from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase=13 , __lowercase=30 , __lowercase=2 , __lowercase=3 , __lowercase=True , __lowercase=True , __lowercase=32 , __lowercase=2 , __lowercase=4 , __lowercase=37 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=10 , __lowercase=0.02 , __lowercase=3 , __lowercase=0.6 , __lowercase=None , )       ->	Tuple:
        __UpperCamelCase     :List[str]			  =		parent
        __UpperCamelCase     :List[Any]			  =		batch_size
        __UpperCamelCase     :str			  =		image_size
        __UpperCamelCase     :List[Any]			  =		patch_size
        __UpperCamelCase     :List[str]			  =		num_channels
        __UpperCamelCase     :Union[str, Any]			  =		is_training
        __UpperCamelCase     :List[str]			  =		use_labels
        __UpperCamelCase     :Tuple			  =		hidden_size
        __UpperCamelCase     :str			  =		num_hidden_layers
        __UpperCamelCase     :List[Any]			  =		num_attention_heads
        __UpperCamelCase     :Optional[Any]			  =		intermediate_size
        __UpperCamelCase     :List[str]			  =		hidden_act
        __UpperCamelCase     :str			  =		hidden_dropout_prob
        __UpperCamelCase     :List[str]			  =		attention_probs_dropout_prob
        __UpperCamelCase     :Union[str, Any]			  =		type_sequence_label_size
        __UpperCamelCase     :List[str]			  =		initializer_range
        __UpperCamelCase     :Optional[int]			  =		mask_ratio
        __UpperCamelCase     :Optional[int]			  =		scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        __UpperCamelCase     :Optional[Any]			  =		(image_size // patch_size) ** 2
        __UpperCamelCase     :Any			  =		int(math.ceil((1 - mask_ratio) * (num_patches + 1)))







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Optional[Any]			  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        __UpperCamelCase     :Tuple			  =		None
        if self.use_labels:
            __UpperCamelCase     :str			  =		ids_tensor([self.batch_size] , self.type_sequence_label_size)

        __UpperCamelCase     :List[Any]			  =		self.get_config()

        return config, pixel_values, labels







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        return ViTMAEConfig(
            image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__lowercase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	Any:
        __UpperCamelCase     :Any			  =		TFViTMAEModel(config=__lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		model(__lowercase , training=__lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[str]:
        __UpperCamelCase     :str			  =		TFViTMAEForPreTraining(__lowercase)
        __UpperCamelCase     :str			  =		model(__lowercase , training=__lowercase)
        # expected sequence length = num_patches
        __UpperCamelCase     :List[str]			  =		(self.image_size // self.patch_size) ** 2
        __UpperCamelCase     :Union[str, Any]			  =		self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))

        # test greyscale images
        __UpperCamelCase     :List[str]			  =		1
        __UpperCamelCase     :List[str]			  =		TFViTMAEForPreTraining(__lowercase)

        __UpperCamelCase     :int			  =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        __UpperCamelCase     :Dict			  =		model(__lowercase , training=__lowercase)
        __UpperCamelCase     :List[str]			  =		self.patch_size**2
        self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[int]			  =		self.prepare_config_and_inputs()
        ((__UpperCamelCase)  ,       (__UpperCamelCase)  ,       (__UpperCamelCase))     :List[str]			  =		config_and_inputs
        __UpperCamelCase     :Dict			  =		{'''pixel_values''': pixel_values}
        return config, inputs_dict


@require_tf
class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						str						=      (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    a__	:						Dict						=      {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

    a__	:						Tuple						=      False
    a__	:						str						=      False
    a__	:						Optional[Any]						=      False
    a__	:						Union[str, Any]						=      False







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :List[str]			  =		TFViTMAEModelTester(self)
        __UpperCamelCase     :List[str]			  =		ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37)







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        self.config_tester.run_common_tests()







    @unittest.skip(reason='''ViTMAE does not use inputs_embeds''')
    def 			UpperCamelCase__   (	self)       ->	str:
        pass







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :List[Any]			  =		model_class(__lowercase)
            self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer))
            __UpperCamelCase     :Optional[Any]			  =		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__lowercase , tf.keras.layers.Layer))







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCamelCase     :Any			  =		['''pixel_values''']
            self.assertListEqual(arg_names[:1] , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :List[str]			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :str			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        # make the mask reproducible
        np.random.seed(2)

        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCamelCase     :Tuple			  =		int((config.image_size // config.patch_size) ** 2)
        __UpperCamelCase     :Optional[int]			  =		np.random.uniform(size=(self.model_tester.batch_size, num_patches))

        for model_class in self.all_model_classes:
            __UpperCamelCase     :str			  =		model_class(__lowercase)
            __UpperCamelCase     :Optional[int]			  =		self._prepare_for_class(__lowercase , __lowercase)

            __UpperCamelCase     :Dict			  =		model(__lowercase , noise=__lowercase)

            __UpperCamelCase     :int			  =		copy.deepcopy(self._prepare_for_class(__lowercase , __lowercase))
            __UpperCamelCase     :Union[str, Any]			  =		model(**__lowercase , noise=__lowercase)
            __UpperCamelCase     :Tuple			  =		outputs_dict[0].numpy()
            __UpperCamelCase     :Union[str, Any]			  =		outputs_keywords[0].numpy()

            self.assertLess(np.sum(np.abs(output_dict - output_keywords)) , 1E-6)







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        # make the mask reproducible
        np.random.seed(2)

