


"""simple docstring"""







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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase    					=      logging.get_logger(__name__)






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							Tuple	=       ["""pixel_values"""]






    def __init__(					self				:    Optional[Any]				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Optional[Dict[str, int]] = None				, __UpperCAmelCase				:    PILImageResampling = PILImageResampling.BILINEAR				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Union[int, float] = 1 / 255				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Optional[Union[float, List[float]]] = None				, __UpperCAmelCase				:    Optional[Union[float, List[float]]] = None				, **__UpperCAmelCase				:    Union[str, Any]				, ):
      super().__init__(**__UpperCAmelCase)
      a						:		Any      							=			size if size is not None else {"shortest_edge": 256}
      a						:		Union[str, Any]      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase)
      a						:		List[Any]      							=			crop_size if crop_size is not None else {"height": 224, "width": 224}
      a						:		Dict      							=			get_size_dict(__UpperCAmelCase)
      a						:		List[Any]      							=			do_resize
      a						:		Dict      							=			size
      a						:		str      							=			resample
      a						:		Tuple      							=			do_center_crop
      a						:		int      							=			crop_size
      a						:		Dict      							=			do_rescale
      a						:		List[str]      							=			rescale_factor
      a						:		Optional[Any]      							=			do_normalize
      a						:		List[Any]      							=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      a						:		int      							=			image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    np.ndarray				, __UpperCAmelCase				:    Dict[str, int]				, __UpperCAmelCase				:    PILImageResampling = PILImageResampling.BICUBIC				, __UpperCAmelCase				:    Optional[Union[str, ChannelDimension]] = None				, **__UpperCAmelCase				:    List[str]				, ):
      a						:		Optional[int]      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase)
      if "shortest_edge" not in size:
        raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
      a						:		int      							=			get_resize_output_image_size(__UpperCAmelCase				, size=size["shortest_edge"]				, default_to_square=__UpperCAmelCase)
      return resize(__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    np.ndarray				, __UpperCAmelCase				:    Dict[str, int]				, __UpperCAmelCase				:    Optional[Union[str, ChannelDimension]] = None				, **__UpperCAmelCase				:    Union[str, Any]				, ):
      a						:		Dict      							=			get_size_dict(__UpperCAmelCase)
      return center_crop(__UpperCAmelCase				, size=(size["height"], size["width"])				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    np.ndarray				, __UpperCAmelCase				:    float				, __UpperCAmelCase				:    Optional[Union[str, ChannelDimension]] = None				, **__UpperCAmelCase				:    List[str]):
      return rescale(__UpperCAmelCase				, scale=__UpperCAmelCase				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    np.ndarray				, __UpperCAmelCase				:    Union[float, List[float]]				, __UpperCAmelCase				:    Union[float, List[float]]				, __UpperCAmelCase				:    Optional[Union[str, ChannelDimension]] = None				, **__UpperCAmelCase				:    Optional[Any]				, ):
      return normalize(__UpperCAmelCase				, mean=__UpperCAmelCase				, std=__UpperCAmelCase				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    ImageInput				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    PILImageResampling = None				, __UpperCAmelCase				:    bool = None				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Optional[float] = None				, __UpperCAmelCase				:    Optional[bool] = None				, __UpperCAmelCase				:    Optional[Union[float, List[float]]] = None				, __UpperCAmelCase				:    Optional[Union[float, List[float]]] = None				, __UpperCAmelCase				:    Optional[Union[str, TensorType]] = None				, __UpperCAmelCase				:    Union[str, ChannelDimension] = ChannelDimension.FIRST				, **__UpperCAmelCase				:    List[str]				, ):
      a						:		Union[str, Any]      							=			do_resize if do_resize is not None else self.do_resize
      a						:		Any      							=			size if size is not None else self.size
      a						:		Dict      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase)
      a						:		List[Any]      							=			resample if resample is not None else self.resample
      a						:		int      							=			do_center_crop if do_center_crop is not None else self.do_center_crop
      a						:		Union[str, Any]      							=			crop_size if crop_size is not None else self.crop_size
      a						:		Optional[Any]      							=			get_size_dict(__UpperCAmelCase)
      a						:		Tuple      							=			do_rescale if do_rescale is not None else self.do_rescale
      a						:		Optional[Any]      							=			rescale_factor if rescale_factor is not None else self.rescale_factor
      a						:		Optional[int]      							=			do_normalize if do_normalize is not None else self.do_normalize
      a						:		Any      							=			image_mean if image_mean is not None else self.image_mean
      a						:		Optional[int]      							=			image_std if image_std is not None else self.image_std

      a						:		Tuple      							=			make_list_of_images(__UpperCAmelCase)

