


"""simple docstring"""


import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
    from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class       A__						:


     """simple docstring"""

     def __init__(						self:    str  ,		__a:    Optional[Any]  ,		__a:    Optional[Any]=13  ,		__a:    Tuple=32  ,		__a:    List[str]=3  ,		__a:    Tuple=4  ,		__a:    Any=[10, 20, 30, 40]  ,		__a:    List[Any]=[2, 2, 3, 2]  ,		__a:    Tuple=True  ,		__a:    Tuple=True  ,		__a:    Dict=37  ,		__a:    Union[str, Any]="gelu"  ,		__a:    Optional[Any]=10  ,		__a:    Any=0.02  ,		__a:    Dict=["stage2", "stage3", "stage4"]  ,		__a:    Any=[2, 3, 4]  ,		__a:    List[Any]=None  ,		)->     List[str]:
           lowerCamelCase       :       Tuple   =						parent
           lowerCamelCase       :       Union[str, Any]   =						batch_size
           lowerCamelCase       :       Union[str, Any]   =						image_size
           lowerCamelCase       :       List[str]   =						num_channels
           lowerCamelCase       :       Optional[int]   =						num_stages
           lowerCamelCase       :       Optional[Any]   =						hidden_sizes
           lowerCamelCase       :       Optional[Any]   =						depths
           lowerCamelCase       :       Any   =						is_training
           lowerCamelCase       :       Union[str, Any]   =						use_labels
           lowerCamelCase       :       Optional[Any]   =						intermediate_size
           lowerCamelCase       :       Tuple   =						hidden_act
           lowerCamelCase       :       Optional[Any]   =						num_labels
           lowerCamelCase       :       int   =						initializer_range
           lowerCamelCase       :       Optional[int]   =						out_features
           lowerCamelCase       :       Tuple   =						out_indices
           lowerCamelCase       :       Union[str, Any]   =						scope

     def 		a__    (						self:    int						)->     Any:
           lowerCamelCase       :       Optional[int]   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

           lowerCamelCase       :       Dict   =						self.get_config()
           return config, pixel_values, labels

     def 		a__    (						self:    Any						)->     List[str]:
           return ConvNextConfig(
               num_channels=self.num_channels  ,		hidden_sizes=self.hidden_sizes  ,		depths=self.depths  ,		num_stages=self.num_stages  ,		hidden_act=self.hidden_act  ,		is_decoder=lowerCamelCase_  ,		initializer_range=self.initializer_range  ,		out_features=self.out_features  ,		out_indices=self.out_indices  ,		num_labels=self.num_labels  ,		)

     def 		a__    (						self:    List[str]  ,		__a:    List[Any]  ,		__a:    Dict  ,		__a:    Tuple						)->     Union[str, Any]:
           lowerCamelCase       :       List[str]   =						ConvNextModel(config=lowerCamelCase_						)
           model.to(lowerCamelCase_						)
           model.eval()
           lowerCamelCase       :       Tuple   =						model(lowerCamelCase_						)
           # 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 		a__    (						self:    Optional[Any]  ,		__a:    Optional[int]  ,		__a:    int  ,		__a:    Union[str, Any]						)->     int:
           lowerCamelCase       :       Optional[Any]   =						ConvNextForImageClassification(lowerCamelCase_						)
           model.to(lowerCamelCase_						)
           model.eval()
           lowerCamelCase       :       Any   =						model(lowerCamelCase_  ,		labels=lowerCamelCase_						)
           self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels)						)

     def 		a__    (						self:    Optional[Any]  ,		__a:    Optional[int]  ,		__a:    int  ,		__a:    Any						)->     Optional[Any]:
           lowerCamelCase       :       Any   =						ConvNextBackbone(config=lowerCamelCase_						)
           model.to(lowerCamelCase_						)
           model.eval()
           lowerCamelCase       :       Optional[Any]   =						model(lowerCamelCase_						)

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps						)  ,		len(config.out_features						)						)
           self.parent.assertListEqual(list(result.feature_maps[0].shape						)  ,		[self.batch_size, self.hidden_sizes[1], 4, 4]						)

           # verify channels
           self.parent.assertEqual(len(model.channels						)  ,		len(config.out_features						)						)
           self.parent.assertListEqual(model.channels  ,		config.hidden_sizes[1:]						)

           # verify backbone works with out_features=None
           lowerCamelCase       :       int   =						None
           lowerCamelCase       :       int   =						ConvNextBackbone(config=lowerCamelCase_						)
           model.to(lowerCamelCase_						)
           model.eval()
           lowerCamelCase       :       Any   =						model(lowerCamelCase_						)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps						)  ,		1						)
           self.parent.assertListEqual(list(result.feature_maps[0].shape						)  ,		[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

