






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import YolosImageProcessor



class     lowerCamelCase (unittest.TestCase     ):







		'''simple docstring'''



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase=7							,						_UpperCamelCase=3							,						_UpperCamelCase=3_0							,						_UpperCamelCase=4_0_0							,						_UpperCamelCase=True							,						_UpperCamelCase=None							,						_UpperCamelCase=True							,						_UpperCamelCase=[0.5, 0.5, 0.5]							,						_UpperCamelCase=[0.5, 0.5, 0.5]							,						_UpperCamelCase=True							,						_UpperCamelCase=1 / 2_5_5							,						_UpperCamelCase=True							,						)      ->				Union[str, Any]:
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							UpperCAmelCase_       : Optional[Any]				=		size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
							UpperCAmelCase_       : Tuple				=		parent
							UpperCAmelCase_       : Union[str, Any]				=		batch_size
							UpperCAmelCase_       : str				=		num_channels
							UpperCAmelCase_       : Union[str, Any]				=		min_resolution
							UpperCAmelCase_       : int				=		max_resolution
							UpperCAmelCase_       : Dict				=		do_resize
							UpperCAmelCase_       : List[Any]				=		size
							UpperCAmelCase_       : List[Any]				=		do_normalize
							UpperCAmelCase_       : List[Any]				=		image_mean
							UpperCAmelCase_       : Union[str, Any]				=		image_std
							UpperCAmelCase_       : Union[str, Any]				=		do_rescale
							UpperCAmelCase_       : List[Any]				=		rescale_factor
							UpperCAmelCase_       : int				=		do_pad



		def    __UpperCAmelCase     (				self  )      ->				Dict:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase=False  )      ->				Optional[int]:
							if not batched:
												UpperCAmelCase_       : Any				=		image_inputs[0]
												if isinstance(lowercase__							,						Image.Image  ):
																	UpperCAmelCase_       : List[str]				=		image.size
												else:
																	UpperCAmelCase_       : int				=		image.shape[1], image.shape[2]
												if w < h:
																	UpperCAmelCase_       : int				=		int(self.size['shortest_edge'] * h / w  )
																	UpperCAmelCase_       : str				=		self.size['''shortest_edge''']
												elif w > h:
																	UpperCAmelCase_       : Union[str, Any]				=		self.size['''shortest_edge''']
																	UpperCAmelCase_       : int				=		int(self.size['shortest_edge'] * w / h  )
												else:
																	UpperCAmelCase_       : str				=		self.size['''shortest_edge''']
																	UpperCAmelCase_       : Optional[Any]				=		self.size['''shortest_edge''']

							else:
												UpperCAmelCase_       : Union[str, Any]				=		[]
												for image in image_inputs:
																	UpperCAmelCase_       : Tuple				=		self.get_expected_values([image]  )
																	expected_values.append((expected_height, expected_width)  )
												UpperCAmelCase_       : Optional[int]				=		max(lowercase__							,						key=lambda _UpperCamelCase  : item[0]  )[0]
												UpperCAmelCase_       : List[Any]				=		max(lowercase__							,						key=lambda _UpperCamelCase  : item[1]  )[1]

							return expected_height, expected_width





@require_torch
@require_vision
class     lowerCamelCase (_UpperCamelCase					,    unittest.TestCase     ):







		'''simple docstring'''



		_snake_case       :       List[str]					=    YolosImageProcessor if is_vision_available() else None



		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							UpperCAmelCase_       : List[str]				=		YolosImageProcessingTester(self  )



		@property
		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()



		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							UpperCAmelCase_       : Any				=		self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(lowercase__							,						'image_mean'  )  )
							self.assertTrue(hasattr(lowercase__							,						'image_std'  )  )
							self.assertTrue(hasattr(lowercase__							,						'do_normalize'  )  )
							self.assertTrue(hasattr(lowercase__							,						'do_resize'  )  )
							self.assertTrue(hasattr(lowercase__							,						'size'  )  )



		def    __UpperCAmelCase     (				self  )      ->				List[Any]:
							UpperCAmelCase_       : List[str]				=		self.image_processing_class.from_dict(self.image_processor_dict  )
							self.assertEqual(image_processor.size							,						{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}  )
							self.assertEqual(image_processor.do_pad							,						lowercase__  )

