

'''simple docstring'''

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  LxmertTokenizer
     UpperCAmelCase_						:     List[Any]  		=  LxmertTokenizerFast
     UpperCAmelCase_						:     str  		=  True
     UpperCAmelCase_						:     Tuple  		=  True


     def        UpperCAmelCase_	(		self			:					str	)						->     Union[str, Any]:
       super().setUp()

       UpperCAmelCase	:     Dict						 =			[
           '[UNK]',
           '[CLS]',
           '[SEP]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       UpperCAmelCase	:     Optional[Any]						 =			os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['vocab_file']	)
       with open(self.vocab_file					,      'w'					,      encoding='utf-8'	) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					List[Any]	)						->     Optional[Any]:
       UpperCAmelCase	:     List[str]						 =			'UNwant\u00E9d,running'
       UpperCAmelCase	:     Union[str, Any]						 =			'unwanted, running'
       return input_text, output_text


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     str:
       UpperCAmelCase	:     Union[str, Any]						 =			self.tokenizer_class(self.vocab_file	)

       UpperCAmelCase	:     Dict						 =			tokenizer.tokenize('UNwant\u00E9d,running'	)
       self.assertListEqual(lowercase_					,      ['un', '##want', '##ed', ',', 'runn', '##ing']	)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_	)					,      [7, 4, 5, 10, 8, 9]	)




     def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
       if not self.test_rust_tokenizer:
         return

       UpperCAmelCase	:     Union[str, Any]						 =			self.get_tokenizer()
       UpperCAmelCase	:     Union[str, Any]						 =			self.get_rust_tokenizer()

       UpperCAmelCase	:     List[str]						 =			'I was born in 92000, and this is falsé.'

       UpperCAmelCase	:     Union[str, Any]						 =			tokenizer.tokenize(lowercase_	)
       UpperCAmelCase	:     Optional[Any]						 =			rust_tokenizer.tokenize(lowercase_	)
       self.assertListEqual(lowercase_					,      lowercase_	)

       UpperCAmelCase	:     List[str]						 =			tokenizer.encode(lowercase_					,      add_special_tokens=lowercase_	)
       UpperCAmelCase	:     Union[str, Any]						 =			rust_tokenizer.encode(lowercase_					,      add_special_tokens=lowercase_	)
       self.assertListEqual(lowercase_					,      lowercase_	)

       UpperCAmelCase	:     List[str]						 =			self.get_rust_tokenizer()
       UpperCAmelCase	:     List[str]						 =			tokenizer.encode(lowercase_	)
       UpperCAmelCase	:     Tuple						 =			rust_tokenizer.encode(lowercase_	)
       self.assertListEqual(lowercase_					,      lowercase_	)







'''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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     Tuple						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 UpperCAmelCase	:     Optional[int]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     Union[str, Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	split=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )
 assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'  ,	[str, list]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Tuple						 =			text_path
 elif issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Optional[Any]						 =			[text_path]
 UpperCAmelCase	:     List[Any]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Union[str, Any]						 =			{'text': 'string'}
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=("train",)     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 for split in splits:
  UpperCAmelCase	:     Union[str, Any]						 =			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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Any						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     int						 =			TextDatasetReader({'train': text_path}  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Union[str, Any]						 =			tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     Union[str, Any]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader({'train': text_path}  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if split:
  UpperCAmelCase	:     int						 =			{split: text_path}
 else:
  UpperCAmelCase	:     int						 =			'train'
  UpperCAmelCase	:     Any						 =			{'train': text_path, 'test': text_path}
 UpperCAmelCase	:     Dict						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Any						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_  ,	splits=list(path.keys()     )     )
 assert all(dataset[split].split == split for split in path.keys()     )