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

      if do_resize and size is None:
        raise ValueError("Size 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.
      a						:		int      							=			[to_numpy_array(__UpperCAmelCase) for image in images]

      if do_resize:
        a						:		Optional[int]      							=			[self.resize(image=__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase) for image in images]

      if do_center_crop:
        a						:		List[Any]      							=			[self.center_crop(image=__UpperCAmelCase				, size=__UpperCAmelCase) for image in images]

      if do_rescale:
        a						:		List[Any]      							=			[self.rescale(image=__UpperCAmelCase				, scale=__UpperCAmelCase) for image in images]

      if do_normalize:
        a						:		Union[str, Any]      							=			[self.normalize(image=__UpperCAmelCase				, mean=__UpperCAmelCase				, std=__UpperCAmelCase) for image in images]

      a						:		Any      							=			[to_channel_dimension_format(__UpperCAmelCase				, __UpperCAmelCase) for image in images]

      a						:		List[Any]      							=			{"pixel_values": images}
      return BatchFeature(data=__UpperCAmelCase				, tensor_type=__UpperCAmelCase)








"""simple docstring"""




import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_A			:       int   		=       get_tests_dir("""fixtures/spiece.model""")

@require_sentencepiece
@require_tokenizers
class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				DebertaVaTokenizer
   __lowerCAmelCase            =				DebertaVaTokenizerFast
   __lowerCAmelCase            =				True
   __lowerCAmelCase            =				True
   def 			__magic_name__			(			self				):
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowercase    :				Any       		=							DebertaVaTokenizer(_a	,    unk_token="<unk>"				)
          tokenizer.save_pretrained(self.tmpdirname				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				int       		=							"this is a test"
          lowercase    :				Tuple       		=							"this is a test"
          return input_text, output_text
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							"<pad>"
          lowercase    :				List[str]       		=							0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a				)	,    _a				)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a				)	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							list(self.get_tokenizer().get_vocab().keys()				)
          self.assertEqual(vocab_keys[0]	,    "<pad>"				)
          self.assertEqual(vocab_keys[1]	,    "<unk>"				)
          self.assertEqual(vocab_keys[-1]	,    "[PAD]"				)
          self.assertEqual(len(_a				)	,    30_001				)
   def 			__magic_name__			(			self				):
          self.assertEqual(self.get_tokenizer().vocab_size	,    30_000				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				List[str]       		=							" \tHeLLo!how  \n Are yoU?  "
          lowercase    :				str       		=							["▁hello", "!", "how", "▁are", "▁you", "?"]
          # fmt: on

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a				)
          lowercase    :				Any       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				str       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a				)
          lowercase    :				Optional[Any]       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				Optional[Any]       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Tuple       		=							["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
          # fmt: on

          lowercase    :				List[Any]       		=							DebertaVaTokenizer(_a	,    split_by_punct=_a				)
          lowercase    :				Tuple       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizerFast(_a	,    split_by_punct=_a				)
          lowercase    :				Any       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				int       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Tuple       		=							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
          # fmt: on

          lowercase    :				List[str]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Optional[Any]       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Optional[int]       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)
          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				List[Any]       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Optional[int]       		=							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
          # fmt: on

          lowercase    :				List[Any]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Optional[int]       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				Optional[int]       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Dict       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				int       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Dict       		=							["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
          # fmt: on