           # verify channels
           self.parent.assertEqual(len(model.channels						)  ,		1						)
           self.parent.assertListEqual(model.channels  ,		[config.hidden_sizes[-1]]						)







     def 		a__    (						self:    Optional[int]						)->     int:
           lowerCamelCase       :       List[str]   =						self.prepare_config_and_inputs()
           lowerCamelCase       :       Tuple   =						config_and_inputs
           lowerCamelCase       :       int   =						{"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class       A__						(    lowercase_			,  lowercase_			,  unittest.TestCase):


     """simple docstring"""
     snake_case__						:       List[Any]				    =(
         (
             ConvNextModel,
             ConvNextForImageClassification,
             ConvNextBackbone,
         )
         if is_torch_available()
         else ()
     )
     snake_case__						:       Optional[int]				    =(
         {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
         if is_torch_available()
         else {}
     )

     snake_case__						:       Optional[int]				    =True
     snake_case__						:       int				    =False
     snake_case__						:       str				    =False
     snake_case__						:       Optional[int]				    =False
     snake_case__						:       Optional[Any]				    =False

     def 		a__    (						self:    Dict						)->     Optional[Any]:
           lowerCamelCase       :       str   =						ConvNextModelTester(self						)
           lowerCamelCase       :       List[str]   =						ConfigTester(self  ,		config_class=lowerCamelCase_  ,		has_text_modality=lowerCamelCase_  ,		hidden_size=37						)

     def 		a__    (						self:    Optional[int]						)->     int:
           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 		a__    (						self:    Optional[int]						)->     str:
           return

     @unittest.skip(reason="""ConvNext does not use inputs_embeds"""						)
     def 		a__    (						self:    int						)->     List[str]:
           pass

     @unittest.skip(reason="""ConvNext does not support input and output embeddings"""						)
     def 		a__    (						self:    Any						)->     List[Any]:
           pass

     @unittest.skip(reason="""ConvNext does not use feedforward chunking"""						)
     def 		a__    (						self:    Optional[int]						)->     Union[str, Any]:
           pass

     def 		a__    (						self:    Optional[Any]						)->     Tuple:
           lowerCamelCase       :       List[Any]   =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase       :       List[str]   =						model_class(lowerCamelCase_						)
                 lowerCamelCase       :       int   =						inspect.signature(model.forward						)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase       :       Dict   =						[*signature.parameters.keys()]

                 lowerCamelCase       :       Optional[Any]   =						["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]  ,		lowerCamelCase_						)

     def 		a__    (						self:    str						)->     Optional[int]:
           lowerCamelCase       :       Optional[int]   =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase_						)

     def 		a__    (						self:    int						)->     List[Any]:
           lowerCamelCase       :       Tuple   =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*lowerCamelCase_						)

     def 		a__    (						self:    Optional[int]						)->     str:



           def check_hidden_states_output(__a:    List[Any]  ,		__a:    Tuple  ,		__a:    List[Any]						):
                 lowerCamelCase       :       Optional[int]   =						model_class(lowerCamelCase_						)
                 model.to(lowerCamelCase_						)
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase       :       int   =						model(**self._prepare_for_class(lowerCamelCase_  ,		lowerCamelCase_						)						)

                 lowerCamelCase       :       int   =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 lowerCamelCase       :       Union[str, Any]   =						self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase_						)  ,		expected_num_stages + 1						)

                 # ConvNext'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]  ,		)

           lowerCamelCase       :       Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase       :       List[str]   =						True
                 check_hidden_states_output(lowerCamelCase_  ,		lowerCamelCase_  ,		lowerCamelCase_						)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase       :       List[str]   =						True

                 check_hidden_states_output(lowerCamelCase_  ,		lowerCamelCase_  ,		lowerCamelCase_						)

     def 		a__    (						self:    int						)->     Tuple:
           lowerCamelCase       :       Tuple   =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_						)







     @slow
     def 		a__    (						self:    Union[str, Any]						)->     Tuple:



           for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase       :       Optional[Any]   =						ConvNextModel.from_pretrained(lowerCamelCase_						)
                 self.assertIsNotNone(lowerCamelCase_						)




def 	snake_case		(  ) ->   Any:
      lowerCamelCase       :       Optional[Any]   =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image


@require_torch
@require_vision
class       A__						(    unittest.TestCase):


     """simple docstring"""

     @cached_property
     def 		a__    (						self:    str						)->     List[str]:
           return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""						) if is_vision_available() else None







     @slow
     def 		a__    (						self:    int						)->     Any:
           lowerCamelCase       :       Optional[int]   =						ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""						).to(lowerCamelCase_						)

           lowerCamelCase       :       Tuple   =						self.default_image_processor
           lowerCamelCase       :       List[str]   =						prepare_img()
           lowerCamelCase       :       List[str]   =						image_processor(images=lowerCamelCase_  ,		return_tensors="""pt"""						).to(lowerCamelCase_						)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase       :       str   =						model(**lowerCamelCase_						)

