




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
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

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							UpperCAmelCase_		:					int          =  False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor




class 		__A   (      unittest.TestCase      ):

						def __init__(		self :Optional[Any]     ,			__snake_case :Dict     ,			__snake_case :List[str]=7     ,			__snake_case :Union[str, Any]=3     ,			__snake_case :int=18     ,			__snake_case :str=30     ,			__snake_case :int=4_00     ,			__snake_case :Dict=None     ,			__snake_case :Tuple=True     ,			__snake_case :Any=True     ,			__snake_case :Tuple=None     ,			):
										'''simple docstring'''

										__magic_name__						:						str										=size if size is not None else {"""height""": 20, """width""": 20}
										__magic_name__						:						Optional[Any]										=parent
										__magic_name__						:						Optional[Any]										=batch_size
										__magic_name__						:						Optional[int]										=num_channels
										__magic_name__						:						List[Any]										=image_size
										__magic_name__						:						Optional[Any]										=min_resolution
										__magic_name__						:						str										=max_resolution
										__magic_name__						:						List[Any]										=size
										__magic_name__						:						Any										=do_normalize
										__magic_name__						:						int										=do_convert_rgb
										__magic_name__						:						Any										=[5_12, 10_24, 20_48, 40_96]
										__magic_name__						:						List[Any]										=patch_size if patch_size is not None else {"""height""": 16, """width""": 16}

						def      A__    (		self :Dict			):
										'''simple docstring'''

										return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										="""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
										__magic_name__						:						Any										=Image.open(requests.get(__snake_case     ,			stream=__snake_case			).raw			).convert("""RGB"""			)
										return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,    reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""       ,    )
@require_torch
@require_vision
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       PixaStructImageProcessor if is_vision_available() else None

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=PixaStructImageProcessingTester(self			)

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__snake_case     ,			"""do_normalize"""			)			)
										self.assertTrue(hasattr(__snake_case     ,			"""do_convert_rgb"""			)			)

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.image_processor_tester.prepare_dummy_image()

										__magic_name__						:						Dict										=self.image_processing_class(**self.image_processor_dict			)
										__magic_name__						:						str										=20_48

										__magic_name__						:						Tuple										=image_processor(__snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			)
										self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     ,			torch.tensor(0.0606			)     ,			atol=1E-3     ,			rtol=1E-3			)			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										__magic_name__						:						Optional[int]										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			Image.Image			)

										# Test not batched input
										__magic_name__						:						Union[str, Any]										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														__magic_name__						:						Union[str, Any]										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						List[str]										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										__magic_name__						:						str										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			Image.Image			)

										# Test not batched input
										__magic_name__						:						str										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										__magic_name__						:						List[Any]										=True

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														with self.assertRaises(__snake_case			):
																		__magic_name__						:						Optional[Any]										=image_processor(
																		    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches

														__magic_name__						:						Optional[int]										="""Hello"""

														__magic_name__						:						List[Any]										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case     ,			header_text=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						Optional[Any]										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case     ,			header_text=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										__magic_name__						:						str										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case     ,			numpify=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			np.ndarray			)

										__magic_name__						:						List[Any]										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														__magic_name__						:						Optional[Any]										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						str										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										__magic_name__						:						Union[str, Any]										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case     ,			torchify=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			torch.Tensor			)

										# Test not batched input
										__magic_name__						:						Union[str, Any]										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														__magic_name__						:						Tuple										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						str										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,    reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""       ,    )
@require_torch
@require_vision
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       PixaStructImageProcessor if is_vision_available() else None

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=PixaStructImageProcessingTester(self     ,			num_channels=4			)
										__magic_name__						:						Optional[int]										=3

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__snake_case     ,			"""do_normalize"""			)			)
										self.assertTrue(hasattr(__snake_case     ,			"""do_convert_rgb"""			)			)


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										__magic_name__						:						Tuple										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			Image.Image			)

										# Test not batched input
										__magic_name__						:						str										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * (self.image_processor_tester.num_channels - 1)
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														__magic_name__						:						Any										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						List[Any]										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)








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'''				,     )

