()
       _check_csv_dataset(a_			,						a_			)






def  lowerCAmelCase__		(					a_  :     Union[str, Any]			,						a_  :     Dict			,						a_  :     Optional[Any]=("train",)			)   -> List[Any]:
       assert isinstance(a_			,						a_			)
       for split in splits:
              UpperCAmelCase__	:   List[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  lowerCAmelCase__		(					a_  :     Union[str, Any]			,						a_  :     int			,						a_  :     Any			)   -> Dict:
       UpperCAmelCase__	:   Union[str, Any]												=	tmp_path / '''cache'''
       UpperCAmelCase__	:   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__	:   Optional[int]												=	CsvDatasetReader({'''train''': csv_path}			,						cache_dir=a_			,						keep_in_memory=a_			).read()
       _check_csv_datasetdict(a_			,						a_			)






@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__		(					a_  :     Optional[Any]			,						a_  :     int			,						a_  :     Any			)   -> Dict:
       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__	:   Union[str, Any]												=	features.copy() if features else default_expected_features
       UpperCAmelCase__	:   List[str]												=	(
           Features({feature: Value(a_			) for feature, dtype in features.items()}			) if features is not None else None
       )
       UpperCAmelCase__	:   List[Any]												=	CsvDatasetReader({'''train''': csv_path}			,						features=a_			,						cache_dir=a_			).read()
       _check_csv_datasetdict(a_			,						a_			)






@pytest.mark.parametrize('''split'''			,						[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def  lowerCAmelCase__		(					a_  :     int			,						a_  :     str			,						a_  :     Tuple			)   -> Dict:
       if split:
              UpperCAmelCase__	:   Any												=	{split: csv_path}
       else:
              UpperCAmelCase__	:   int												=	{'''train''': csv_path, '''test''': csv_path}
       UpperCAmelCase__	:   List[str]												=	tmp_path / '''cache'''
       UpperCAmelCase__	:   List[str]												=	{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       UpperCAmelCase__	:   Union[str, Any]												=	CsvDatasetReader(a_			,						cache_dir=a_			).read()
       _check_csv_datasetdict(a_			,						a_			,						splits=list(path.keys()			)			)
       assert all(dataset[split].split == split for split in path.keys()			)






def  lowerCAmelCase__		(					a_  :     List[Any]			)   -> Union[str, Any]:
       with open(a_			,						encoding='''utf-8'''			) as csvfile:
              yield from csv.reader(a_			)






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

       UpperCAmelCase__	:   Optional[int]												=	iter_csv_file(a_			)
       UpperCAmelCase__	:   List[Any]												=	iter_csv_file(a_			)

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






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

       UpperCAmelCase__	:   int												=	iter_csv_file(a_			)
       UpperCAmelCase__	:   Dict												=	iter_csv_file(a_			)

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






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







'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  import torch

  from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 					__UpperCAmelCase (		unittest.TestCase	):

      '''simple docstring'''





      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	CamembertModel.from_pretrained('''camembert-base'''						)
             model.to(_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	torch.tensor(
                 [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,		device=_UpperCAmelCase  ,		dtype=torch.long  ,		)  # J'aime le camembert !
             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	model(_UpperCAmelCase						)['''last_hidden_state''']
             UpperCAmelCase__	:   List[str]												=	torch.Size((1, 10, 768)						)
             self.assertEqual(output.shape  ,		_UpperCAmelCase						)
             # compare the actual values for a slice.
             UpperCAmelCase__	:   List[Any]												=	torch.tensor(
                 [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]  ,		device=_UpperCAmelCase  ,		dtype=torch.float  ,		)
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		_UpperCAmelCase  ,		atol=1E-4						)						)