






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       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__=13      ,    a__=32      ,    a__=3      ,    a__=4      ,    a__=[10, 20, 30, 40]      ,    a__=[2, 2, 3, 2]      ,    a__=True      ,    a__=True      ,    a__=37      ,    a__="gelu"      ,    a__=10      ,    a__=0.02      ,    a__=["stage2", "stage3", "stage4"]      ,    a__=[2, 3, 4]      ,    a__=None      ,    )			->   int:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    parent
            __snake_case      :Dict 							=    batch_size
            __snake_case      :int 							=    image_size
            __snake_case      :Union[str, Any] 							=    num_channels
            __snake_case      :List[str] 							=    num_stages
            __snake_case      :str 							=    hidden_sizes
            __snake_case      :Dict 							=    depths
            __snake_case      :str 							=    is_training
            __snake_case      :Dict 							=    use_labels
            __snake_case      :Tuple 							=    intermediate_size
            __snake_case      :Union[str, Any] 							=    hidden_act
            __snake_case      :Dict 							=    num_labels
            __snake_case      :Union[str, Any] 							=    initializer_range
            __snake_case      :int 							=    out_features
            __snake_case      :Dict 							=    out_indices
            __snake_case      :str 							=    scope



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

            __snake_case      :Union[str, Any] 							=    None
            if self.use_labels:
                 __snake_case      :Any 							=    ids_tensor([self.batch_size]      ,    self.num_labels     )

            __snake_case      :int 							=    self.get_config()
            return config, pixel_values, labels



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            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=a__      ,    initializer_range=self.initializer_range      ,    out_features=self.out_features      ,    out_indices=self.out_indices      ,    num_labels=self.num_labels      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    ConvNextModel(config=a__     )
            model.to(a__     )
            model.eval()
            __snake_case      :Union[str, Any] 							=    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        __lowercase    (				self      ,    a__      ,    a__      ,    a__     )			->   Any:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    ConvNextForImageClassification(a__     )
            model.to(a__     )
            model.eval()
            __snake_case      :Optional[Any] 							=    model(a__      ,    labels=a__     )
            self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_labels)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__     )			->   int:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    ConvNextBackbone(config=a__     )
            model.to(a__     )
            model.eval()
            __snake_case      :List[str] 							=    model(a__     )

            # 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
            __snake_case      :Optional[int] 							=    None
            __snake_case      :Union[str, Any] 							=    ConvNextBackbone(config=a__     )
            model.to(a__     )
            model.eval()
            __snake_case      :List[str] 							=    model(a__     )

            # 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        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Dict 							=    self.prepare_config_and_inputs()
            __snake_case							,			__snake_case							,			__snake_case      :Union[str, Any] 							=    config_and_inputs
            __snake_case      :List[str] 							=    {"""pixel_values""": pixel_values}
            return config, inputs_dict




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

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

       lowerCamelCase				:					List[Any]								=			True
       lowerCamelCase				:					str								=			False
       lowerCamelCase				:					Optional[int]								=			False
       lowerCamelCase				:					Tuple								=			False
       lowerCamelCase				:					List[Any]								=			False



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    ConvNextModelTester(self     )
            __snake_case      :int 							=    ConfigTester(self      ,    config_class=a__      ,    has_text_modality=a__      ,    hidden_size=37     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            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        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            return



       @unittest.skip(reason="""ConvNext does not use inputs_embeds"""     )
       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            pass



       @unittest.skip(reason="""ConvNext does not support input and output embeddings"""     )
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            pass



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






            '''simple docstring'''





            pass



       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case							,			__snake_case      :int 							=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case      :int 							=    model_class(a__     )
                 __snake_case      :Optional[Any] 							=    inspect.signature(model.forward     )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __snake_case      :Tuple 							=    [*signature.parameters.keys()]

                 __snake_case      :Optional[int] 							=    ["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]      ,    a__     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Any 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a__     )



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Dict 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*a__     )



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            def check_hidden_states_output(a__      ,    a__      ,    a__     ):
                 __snake_case      :Union[str, Any] 							=    model_class(a__     )
                 model.to(a__     )
                 model.eval()

                 with torch.no_grad():
                      __snake_case      :Optional[Any] 							=    model(**self._prepare_for_class(a__      ,    a__     )     )

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

                 __snake_case      :int 							=    self.model_tester.num_stages
                 self.assertEqual(len(a__     )      ,    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]      ,    )

            __snake_case							,			__snake_case      :Tuple 							=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case      :Tuple 							=    True
                 check_hidden_states_output(a__      ,    a__      ,    a__     )

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

                 check_hidden_states_output(a__      ,    a__      ,    a__     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :int 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*a__     )



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






            '''simple docstring'''





            for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case      :Tuple 							=    ConvNextModel.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
     return image




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

       '''simple docstring'''



       @cached_property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""     ) if is_vision_available() else None



       @slow
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Any 							=    ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""     ).to(a__     )

            __snake_case      :Tuple 							=    self.default_image_processor
            __snake_case      :Optional[Any] 							=    prepare_img()
            __snake_case      :Dict 							=    image_processor(images=a__      ,    return_tensors="""pt"""     ).to(a__     )

            # forward pass
            with torch.no_grad():
                 __snake_case      :List[str] 							=    model(**a__     )

            # verify the logits
            __snake_case      :int 							=    torch.Size((1, 10_00)     )
            self.assertEqual(outputs.logits.shape      ,    a__     )

