




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


UpperCamelCase								=	logging.get_logger(__name__)



def 				_A  (		lowerCAmelCase_	:   Optional[Any]     ):
     """simple docstring"""

     if isinstance(lowerCAmelCase_  ,							(list, tuple)     ) and isinstance(videos[0]  ,							(list, tuple)     ) and is_valid_image(videos[0][0]     ):
          return videos

     elif isinstance(lowerCAmelCase_  ,							(list, tuple)     ) and is_valid_image(videos[0]     ):
          return [videos]

     elif is_valid_image(lowerCAmelCase_     ):
          return [[videos]]

     raise ValueError(F'Could not make batched video from {videos}'     )






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = ["pixel_values"]


    def __init__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BILINEAR  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float] = 1 / 255  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			None:
         super().__init__(**SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					size if size is not None else {"shortest_edge": 224}
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      param_name="crop_size"  )

         lowerCAmelCase__            =					do_resize
         lowerCAmelCase__            =					size
         lowerCAmelCase__            =					do_center_crop
         lowerCAmelCase__            =					crop_size
         lowerCAmelCase__            =					resample
         lowerCAmelCase__            =					do_rescale
         lowerCAmelCase__            =					rescale_factor
         lowerCAmelCase__            =					do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase__            =					image_std if image_std is not None else IMAGENET_STANDARD_STD


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BILINEAR  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         if "shortest_edge" in size:
              lowerCAmelCase__            =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__  ,      size["shortest_edge"]  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         elif "height" in size and "width" in size:
              lowerCAmelCase__            =					(size["height"], size["width"])
         else:
              raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'  )
         return resize(SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  ,      resample=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'  )
         return center_crop(SCREAMING_SNAKE_CASE__  ,      size=(size["height"], size["width"])  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			Optional[Any]:
         return rescale(SCREAMING_SNAKE_CASE__  ,      scale=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[float, List[float]]  ,      SCREAMING_SNAKE_CASE__	:	Union[float, List[float]]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	str  ,      ) ->			np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__  ,      mean=SCREAMING_SNAKE_CASE__  ,      std=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	ImageInput  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	float = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[ChannelDimension] = ChannelDimension.FIRST  ,      ) ->			np.ndarray:
         if do_resize and size is None or resample is None:
              raise ValueError("Size and resample must be specified if do_resize is True."  )

         if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."  )

         if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."  )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and std must be specified if do_normalize is True."  )

         # All transformations expect numpy arrays.
         lowerCAmelCase__            =					to_numpy_array(SCREAMING_SNAKE_CASE__  )

         if do_resize:
              lowerCAmelCase__            =					self.resize(image=SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  ,      resample=SCREAMING_SNAKE_CASE__  )

         if do_center_crop:
              lowerCAmelCase__            =					self.center_crop(SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  )

         if do_rescale:
              lowerCAmelCase__            =					self.rescale(image=SCREAMING_SNAKE_CASE__  ,      scale=SCREAMING_SNAKE_CASE__  )

         if do_normalize:
              lowerCAmelCase__            =					self.normalize(image=SCREAMING_SNAKE_CASE__  ,      mean=SCREAMING_SNAKE_CASE__  ,      std=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					to_channel_dimension_format(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         return image


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	ImageInput  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	float = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      SCREAMING_SNAKE_CASE__	:	ChannelDimension = ChannelDimension.FIRST  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			PIL.Image.Image:
         lowerCAmelCase__            =					do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__            =					resample if resample is not None else self.resample
         lowerCAmelCase__            =					do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase__            =					do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__            =					rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__            =					do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__            =					image_std if image_std is not None else self.image_std

         lowerCAmelCase__            =					size if size is not None else self.size
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      param_name="crop_size"  )

         if not valid_images(SCREAMING_SNAKE_CASE__  ):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."  )

         lowerCAmelCase__            =					make_batched(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					[
             [
                 self._preprocess_image(
                     image=SCREAMING_SNAKE_CASE__  ,      do_resize=SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  ,      resample=SCREAMING_SNAKE_CASE__  ,      do_center_crop=SCREAMING_SNAKE_CASE__  ,      crop_size=SCREAMING_SNAKE_CASE__  ,      do_rescale=SCREAMING_SNAKE_CASE__  ,      rescale_factor=SCREAMING_SNAKE_CASE__  ,      do_normalize=SCREAMING_SNAKE_CASE__  ,      image_mean=SCREAMING_SNAKE_CASE__  ,      image_std=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      )
                 for img in video
             ]
             for video in videos
         ]

         lowerCAmelCase__            =					{"pixel_values": videos}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__  ,      tensor_type=SCREAMING_SNAKE_CASE__  )





'''simple docstring'''

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


a							   =				get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Tuple        =				XLNetTokenizer
							UpperCAmelCase			:		str        =				XLNetTokenizerFast
							UpperCAmelCase			:		str        =				True
							UpperCAmelCase			:		str        =				True


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

										# We have a SentencePiece fixture for testing
										snake_case:    Tuple      							=XLNetTokenizer(a_      ,						keep_accents=a_			)
										tokenizer.sanitize_special_tokens()
										tokenizer.save_pretrained(self.tmpdirname			)


							def    UpperCamelCase     (				self   :						str			)			->					str:
										snake_case:    List[str]      							='<s>'
										snake_case:    Tuple      							=1

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


							def    UpperCamelCase     (				self   :						Any			)			->					int:
										snake_case:    str      							=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(a_			)      ,						1_0_0_6			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					List[Any]:
										self.assertEqual(self.get_tokenizer().vocab_size      ,						1_0_0_0			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Tuple:
										snake_case:    str      							=XLNetTokenizer(a_      ,						keep_accents=a_			)

										snake_case:    Dict      							=tokenizer.tokenize('This is a test'			)
										self.assertListEqual(a_      ,						['▁This', '▁is', '▁a', '▁t', 'est']			)

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

										snake_case:    List[str]      							=tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
										self.assertListEqual(
										    a_      ,						[
										        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',
										        '.',
										    ]      ,						)
										snake_case:    Optional[int]      							=tokenizer.convert_tokens_to_ids(a_			)
										self.assertListEqual(a_      ,						[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]			)

										snake_case:    List[str]      							=tokenizer.convert_ids_to_tokens(a_			)
										self.assertListEqual(
										    a_      ,						[
										        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   :						Dict			)			->					int:
										snake_case:    Tuple      							=XLNetTokenizer(a_      ,						do_lower_case=a_			)
										snake_case:    Optional[int]      							=tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
										self.assertListEqual(
										    a_      ,						[
										        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   :						Tuple			)			->					Dict:
										snake_case:    str      							=XLNetTokenizer(a_      ,						do_lower_case=a_			)
										snake_case:    List[Any]      							=tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
										self.assertListEqual(
										    a_      ,						[
										        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   :						List[str]			)			->					Optional[Any]:
										snake_case:    Tuple      							=XLNetTokenizer.from_pretrained('xlnet-base-cased'			)

										snake_case:    Optional[Any]      							=tokenizer.encode('sequence builders'      ,						add_special_tokens=a_			)
										snake_case:    List[str]      							=tokenizer.encode('multi-sequence build'      ,						add_special_tokens=a_			)

										snake_case:    Optional[Any]      							=tokenizer.build_inputs_with_special_tokens(a_			)
										snake_case:    Any      							=tokenizer.build_inputs_with_special_tokens(a_      ,						a_			)

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




							@slow
							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										# fmt: off
										snake_case:    Dict      							={'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=a_      ,						model_name='xlnet-base-cased'      ,						revision='c841166438c31ec7ca9a106dee7bb312b73ae511'      ,						)