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Any       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Dict       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				Dict       		=							" \tHeLLo!how  \n Are yoU?  "
          lowercase    :				str       		=							["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
          # fmt: on

          lowercase    :				Optional[int]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				int       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				List[str]       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				List[Any]       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.get_tokenizer()
          lowercase    :				Dict       		=							self.get_rust_tokenizer()

          lowercase    :				str       		=							"I was born in 92000, and this is falsé."

          lowercase    :				Optional[Any]       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)
          lowercase    :				Dict       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				str       		=							tokenizer.encode(_a	,    add_special_tokens=_a				)
          lowercase    :				Dict       		=							rust_tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				Optional[int]       		=							self.get_rust_tokenizer()
          lowercase    :				Tuple       		=							tokenizer.encode(_a				)
          lowercase    :				List[str]       		=							rust_tokenizer.encode(_a				)
          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							"This is a test"
          lowercase    :				Tuple       		=							[13, 1, 4_398, 25, 21, 1_289]
          lowercase    :				Optional[int]       		=							["▁", "T", "his", "▁is", "▁a", "▁test"]
          lowercase    :				Optional[Any]       		=							["▁", "<unk>", "his", "▁is", "▁a", "▁test"]

          lowercase    :				Any       		=							DebertaVaTokenizer(_a	,    keep_accents=_a				)
          lowercase    :				Dict       		=							DebertaVaTokenizerFast(_a	,    keep_accents=_a				)

          lowercase    :				str       		=							tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				str       		=							tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				str       		=							tokenizer.convert_ids_to_tokens(_a				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				int       		=							rust_tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				Any       		=							rust_tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				Union[str, Any]       		=							rust_tokenizer.convert_ids_to_tokens(_a				)
          self.assertListEqual(_a	,    _a				)

          # fmt: off
          lowercase    :				int       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Any       		=							[13, 1, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]
          lowercase    :				List[str]       		=							["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
          lowercase    :				str       		=							["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
          # fmt: on

          lowercase    :				Tuple       		=							tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				List[Any]       		=							tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				str       		=							tokenizer.convert_ids_to_tokens(_a				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				Optional[int]       		=							rust_tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				List[Any]       		=							rust_tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				List[Any]       		=							rust_tokenizer.convert_ids_to_tokens(_a				)
          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							DebertaVaTokenizer(_a				)

          lowercase    :				List[Any]       		=							tokenizer.encode("sequence builders"				)
          lowercase    :				Dict       		=							tokenizer.encode("multi-sequence build"				)

          lowercase    :				List[Any]       		=							tokenizer.build_inputs_with_special_tokens(_a				)
          lowercase    :				Dict       		=							tokenizer.build_inputs_with_special_tokens(_a	,    _a				)

          self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]	,    _a				)
          self.assertEqual(
              [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]	,    _a	,    )

   @slow
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				Dict       		=							{"input_ids": [[1, 39_867, 36, 19_390, 486, 27, 35_052, 81_436, 18, 60_685, 1_225, 7, 35_052, 81_436, 18, 9_367, 16_899, 18, 15_937, 53, 594, 773, 18, 16_287, 30_465, 36, 15_937, 6, 41_139, 38, 36_979, 60_763, 191, 6, 34_132, 99, 6, 50_538, 390, 43_230, 6, 34_132, 2_779, 20_850, 14, 699, 1_072, 1_194, 36, 382, 10_901, 53, 7, 699, 1_072, 2_084, 36, 20_422, 630, 53, 19, 105, 3_049, 1_896, 1_053, 16_899, 1_506, 11, 37_978, 4_243, 7, 1_237, 31_869, 200, 16_566, 654, 6, 35_052, 81_436, 7, 55_630, 13_593, 4, 2], [1, 26, 15_011, 13, 667, 8, 1_053, 18, 23_611, 1_237, 72_356, 12_820, 34, 104_134, 1_209, 35, 13_313, 6_627, 21, 202, 347, 7, 164, 2_399, 11, 46, 4_485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 1_232, 2_864, 15_785, 14_951, 105, 5, 8_581, 1_250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 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], [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]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=_a	,    model_name="microsoft/deberta-v2-xlarge"	,    revision="ad6e42c1532ddf3a15c39246b63f5559d558b670"	,    )


