






"""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 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   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=A				,		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]				,		A				,		atol=1E-4			)			)












"""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 			snake_case  (A_      :Dict	,			A_      :Tuple      ):







  '''simple docstring'''

  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 			snake_case  (A_      :str	,			A_      :List[Any]	,			A_      :Optional[Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   tmp_path / 'cache'
  a     :			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():
    a     :			Union[str, Any]				=   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 			snake_case  (A_      :List[str]	,			A_      :Union[str, Any]	,			A_      :Union[str, Any]      ):







  '''simple docstring'''

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




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			snake_case  (A_      :List[str]	,			A_      :str	,			A_      :int      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  a     :			Tuple				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			List[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 			snake_case  (A_      :int	,			A_      :List[Any]	,			A_      :str      ):







  '''simple docstring'''

  if issubclass(A_	,			A_      ):
    a     :			Dict				=   csv_path
  elif issubclass(A_	,			A_      ):
    a     :			Optional[Any]				=   [csv_path]
  a     :			Tuple				=   tmp_path / 'cache'
  a     :			str				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Any				=   CsvDatasetReader(A_	,			cache_dir=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




def 			snake_case  (A_      :List[str]	,			A_      :Dict	,			A_      :Optional[Any]=("train",)      ):







  '''simple docstring'''

  assert isinstance(A_	,			A_      )
  for split in splits:
    a     :			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 			snake_case  (A_      :str	,			A_      :Any	,			A_      :List[str]      ):







  '''simple docstring'''

  a     :			Any				=   tmp_path / 'cache'
  a     :			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():
    a     :			str				=   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 			snake_case  (A_      :Optional[int]	,			A_      :List[Any]	,			A_      :Tuple      ):







  '''simple docstring'''

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







  '''simple docstring'''

  if split:
    a     :			List[Any]				=   {split: csv_path}
  else:
    a     :			str				=   {'train': csv_path, 'test': csv_path}
  a     :			Union[str, Any]				=   tmp_path / 'cache'
  a     :			Tuple				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			List[str]				=   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 			snake_case  (A_      :Dict      ):







  '''simple docstring'''

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




def 			snake_case  (A_      :int	,			A_      :Optional[int]      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  a     :			List[str]				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=1      ).write()

  a     :			int				=   iter_csv_file(A_      )
  a     :			Optional[Any]				=   iter_csv_file(A_      )

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




def 			snake_case  (A_      :Tuple	,			A_      :Tuple      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   tmp_path / 'cache'
  a     :			List[Any]				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=2      ).write()

  a     :			Dict				=   iter_csv_file(A_      )
  a     :			List[Any]				=   iter_csv_file(A_      )

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






def 			snake_case  (A_      :Union[str, Any]	,			A_      :List[Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   tmp_path / 'cache'
  a     :			Dict				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  with pytest.raises(A_      ):
    CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=0      )