							UpperCAmelCase_       : Dict				=		self.image_processing_class.from_dict(
							    self.image_processor_dict							,						size=4_2							,						max_size=8_4							,						pad_and_return_pixel_mask=lowercase__  )
							self.assertEqual(image_processor.size							,						{'shortest_edge': 4_2, 'longest_edge': 8_4}  )
							self.assertEqual(image_processor.do_pad							,						lowercase__  )



		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							pass



		def    __UpperCAmelCase     (				self  )      ->				List[str]:
							# Initialize image_processing
							UpperCAmelCase_       : Optional[Any]				=		self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							UpperCAmelCase_       : Optional[Any]				=		prepare_image_inputs(self.image_processor_tester							,						equal_resolution=lowercase__  )
							for image in image_inputs:
												self.assertIsInstance(lowercase__							,						Image.Image  )

							# Test not batched input
							UpperCAmelCase_       : Union[str, Any]				=		image_processing(image_inputs[0]							,						return_tensors='pt'  ).pixel_values

							UpperCAmelCase_       : Optional[int]				=		self.image_processor_tester.get_expected_values(lowercase__  )

							self.assertEqual(
							    encoded_images.shape							,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,						)

							# Test batched
							UpperCAmelCase_       : Tuple				=		self.image_processor_tester.get_expected_values(lowercase__							,						batched=lowercase__  )

							UpperCAmelCase_       : Dict				=		image_processing(lowercase__							,						return_tensors='pt'  ).pixel_values
							self.assertEqual(
							    encoded_images.shape							,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )							,						)



		def    __UpperCAmelCase     (				self  )      ->				Dict:
							# Initialize image_processing
							UpperCAmelCase_       : Optional[Any]				=		self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							UpperCAmelCase_       : str				=		prepare_image_inputs(self.image_processor_tester							,						equal_resolution=lowercase__							,						numpify=lowercase__  )
							for image in image_inputs:
												self.assertIsInstance(lowercase__							,						np.ndarray  )

							# Test not batched input
							UpperCAmelCase_       : Optional[int]				=		image_processing(image_inputs[0]							,						return_tensors='pt'  ).pixel_values

							UpperCAmelCase_       : Optional[Any]				=		self.image_processor_tester.get_expected_values(lowercase__  )

							self.assertEqual(
							    encoded_images.shape							,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,						)

							# Test batched
							UpperCAmelCase_       : Tuple				=		image_processing(lowercase__							,						return_tensors='pt'  ).pixel_values

							UpperCAmelCase_       : Dict				=		self.image_processor_tester.get_expected_values(lowercase__							,						batched=lowercase__  )

							self.assertEqual(
							    encoded_images.shape							,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )							,						)



		def    __UpperCAmelCase     (				self  )      ->				List[Any]:
							# Initialize image_processing
							UpperCAmelCase_       : Union[str, Any]				=		self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							UpperCAmelCase_       : Tuple				=		prepare_image_inputs(self.image_processor_tester							,						equal_resolution=lowercase__							,						torchify=lowercase__  )
							for image in image_inputs:
												self.assertIsInstance(lowercase__							,						torch.Tensor  )

							# Test not batched input
							UpperCAmelCase_       : List[str]				=		image_processing(image_inputs[0]							,						return_tensors='pt'  ).pixel_values

							UpperCAmelCase_       : Optional[Any]				=		self.image_processor_tester.get_expected_values(lowercase__  )

							self.assertEqual(
							    encoded_images.shape							,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,						)

							# Test batched
							UpperCAmelCase_       : Union[str, Any]				=		image_processing(lowercase__							,						return_tensors='pt'  ).pixel_values

							UpperCAmelCase_       : int				=		self.image_processor_tester.get_expected_values(lowercase__							,						batched=lowercase__  )

							self.assertEqual(
							    encoded_images.shape							,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )							,						)



