





'''simple docstring'''

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 DeformableDetrImageProcessor







class __a			( unittest.TestCase      ):





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict=7					,lowerCamelCase					:      Optional[Any]=3					,lowerCamelCase					:      int=30					,lowerCamelCase					:      Any=400					,lowerCamelCase					:      Union[str, Any]=True					,lowerCamelCase					:      Optional[int]=None					,lowerCamelCase					:      Any=True					,lowerCamelCase					:      Any=[0.5, 0.5, 0.5]					,lowerCamelCase					:      Dict=[0.5, 0.5, 0.5]					,lowerCamelCase					:      Optional[int]=True					,lowerCamelCase					:      int=1 / 255					,lowerCamelCase					:      Tuple=True					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							min_resolution
      __SCREAMING_SNAKE_CASE   				=							max_resolution
      __SCREAMING_SNAKE_CASE   				=							do_resize
      __SCREAMING_SNAKE_CASE   				=							size
      __SCREAMING_SNAKE_CASE   				=							do_normalize
      __SCREAMING_SNAKE_CASE   				=							image_mean
      __SCREAMING_SNAKE_CASE   				=							image_std
      __SCREAMING_SNAKE_CASE   				=							do_rescale
      __SCREAMING_SNAKE_CASE   				=							rescale_factor
      __SCREAMING_SNAKE_CASE   				=							do_pad





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      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					:      Optional[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      List[Any]=False     ):

      '''simple docstring'''

      if not batched:
         __SCREAMING_SNAKE_CASE   				=							image_inputs[0]
         if isinstance(lowerCamelCase					,Image.Image     ):
            __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							image.size
         else:
            __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							image.shape[1], image.shape[2]
         if w < h:
            __SCREAMING_SNAKE_CASE   				=							int(self.size["""shortest_edge"""] * h / w     )
            __SCREAMING_SNAKE_CASE   				=							self.size["""shortest_edge"""]
         elif w > h:
            __SCREAMING_SNAKE_CASE   				=							self.size["""shortest_edge"""]
            __SCREAMING_SNAKE_CASE   				=							int(self.size["""shortest_edge"""] * w / h     )
         else:
            __SCREAMING_SNAKE_CASE   				=							self.size["""shortest_edge"""]
            __SCREAMING_SNAKE_CASE   				=							self.size["""shortest_edge"""]

      else:
         __SCREAMING_SNAKE_CASE   				=							[]
         for image in image_inputs:
            __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.get_expected_values([image]     )
            expected_values.append((expected_height, expected_width)     )
         __SCREAMING_SNAKE_CASE   				=							max(lowerCamelCase					,key=lambda lowerCamelCase     : item[0]     )[0]
         __SCREAMING_SNAKE_CASE   				=							max(lowerCamelCase					,key=lambda lowerCamelCase     : item[1]     )[1]

      return expected_height, expected_width







@require_torch
@require_vision
class __a			( _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			List[str]						   =						DeformableDetrImageProcessor if is_vision_available() else None





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							DeformableDetrImageProcessingTester(self     )





   @property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.image_processing_class(**self.image_processor_dict     )
      self.assertTrue(hasattr(lowerCamelCase					,"""image_mean"""     )     )
      self.assertTrue(hasattr(lowerCamelCase					,"""image_std"""     )     )
      self.assertTrue(hasattr(lowerCamelCase					,"""do_normalize"""     )     )
      self.assertTrue(hasattr(lowerCamelCase					,"""do_resize"""     )     )
      self.assertTrue(hasattr(lowerCamelCase					,"""do_rescale"""     )     )
      self.assertTrue(hasattr(lowerCamelCase					,"""do_pad"""     )     )
      self.assertTrue(hasattr(lowerCamelCase					,"""size"""     )     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.image_processing_class.from_dict(self.image_processor_dict     )
      self.assertEqual(image_processor.size					,{"""shortest_edge""": 18, """longest_edge""": 1333}     )
      self.assertEqual(image_processor.do_pad					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.image_processing_class.from_dict(
          self.image_processor_dict					,size=42					,max_size=84					,pad_and_return_pixel_mask=lowerCamelCase     )
      self.assertEqual(image_processor.size					,{"""shortest_edge""": 42, """longest_edge""": 84}     )
      self.assertEqual(image_processor.do_pad					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

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

      # Test not batched input
      __SCREAMING_SNAKE_CASE   				=							image_processing(image_inputs[0]					,return_tensors="""pt"""     ).pixel_values

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.image_processor_tester.get_expected_values(lowerCamelCase     )

