
"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFResNetForImageClassification, TFResNetModel
   from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class 		a__						:
   def __init__(			self	,    _a	,    _a=3	,    _a=32	,    _a=3	,    _a=10	,    _a=[10, 20, 30, 40]	,    _a=[1, 1, 2, 1]	,    _a=True	,    _a=True	,    _a="relu"	,    _a=3	,    _a=None	,    ):
          lowercase    :				Optional[int]       		=							parent
          lowercase    :				List[Any]       		=							batch_size
          lowercase    :				Tuple       		=							image_size
          lowercase    :				Dict       		=							num_channels
          lowercase    :				Any       		=							embeddings_size
          lowercase    :				str       		=							hidden_sizes
          lowercase    :				int       		=							depths
          lowercase    :				Any       		=							is_training
          lowercase    :				List[str]       		=							use_labels
          lowercase    :				Tuple       		=							hidden_act
          lowercase    :				Any       		=							num_labels
          lowercase    :				List[Any]       		=							scope
          lowercase    :				int       		=							len(_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          lowercase    :				Tuple       		=							None
          if self.use_labels:
                 lowercase    :				Any       		=							ids_tensor([self.batch_size]	,    self.num_labels				)

          lowercase    :				Any       		=							self.get_config()

          return config, pixel_values, labels
   def 			__magic_name__			(			self				):
          return ResNetConfig(
              num_channels=self.num_channels	,    embeddings_size=self.embeddings_size	,    hidden_sizes=self.hidden_sizes	,    depths=self.depths	,    hidden_act=self.hidden_act	,    num_labels=self.num_labels	,    image_size=self.image_size	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Any       		=							TFResNetModel(config=_a				)
          lowercase    :				Optional[int]       		=							model(_a				)
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape	,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Any       		=							self.num_labels
          lowercase    :				int       		=							TFResNetForImageClassification(_a				)
          lowercase    :				List[Any]       		=							model(_a	,    labels=_a				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.prepare_config_and_inputs()
          lowercase		,    lowercase		,    lowercase    :				List[str]       		=							config_and_inputs
          lowercase    :				List[str]       		=							{"pixel_values": pixel_values}
          return config, inputs_dict


@require_tf
class 		a__						(      a_,			a_,			unittest.TestCase						):
   __lowerCAmelCase            =				(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
   __lowerCAmelCase            =				(
       {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							TFResNetModelTester(self				)
          lowercase    :				str       		=							ConfigTester(self	,    config_class=_a	,    has_text_modality=_a				)
   def 			__magic_name__			(			self				):
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()
   def 			__magic_name__			(			self				):
          return
   @unittest.skip(reason="ResNet does not use inputs_embeds"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip(reason="ResNet does not support input and output embeddings"				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Tuple       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Dict       		=							model_class(_a				)
                 lowercase    :				Optional[int]       		=							inspect.signature(model.call				)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowercase    :				Optional[Any]       		=							[*signature.parameters.keys()]

                 lowercase    :				Optional[int]       		=							["pixel_values"]
                 self.assertListEqual(arg_names[:1]	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a				)
   def 			__magic_name__			(			self				):
          def check_hidden_states_output(_a	,    _a	,    _a				):
                 lowercase    :				int       		=							model_class(_a				)
                 lowercase    :				Any       		=							model(**self._prepare_for_class(_a	,    _a				)				)

                 lowercase    :				Tuple       		=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 lowercase    :				Dict       		=							self.model_tester.num_stages
                 self.assertEqual(len(_a				)	,    expected_num_stages + 1				)

                 # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]				)	,    [self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,    )

          lowercase		,    lowercase    :				List[Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				int       		=							["basic", "bottleneck"]
          for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                        lowercase    :				List[str]       		=							layer_type
                        lowercase    :				Tuple       		=							True
                        check_hidden_states_output(_a	,    _a	,    _a				)

                        # check that output_hidden_states also work using config
                        del inputs_dict["output_hidden_states"]
                        lowercase    :				int       		=							True

                        check_hidden_states_output(_a	,    _a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_a				)

   @slow
   def 			__magic_name__			(			self				):
          for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase    :				int       		=							TFResNetModel.from_pretrained(_a				)
                 self.assertIsNotNone(_a				)


def   __magic_name__	(      ) ->				int:
       lowercase    :				Any       		=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image

@require_tf
@require_vision
class 		a__						(      unittest.TestCase						):
   @cached_property
   def 			__magic_name__			(			self				):
          return (
              AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
              if is_vision_available()
              else None
          )

   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

          lowercase    :				List[str]       		=							self.default_image_processor
          lowercase    :				Optional[Any]       		=							prepare_img()
          lowercase    :				Tuple       		=							image_processor(images=_a	,    return_tensors="tf"				)

          # forward pass
          lowercase    :				Tuple       		=							model(**_a				)

          # verify the logits
          lowercase    :				Dict       		=							tf.TensorShape((1, 1_000)				)
          self.assertEqual(outputs.logits.shape	,    _a				)

          lowercase    :				str       		=							tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]				)

          self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()	,    _a	,    atol=1E-4				)				)




"""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"	,    )