		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							# Initialize image_processings
							UpperCAmelCase_       : str				=		self.image_processing_class(**self.image_processor_dict  )
							UpperCAmelCase_       : Union[str, Any]				=		self.image_processing_class(do_resize=lowercase__							,						do_normalize=lowercase__							,						do_rescale=lowercase__  )
							# create random PyTorch tensors
							UpperCAmelCase_       : Optional[int]				=		prepare_image_inputs(self.image_processor_tester							,						equal_resolution=lowercase__							,						torchify=lowercase__  )
							for image in image_inputs:
												self.assertIsInstance(lowercase__							,						torch.Tensor  )

							# Test whether the method "pad" and calling the image processor return the same tensors
							UpperCAmelCase_       : List[Any]				=		image_processing_a.pad(lowercase__							,						return_tensors='pt'  )
							UpperCAmelCase_       : int				=		image_processing_a(lowercase__							,						return_tensors='pt'  )

							self.assertTrue(
							    torch.allclose(encoded_images_with_method['pixel_values']							,						encoded_images['pixel_values']							,						atol=1E-4  )  )



		@slow
		def    __UpperCAmelCase     (				self  )      ->				List[Any]:
							# prepare image and target
							UpperCAmelCase_       : str				=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'							,						'r'  ) as f:
												UpperCAmelCase_       : Any				=		json.loads(f.read()  )

							UpperCAmelCase_       : Tuple				=		{'''image_id''': 3_9_7_6_9, '''annotations''': target}

							# encode them
							UpperCAmelCase_       : Tuple				=		YolosImageProcessor.from_pretrained('hustvl/yolos-small'  )
							UpperCAmelCase_       : Optional[int]				=		image_processing(images=lowercase__							,						annotations=lowercase__							,						return_tensors='pt'  )

							# verify pixel values
							UpperCAmelCase_       : int				=		torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
							self.assertEqual(encoding['pixel_values'].shape							,						lowercase__  )

							UpperCAmelCase_       : Any				=		torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,						lowercase__							,						atol=1E-4  )  )

							# verify area
							UpperCAmelCase_       : Union[str, Any]				=		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,						lowercase__  )  )
							# verify boxes
							UpperCAmelCase_       : List[Any]				=		torch.Size([6, 4]  )
							self.assertEqual(encoding['labels'][0]['boxes'].shape							,						lowercase__  )
							UpperCAmelCase_       : Dict				=		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,						lowercase__							,						atol=1E-3  )  )
							# verify image_id
							UpperCAmelCase_       : List[Any]				=		torch.tensor([3_9_7_6_9]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,						lowercase__  )  )
							# verify is_crowd
							UpperCAmelCase_       : List[str]				=		torch.tensor([0, 0, 0, 0, 0, 0]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,						lowercase__  )  )
							# verify class_labels
							UpperCAmelCase_       : Any				=		torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']							,						lowercase__  )  )
							# verify orig_size
							UpperCAmelCase_       : List[Any]				=		torch.tensor([4_8_0, 6_4_0]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,						lowercase__  )  )
							# verify size
							UpperCAmelCase_       : Dict				=		torch.tensor([8_0_0, 1_0_6_6]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,						lowercase__  )  )



		@slow
		def    __UpperCAmelCase     (				self  )      ->				Union[str, Any]:
							# prepare image, target and masks_path
							UpperCAmelCase_       : List[str]				=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'							,						'r'  ) as f:
												UpperCAmelCase_       : List[str]				=		json.loads(f.read()  )

							UpperCAmelCase_       : Dict				=		{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

							UpperCAmelCase_       : List[Any]				=		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'  )

							# encode them
							UpperCAmelCase_       : Union[str, Any]				=		YolosImageProcessor(format='coco_panoptic'  )
							UpperCAmelCase_       : List[str]				=		image_processing(images=lowercase__							,						annotations=lowercase__							,						masks_path=lowercase__							,						return_tensors='pt'  )

							# verify pixel values
							UpperCAmelCase_       : List[str]				=		torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
							self.assertEqual(encoding['pixel_values'].shape							,						lowercase__  )

							UpperCAmelCase_       : List[Any]				=		torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,						lowercase__							,						atol=1E-4  )  )