           # verify the logits
           lowerCamelCase       :       Union[str, Any]   =						torch.Size((1, 1_000)						)
           self.assertEqual(outputs.logits.shape  ,		lowerCamelCase_						)

           lowerCamelCase       :       List[Any]   =						torch.tensor([-0.02_60, -0.47_39, 0.19_11]						).to(lowerCamelCase_						)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,		lowerCamelCase_  ,		atol=1e-4						)						)


@require_torch
class       A__						(    unittest.TestCase			,  lowercase_):


     """simple docstring"""
     snake_case__						:       int				    =(ConvNextBackbone,) if is_torch_available() else ()
     snake_case__						:       Optional[int]				    =ConvNextConfig

     snake_case__						:       int				    =False

     def 		a__    (						self:    List[str]						)->     Union[str, Any]:
           lowerCamelCase       :       Tuple   =						ConvNextModelTester(self						)











'''simple docstring'''




import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase            =       """▁"""

__UpperCAmelCase            =       get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	BigBirdTokenizer
					SCREAMING_SNAKE_CASE__				=	BigBirdTokenizerFast
					SCREAMING_SNAKE_CASE__				=	True
					SCREAMING_SNAKE_CASE__				=	True


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							super().setUp()

							SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer_class(lowerCamelCase_      ,						keep_accents=lowerCamelCase_						)
							tokenizer.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""<s>"""
							SCREAMING_SNAKE_CASE :				str		=			1

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


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[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]      ,						"""[MASK]"""						)
							self.assertEqual(len(lowerCamelCase_						)      ,						10_04						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size      ,						10_00						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							if not self.test_rust_tokenizer:
									return

							SCREAMING_SNAKE_CASE :				List[Any]		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				str		=			self.get_rust_tokenizer()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			"""I was born in 92000, and this is falsé."""

							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer.tokenize(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			rust_tokenizer.tokenize(lowerCamelCase_						)
							self.assertListEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			tokenizer.encode(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			rust_tokenizer.encode(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
							self.assertListEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			tokenizer.encode(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			rust_tokenizer.encode(lowerCamelCase_						)
							self.assertListEqual(lowerCamelCase_      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			BigBirdTokenizer(lowerCamelCase_      ,						keep_accents=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			tokenizer.tokenize("""This is a test"""						)
							self.assertListEqual(lowerCamelCase_      ,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(lowerCamelCase_						)      ,						[2_85, 46, 10, 1_70, 3_82]      ,						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			tokenizer.tokenize("""I was born in 92000, and this is falsé."""						)
							self.assertListEqual(
							    lowerCamelCase_      ,						[
							        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""",
							        """.""",
							    ]      ,						)
							SCREAMING_SNAKE_CASE :				int		=			tokenizer.convert_tokens_to_ids(lowerCamelCase_						)
							self.assertListEqual(
							    lowerCamelCase_      ,						[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,						)

							SCREAMING_SNAKE_CASE :				int		=			tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
							self.assertListEqual(
							    lowerCamelCase_      ,						[
							        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>""",
							        """.""",
							    ]      ,						)


					@cached_property
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""						)


					@slow
					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			"""Hello World!"""
							SCREAMING_SNAKE_CASE :				Tuple		=			[65, 1_85_36, 22_60, 1_01, 66]

							self.assertListEqual(lowerCamelCase_      ,						self.big_tokenizer.encode(lowerCamelCase_						)						)


					@slow
					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			(
							    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
							    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
							)
							# fmt: off
							SCREAMING_SNAKE_CASE :				Dict		=			[65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
							# fmt: on
							self.assertListEqual(lowerCamelCase_      ,						self.big_tokenizer.encode(lowerCamelCase_						)						)


					@require_torch
					@slow
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							import torch

							from transformers import BigBirdConfig, BigBirdModel

							# Build sequence
							SCREAMING_SNAKE_CASE :				int		=			list(self.big_tokenizer.get_vocab().keys()						)[:10]
							SCREAMING_SNAKE_CASE :				List[str]		=			""" """.join(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.big_tokenizer.encode_plus(lowerCamelCase_      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			self.big_tokenizer.batch_encode_plus(
							    [sequence + """ """ + sequence]      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			BigBirdConfig(attention_type="""original_full"""						)
							SCREAMING_SNAKE_CASE :				Tuple		=			BigBirdModel(lowerCamelCase_						)

							assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

							with torch.no_grad():
									model(**lowerCamelCase_						)
									model(**lowerCamelCase_						)


					@slow
					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			tokenizer.decode(tokenizer("""Paris is the [MASK]."""						).input_ids						)

							self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""						)



					@slow
					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			{"""input_ids""": [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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], [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]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=lowerCamelCase_      ,						model_name="""google/bigbird-roberta-base"""      ,						revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""      ,						)



