






import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__      :	Optional[Any]       	=      get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class 						_a       (  __a						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   XLNetTokenizer
  A_						=   XLNetTokenizerFast
  A_						=   True
  A_						=   True




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     super().setUp()

     # We have a SentencePiece fixture for testing
     UpperCamelCase_									=     XLNetTokenizer(lowerCAmelCase_		,		keep_accents=lowerCAmelCase_ )
     tokenizer.sanitize_special_tokens()
     tokenizer.save_pretrained(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     '<s>'
     UpperCamelCase_									=     1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_ )		,		lowerCAmelCase_ )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_ )		,		lowerCAmelCase_ )




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     list(self.get_tokenizer().get_vocab().keys() )

     self.assertEqual(vocab_keys[0]		,		'<unk>' )
     self.assertEqual(vocab_keys[1]		,		'<s>' )
     self.assertEqual(vocab_keys[-1]		,		'<eod>' )
     self.assertEqual(len(lowerCAmelCase_ )		,		1006 )




  def    _UpperCAmelCase     (     self )			-> int:
     self.assertEqual(self.get_tokenizer().vocab_size		,		1000 )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     XLNetTokenizer(lowerCAmelCase_		,		keep_accents=lowerCAmelCase_ )

     UpperCamelCase_									=     tokenizer.tokenize('This is a test' )
     self.assertListEqual(lowerCAmelCase_		,		['▁This', '▁is', '▁a', '▁t', 'est'] )

     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ )		,		[285, 46, 10, 170, 382] )

     UpperCamelCase_									=     tokenizer.tokenize('I was born in 92000, and this is falsé.' )
     self.assertListEqual(
         lowerCAmelCase_		,		[
             SPIECE_UNDERLINE + 'I',
             SPIECE_UNDERLINE + 'was',
             SPIECE_UNDERLINE + 'b',
             'or',
             'n',
             SPIECE_UNDERLINE + 'in',
             SPIECE_UNDERLINE + '',
             '9',
             '2',
             '0',
             '0',
             '0',
             ',',
             SPIECE_UNDERLINE + 'and',
             SPIECE_UNDERLINE + 'this',
             SPIECE_UNDERLINE + 'is',
             SPIECE_UNDERLINE + 'f',
             'al',
             's',
             'é',
             '.',
         ]		,		)
     UpperCamelCase_									=     tokenizer.convert_tokens_to_ids(lowerCAmelCase_ )
     self.assertListEqual(lowerCAmelCase_		,		[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )

     UpperCamelCase_									=     tokenizer.convert_ids_to_tokens(lowerCAmelCase_ )
     self.assertListEqual(
         lowerCAmelCase_		,		[
             SPIECE_UNDERLINE + 'I',
             SPIECE_UNDERLINE + 'was',
             SPIECE_UNDERLINE + 'b',
             'or',
             'n',
             SPIECE_UNDERLINE + 'in',
             SPIECE_UNDERLINE + '',
             '<unk>',
             '2',
             '0',
             '0',
             '0',
             ',',
             SPIECE_UNDERLINE + 'and',
             SPIECE_UNDERLINE + 'this',
             SPIECE_UNDERLINE + 'is',
             SPIECE_UNDERLINE + 'f',
             'al',
             's',
             '<unk>',
             '.',
         ]		,		)




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     XLNetTokenizer(lowerCAmelCase_		,		do_lower_case=lowerCAmelCase_ )
     UpperCamelCase_									=     tokenizer.tokenize('I was born in 92000, and this is falsé.' )
     self.assertListEqual(
         lowerCAmelCase_		,		[
             SPIECE_UNDERLINE + '',
             'i',
             SPIECE_UNDERLINE + 'was',
             SPIECE_UNDERLINE + 'b',
             'or',
             'n',
             SPIECE_UNDERLINE + 'in',
             SPIECE_UNDERLINE + '',
             '9',
             '2',
             '0',
             '0',
             '0',
             ',',
             SPIECE_UNDERLINE + 'and',
             SPIECE_UNDERLINE + 'this',
             SPIECE_UNDERLINE + 'is',
             SPIECE_UNDERLINE + 'f',
             'al',
             'se',
             '.',
         ]		,		)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )		,		['▁he', 'll', 'o'] )




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     XLNetTokenizer(lowerCAmelCase_		,		do_lower_case=lowerCAmelCase_ )
     UpperCamelCase_									=     tokenizer.tokenize('I was born in 92000, and this is falsé.' )
     self.assertListEqual(
         lowerCAmelCase_		,		[
             SPIECE_UNDERLINE + 'I',
             SPIECE_UNDERLINE + 'was',
             SPIECE_UNDERLINE + 'b',
             'or',
             'n',
             SPIECE_UNDERLINE + 'in',
             SPIECE_UNDERLINE + '',
             '9',
             '2',
             '0',
             '0',
             '0',
             ',',
             SPIECE_UNDERLINE + 'and',
             SPIECE_UNDERLINE + 'this',
             SPIECE_UNDERLINE + 'is',
             SPIECE_UNDERLINE + 'f',
             'al',
             'se',
             '.',
         ]		,		)




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     XLNetTokenizer.from_pretrained('xlnet-base-cased' )

     UpperCamelCase_									=     tokenizer.encode('sequence builders'		,		add_special_tokens=lowerCAmelCase_ )
     UpperCamelCase_									=     tokenizer.encode('multi-sequence build'		,		add_special_tokens=lowerCAmelCase_ )

     UpperCamelCase_									=     tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ )
     UpperCamelCase_									=     tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_		,		lowerCAmelCase_ )

     assert encoded_sentence == text + [4, 3]
     assert encoded_pair == text + [4] + text_a + [4, 3]




  @slow
  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     {'input_ids': [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 13894, 3380, 23, 95, 18, 17634, 2288, 9, 4, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowerCAmelCase_		,		model_name='xlnet-base-cased'		,		revision='c841166438c31ec7ca9a106dee7bb312b73ae511'		,		)














import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     tempfile.mkdtemp()

     # fmt: off
     UpperCamelCase_									=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     UpperCamelCase_									=     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] ) )

     UpperCamelCase_									=     {
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		_UpperCAmelCase )
     with open(self.image_processor_file		,		'w'		,		encoding='utf-8' ) as fp:
        json.dump(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[Any]:
     return BertTokenizer.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[int]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     shutil.rmtree(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     [np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta )]

     UpperCamelCase_									=     [Image.fromarray(np.moveaxis(_UpperCAmelCase		,		0		,		-1 ) ) for x in image_inputs]

     return image_inputs




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.get_tokenizer()
     UpperCamelCase_									=     self.get_image_processor()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     processor.save_pretrained(self.tmpdirname )
     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()		,		image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     UpperCamelCase_									=     self.get_tokenizer(bos_token='(BOS)'		,		eos_token='(EOS)' )
     UpperCamelCase_									=     self.get_image_processor(do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname		,		bos_token='(BOS)'		,		eos_token='(EOS)'		,		do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     image_processor(_UpperCAmelCase		,		return_tensors='np' )
     UpperCamelCase_									=     processor(images=_UpperCAmelCase		,		return_tensors='np' )

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()		,		input_processor[key].sum()		,		delta=1e-2 )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'

     UpperCamelCase_									=     processor(text=_UpperCAmelCase )

     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]		,		encoded_processor[key] )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )

     # test if it raises when no input is passed
     with self.assertRaises(_UpperCAmelCase ):
        processor()




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCamelCase_									=     processor.batch_decode(_UpperCAmelCase )
     UpperCamelCase_									=     tokenizer.batch_decode(_UpperCAmelCase )

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		processor.model_input_names )