							# verify area
							UpperCAmelCase_       : List[str]				=		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,						lowercase__  )  )
							# verify boxes
							UpperCAmelCase_       : int				=		torch.Size([6, 4]  )
							self.assertEqual(encoding['labels'][0]['boxes'].shape							,						lowercase__  )
							UpperCAmelCase_       : int				=		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,						lowercase__							,						atol=1E-3  )  )
							# verify image_id
							UpperCAmelCase_       : List[str]				=		torch.tensor([3_9_7_6_9]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,						lowercase__  )  )
							# verify is_crowd
							UpperCAmelCase_       : Optional[Any]				=		torch.tensor([0, 0, 0, 0, 0, 0]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,						lowercase__  )  )
							# verify class_labels
							UpperCAmelCase_       : int				=		torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']							,						lowercase__  )  )
							# verify masks
							UpperCAmelCase_       : Union[str, Any]				=		8_2_2_8_7_3
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()							,						lowercase__  )
							# verify orig_size
							UpperCAmelCase_       : Optional[Any]				=		torch.tensor([4_8_0, 6_4_0]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,						lowercase__  )  )
							# verify size
							UpperCAmelCase_       : Optional[int]				=		torch.tensor([8_0_0, 1_0_6_6]  )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,						lowercase__  )  )










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


lowerCAmelCase       		=    get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Union[str, Any]										=						XLNetTokenizer
	_lowerCAmelCase     :		int										=						XLNetTokenizerFast
	_lowerCAmelCase     :		Optional[Any]										=						True
	_lowerCAmelCase     :		Optional[Any]										=						True


	def  A(    self):
							super().setUp()

							# We have a SentencePiece fixture for testing
							__UpperCAmelCase			:							Optional[Any]	    =	XLNetTokenizer(lowercase__				,     keep_accents=lowercase__)
							tokenizer.sanitize_special_tokens()
							tokenizer.save_pretrained(self.tmpdirname)


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	'''<s>'''
							__UpperCAmelCase			:							List[str]	    =	1

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


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	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(lowercase__)				,     1_0_0_6)


	def  A(    self):
							self.assertEqual(self.get_tokenizer().vocab_size				,     1_0_0_0)


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	XLNetTokenizer(lowercase__				,     keep_accents=lowercase__)

							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.tokenize('''This is a test''')
							self.assertListEqual(lowercase__				,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__)				,     [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2])

							__UpperCAmelCase			:							Tuple	    =	tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
							self.assertListEqual(
							    lowercase__				,     [
							        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			:							Union[str, Any]	    =	tokenizer.convert_tokens_to_ids(lowercase__)
							self.assertListEqual(lowercase__				,     [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4])

							__UpperCAmelCase			:							int	    =	tokenizer.convert_ids_to_tokens(lowercase__)
							self.assertListEqual(
							    lowercase__				,     [
							        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  A(    self):
							__UpperCAmelCase			:							str	    =	XLNetTokenizer(lowercase__				,     do_lower_case=lowercase__)
							__UpperCAmelCase			:							int	    =	tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
							self.assertListEqual(
							    lowercase__				,     [
							        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  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	XLNetTokenizer(lowercase__				,     do_lower_case=lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
							self.assertListEqual(
							    lowercase__				,     [
							        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  A(    self):
							__UpperCAmelCase			:							Tuple	    =	XLNetTokenizer.from_pretrained('''xlnet-base-cased''')

							__UpperCAmelCase			:							Optional[int]	    =	tokenizer.encode('''sequence builders'''				,     add_special_tokens=lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	tokenizer.encode('''multi-sequence build'''				,     add_special_tokens=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.build_inputs_with_special_tokens(lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.build_inputs_with_special_tokens(lowercase__				,     lowercase__)

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


	@slow
	def  A(    self):
							# fmt: off
							__UpperCAmelCase			:							Optional[Any]	    =	{'''input_ids''': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 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, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 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, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 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=lowercase__				,     model_name='''xlnet-base-cased'''				,     revision='''c841166438c31ec7ca9a106dee7bb312b73ae511'''				,     )

