



'''simple docstring'''





import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     assert isinstance(__A    ,      __A)
     assert dataset.num_rows == 4
     assert dataset.num_columns == 1
     assert dataset.column_names == ["text"]
     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    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    tmp_path / '''cache'''
     _a           =    {'''text''': '''string'''}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _a           =    TextDatasetReader(__A    ,      cache_dir=__A    ,      keep_in_memory=__A).read()
     _check_text_dataset(__A    ,      __A)




@pytest.mark.parametrize(
    '''features'''    ,      [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]    ,      )
def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    tmp_path / '''cache'''
     _a           =    {'''text''': '''string'''}
     _a           =    features.copy() if features else default_expected_features
     _a           =    (
         Features({feature: Value(__A) for feature, dtype in features.items()}) if features is not None else None
     )
     _a           =    TextDatasetReader(__A    ,      features=__A    ,      cache_dir=__A).read()
     _check_text_dataset(__A    ,      __A)




@pytest.mark.parametrize('''split'''    ,      [None, NamedSplit('''train'''), '''train''', '''test'''])
def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    tmp_path / '''cache'''
     _a           =    {'''text''': '''string'''}
     _a           =    TextDatasetReader(__A    ,      cache_dir=__A    ,      split=__A).read()
     _check_text_dataset(__A    ,      __A)
     assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type'''    ,      [str, list])
def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     if issubclass(__A    ,      __A):
          _a           =    text_path
     elif issubclass(__A    ,      __A):
          _a           =    [text_path]
     _a           =    tmp_path / '''cache'''
     _a           =    {'''text''': '''string'''}
     _a           =    TextDatasetReader(__A    ,      cache_dir=__A).read()
     _check_text_dataset(__A    ,      __A)




def lowerCAmelCase    (__A    ,      __A    ,      __A=("train",)):



     """simple docstring"""



     assert isinstance(__A    ,      __A)
     for split in splits:
          _a           =    dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 1
          assert dataset.column_names == ["text"]
          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    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    tmp_path / '''cache'''
     _a           =    {'''text''': '''string'''}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _a           =    TextDatasetReader({'''train''': text_path}    ,      cache_dir=__A    ,      keep_in_memory=__A).read()
     _check_text_datasetdict(__A    ,      __A)




@pytest.mark.parametrize(
    '''features'''    ,      [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]    ,      )
def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    tmp_path / '''cache'''
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _a           =    {'''text''': '''string'''}
     _a           =    features.copy() if features else default_expected_features
     _a           =    (
         Features({feature: Value(__A) for feature, dtype in features.items()}) if features is not None else None
     )
     _a           =    TextDatasetReader({'''train''': text_path}    ,      features=__A    ,      cache_dir=__A).read()
     _check_text_datasetdict(__A    ,      __A)




@pytest.mark.parametrize('''split'''    ,      [None, NamedSplit('''train'''), '''train''', '''test'''])
def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     if split:
          _a           =    {split: text_path}
     else:
          _a           =    '''train'''
          _a           =    {'''train''': text_path, '''test''': text_path}
     _a           =    tmp_path / '''cache'''
     _a           =    {'''text''': '''string'''}
     _a           =    TextDatasetReader(__A    ,      cache_dir=__A).read()
     _check_text_datasetdict(__A    ,      __A    ,      splits=list(path.keys()))
     assert all(dataset[split].split == split for split in path.keys())








'''simple docstring'''





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			CTRLTokenizer
 __lowerCamelCase       :			Union[str, Any]		=			False
 __lowerCamelCase       :			Any		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a           =    ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)
      _a           =    ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
      _a           =    {'''unk_token''': '''<unk>'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write(json.dumps(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	int:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return CTRLTokenizer.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt react readapt apt'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    CTRLTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
      _a           =    tokenizer.tokenize(A			)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]

      _a           =    [0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