            __snake_case      :Dict 							=    torch.tensor([-0.02_60, -0.47_39, 0.19_11]     ).to(a__     )

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




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

       '''simple docstring'''
       lowerCamelCase				:					str								=			(ConvNextBackbone,) if is_torch_available() else ()
       lowerCamelCase				:					Any								=			ConvNextConfig

       lowerCamelCase				:					Tuple								=			False



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    ConvNextModelTester(self     )










import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


lowerCamelCase__       		=  get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")




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

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			PegasusTokenizer
       lowerCamelCase				:					Optional[int]								=			PegasusTokenizerFast
       lowerCamelCase				:					int								=			True
       lowerCamelCase				:					List[Any]								=			True



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            super().setUp()

            # We have a SentencePiece fixture for testing
            __snake_case      :Optional[int] 							=    PegasusTokenizer(a__     )
            tokenizer.save_pretrained(self.tmpdirname     )



       @cached_property
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained("""google/pegasus-large"""     )



       def        __lowercase    (				self      ,    **a__     )			->   PegasusTokenizer:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



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






            '''simple docstring'''





            return ("This is a test", "This is a test")



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Dict 							=    """</s>"""
            __snake_case      :Union[str, Any] 							=    1

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



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Any 							=    list(self.get_tokenizer().get_vocab().keys()     )

            self.assertEqual(vocab_keys[0]      ,    """<pad>"""     )
            self.assertEqual(vocab_keys[1]      ,    """</s>"""     )
            self.assertEqual(vocab_keys[-1]      ,    """v"""     )
            self.assertEqual(len(a__     )      ,    11_03     )



       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            self.assertEqual(self.get_tokenizer().vocab_size      ,    11_03     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Tuple 							=    self.tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :List[Any] 							=    (
                """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
                """ </s> <pad> <pad> <pad>"""
            )
            __snake_case      :Tuple 							=    rust_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            __snake_case      :Union[str, Any] 							=    py_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self._large_tokenizer
            # <mask_1> masks whole sentence while <mask_2> masks single word
            __snake_case      :Any 							=    """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
            __snake_case      :List[Any] 							=    [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
            __snake_case      :Tuple 							=    tokenizer([raw_input_str]      ,    return_tensors=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self._large_tokenizer
            # The tracebacks for the following asserts are **better** without messages or self.assertEqual
            assert tokenizer.vocab_size == 9_61_03
            assert tokenizer.pad_token_id == 0
            assert tokenizer.eos_token_id == 1
            assert tokenizer.offset == 1_03
            assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
            assert tokenizer.unk_token == "<unk>"
            assert tokenizer.model_max_length == 10_24
            __snake_case      :Any 							=    """To ensure a smooth flow of bank resolutions."""
            __snake_case      :int 							=    [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
            __snake_case      :Any 							=    tokenizer([raw_input_str]      ,    return_tensors=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )
            assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]     ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



       @require_torch
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :str 							=    ["""This is going to be way too long.""" * 1_50, """short example"""]
            __snake_case      :int 							=    ["""not super long but more than 5 tokens""", """tiny"""]
            __snake_case      :str 							=    self._large_tokenizer(a__      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )
            __snake_case      :Optional[int] 							=    self._large_tokenizer(
                text_target=a__      ,    max_length=5      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )

            assert batch.input_ids.shape == (2, 10_24)
            assert batch.attention_mask.shape == (2, 10_24)
            assert targets["input_ids"].shape == (2, 5)
            assert len(a__     ) == 2  # input_ids, attention_mask.



       @slow
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    {"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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="""google/bigbird-pegasus-large-arxiv"""      ,    revision="""ba85d0851d708441f91440d509690f1ab6353415"""      ,    )




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

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			PegasusTokenizer
       lowerCamelCase				:					List[Any]								=			PegasusTokenizerFast
       lowerCamelCase				:					List[str]								=			True
       lowerCamelCase				:					List[Any]								=			True



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            super().setUp()

            # We have a SentencePiece fixture for testing
            __snake_case      :Optional[int] 							=    PegasusTokenizer(a__      ,    offset=0      ,    mask_token_sent=a__      ,    mask_token="""[MASK]"""     )
            tokenizer.save_pretrained(self.tmpdirname     )



       @cached_property
       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""     )



       def        __lowercase    (				self      ,    **a__     )			->   PegasusTokenizer:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return ("This is a test", "This is a test")



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Optional[int] 							=    self.tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Union[str, Any] 							=    (
                """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
                """ <pad> <pad> <pad>"""
            )
            __snake_case      :Dict 							=    rust_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            __snake_case      :List[Any] 							=    py_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       @require_torch
       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :Any 							=    ["""This is going to be way too long.""" * 10_00, """short example"""]
            __snake_case      :str 							=    ["""not super long but more than 5 tokens""", """tiny"""]
            __snake_case      :Optional[int] 							=    self._large_tokenizer(a__      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )
            __snake_case      :Union[str, Any] 							=    self._large_tokenizer(
                text_target=a__      ,    max_length=5      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )

            assert batch.input_ids.shape == (2, 40_96)
            assert batch.attention_mask.shape == (2, 40_96)
            assert targets["input_ids"].shape == (2, 5)
            assert len(a__     ) == 2  # input_ids, attention_mask.



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Tuple 							=    (
                """This is an example string that is used to test the original TF implementation against the HF"""
                """ implementation"""
            )

            __snake_case      :Dict 							=    self._large_tokenizer(a__     ).input_ids

            self.assertListEqual(
                a__      ,    [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]      ,    )