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

      # Test batched
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.image_processor_tester.get_expected_values(lowerCamelCase					,batched=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							image_processing(lowerCamelCase					,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					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.image_processing_class(**self.image_processor_dict     )
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE   				=							prepare_image_inputs(self.image_processor_tester					,equal_resolution=lowerCamelCase					,numpify=lowerCamelCase     )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase					,np.ndarray     )

      # Test not batched input
      __SCREAMING_SNAKE_CASE   				=							image_processing(image_inputs[0]					,return_tensors="""pt"""     ).pixel_values

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.image_processor_tester.get_expected_values(lowerCamelCase     )

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

      # Test batched
      __SCREAMING_SNAKE_CASE   				=							image_processing(lowerCamelCase					,return_tensors="""pt"""     ).pixel_values

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.image_processor_tester.get_expected_values(lowerCamelCase					,batched=lowerCamelCase     )

      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]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.image_processing_class(**self.image_processor_dict     )
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE   				=							prepare_image_inputs(self.image_processor_tester					,equal_resolution=lowerCamelCase					,torchify=lowerCamelCase     )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase					,torch.Tensor     )

      # Test not batched input
      __SCREAMING_SNAKE_CASE   				=							image_processing(image_inputs[0]					,return_tensors="""pt"""     ).pixel_values

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.image_processor_tester.get_expected_values(lowerCamelCase     )

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

      # Test batched
      __SCREAMING_SNAKE_CASE   				=							image_processing(lowerCamelCase					,return_tensors="""pt"""     ).pixel_values

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.image_processor_tester.get_expected_values(lowerCamelCase					,batched=lowerCamelCase     )

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





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""					,"""r"""     ) as f:
         __SCREAMING_SNAKE_CASE   				=							json.loads(f.read()     )

      __SCREAMING_SNAKE_CASE   				=							{"""image_id""": 3_9769, """annotations""": target}

      # encode them
      __SCREAMING_SNAKE_CASE   				=							DeformableDetrImageProcessor()
      __SCREAMING_SNAKE_CASE   				=							image_processing(images=lowerCamelCase					,annotations=lowerCamelCase					,return_tensors="""pt"""     )

      # verify pixel values
      __SCREAMING_SNAKE_CASE   				=							torch.Size([1, 3, 800, 1066]     )
      self.assertEqual(encoding["""pixel_values"""].shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]					,lowerCamelCase					,atol=1E-4     )     )

      # verify area
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]					,lowerCamelCase     )     )
      # verify boxes
      __SCREAMING_SNAKE_CASE   				=							torch.Size([6, 4]     )
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,lowerCamelCase					,atol=1E-3     )     )
      # verify image_id
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([3_9769]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,lowerCamelCase     )     )
      # verify is_crowd
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([0, 0, 0, 0, 0, 0]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,lowerCamelCase     )     )
      # verify class_labels
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([75, 75, 63, 65, 17, 17]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,lowerCamelCase     )     )
      # verify orig_size
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([480, 640]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,lowerCamelCase     )     )
      # verify size
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([800, 1066]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,lowerCamelCase     )     )





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""					,"""r"""     ) as f:
         __SCREAMING_SNAKE_CASE   				=							json.loads(f.read()     )

      __SCREAMING_SNAKE_CASE   				=							{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

      __SCREAMING_SNAKE_CASE   				=							pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""     )

      # encode them
      __SCREAMING_SNAKE_CASE   				=							DeformableDetrImageProcessor(format="""coco_panoptic"""     )
      __SCREAMING_SNAKE_CASE   				=							image_processing(images=lowerCamelCase					,annotations=lowerCamelCase					,masks_path=lowerCamelCase					,return_tensors="""pt"""     )

      # verify pixel values
      __SCREAMING_SNAKE_CASE   				=							torch.Size([1, 3, 800, 1066]     )
      self.assertEqual(encoding["""pixel_values"""].shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]					,lowerCamelCase					,atol=1E-4     )     )

      # verify area
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]					,lowerCamelCase     )     )
      # verify boxes
      __SCREAMING_SNAKE_CASE   				=							torch.Size([6, 4]     )
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,lowerCamelCase					,atol=1E-3     )     )
      # verify image_id
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([3_9769]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,lowerCamelCase     )     )
      # verify is_crowd
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([0, 0, 0, 0, 0, 0]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,lowerCamelCase     )     )
      # verify class_labels
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([17, 17, 63, 75, 75, 93]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,lowerCamelCase     )     )
      # verify masks
      __SCREAMING_SNAKE_CASE   				=							82_2873
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,lowerCamelCase     )
      # verify orig_size
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([480, 640]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,lowerCamelCase     )     )
      # verify size
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([800, 1066]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,lowerCamelCase     )     )









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'''				,     )