        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCamelCase     :int			  =		int((config.image_size // config.patch_size) ** 2)
        __UpperCamelCase     :str			  =		np.random.uniform(size=(self.model_tester.batch_size, num_patches))

        def prepare_numpy_arrays(__lowercase):
            __UpperCamelCase     :Optional[int]			  =		{}
            for k, v in inputs_dict.items():
                if tf.is_tensor(__lowercase):
                    __UpperCamelCase     :Optional[Any]			  =		v.numpy()
                else:
                    __UpperCamelCase     :Optional[int]			  =		np.array(__lowercase)

            return inputs_np_dict

        for model_class in self.all_model_classes:
            __UpperCamelCase     :int			  =		model_class(__lowercase)

            __UpperCamelCase     :Tuple			  =		self._prepare_for_class(__lowercase , __lowercase)
            __UpperCamelCase     :Any			  =		prepare_numpy_arrays(__lowercase)

            __UpperCamelCase     :Any			  =		model(__lowercase , noise=__lowercase)
            __UpperCamelCase     :Tuple			  =		model(**__lowercase , noise=__lowercase)
            self.assert_outputs_same(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[Any]:
        # make masks reproducible
        np.random.seed(2)

        __UpperCamelCase     :Any			  =		int((tf_model.config.image_size // tf_model.config.patch_size) ** 2)
        __UpperCamelCase     :Optional[Any]			  =		np.random.uniform(size=(self.model_tester.batch_size, num_patches))
        __UpperCamelCase     :Dict			  =		tf.constant(__lowercase)

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        __UpperCamelCase     :Any			  =		tf_noise

        super().check_pt_tf_models(__lowercase , __lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        # make mask reproducible
        np.random.seed(2)

        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCamelCase     :Optional[int]			  =		{
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__),)
            for module_member_name in dir(__lowercase)
            if module_member_name.endswith('''MainLayer''')
            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
            and module_member_name[: -len('''MainLayer''')] == model_class.__name__[: -len('''Model''')]
            for module_member in (getattr(__lowercase , __lowercase),)
            if isinstance(__lowercase , __lowercase)
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(__lowercase , '''_keras_serializable''' , __lowercase)
        }

        __UpperCamelCase     :Union[str, Any]			  =		int((config.image_size // config.patch_size) ** 2)
        __UpperCamelCase     :List[str]			  =		np.random.uniform(size=(self.model_tester.batch_size, num_patches))
        __UpperCamelCase     :str			  =		tf.convert_to_tensor(__lowercase)
        inputs_dict.update({'''noise''': noise})

        for main_layer_class in tf_main_layer_classes:
            __UpperCamelCase     :Optional[int]			  =		main_layer_class(__lowercase)

            __UpperCamelCase     :Optional[Any]			  =		{
                name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype) for name, tensor in inputs_dict.items()
            }

            __UpperCamelCase     :Dict			  =		tf.keras.Model(__lowercase , outputs=main_layer(__lowercase))
            __UpperCamelCase     :str			  =		model(__lowercase)

            with tempfile.TemporaryDirectory() as tmpdirname:
                __UpperCamelCase     :str			  =		os.path.join(__lowercase , '''keras_model.h5''')
                model.save(__lowercase)
                __UpperCamelCase     :List[Any]			  =		tf.keras.models.load_model(
                    __lowercase , custom_objects={main_layer_class.__name__: main_layer_class})
                assert isinstance(__lowercase , tf.keras.Model)
                __UpperCamelCase     :Optional[Any]			  =		model(__lowercase)
                self.assert_outputs_same(__lowercase , __lowercase)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Dict:
        # make mask reproducible
        np.random.seed(2)

        __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCamelCase     :Optional[Any]			  =		int((config.image_size // config.patch_size) ** 2)
        __UpperCamelCase     :Any			  =		np.random.uniform(size=(self.model_tester.batch_size, num_patches))

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Optional[int]			  =		model_class(__lowercase)
            __UpperCamelCase     :Union[str, Any]			  =		self._prepare_for_class(__lowercase , __lowercase)
            __UpperCamelCase     :Optional[int]			  =		model(__lowercase , noise=__lowercase)

            if model_class.__name__ == "TFViTMAEModel":
                __UpperCamelCase     :Any			  =		outputs.last_hidden_state.numpy()
                __UpperCamelCase     :Optional[Any]			  =		0
            else:
                __UpperCamelCase     :List[str]			  =		outputs.logits.numpy()
                __UpperCamelCase     :Optional[int]			  =		0

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(__lowercase , saved_model=__lowercase)
                __UpperCamelCase     :Optional[int]			  =		model_class.from_pretrained(__lowercase)
                __UpperCamelCase     :List[str]			  =		model(__lowercase , noise=__lowercase)

                if model_class.__name__ == "TFViTMAEModel":
                    __UpperCamelCase     :List[Any]			  =		after_outputs['''last_hidden_state'''].numpy()
                    __UpperCamelCase     :List[Any]			  =		0
                else:
                    __UpperCamelCase     :Any			  =		after_outputs['''logits'''].numpy()
                    __UpperCamelCase     :Tuple			  =		0

                __UpperCamelCase     :Any			  =		np.amax(np.abs(out_a - out_a))
                self.assertLessEqual(__lowercase , 1E-5)







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        # make mask reproducible
        np.random.seed(2)

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCamelCase     :str			  =		int((config.image_size // config.patch_size) ** 2)
        __UpperCamelCase     :Optional[int]			  =		np.random.uniform(size=(self.model_tester.batch_size, num_patches))

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Tuple			  =		model_class(__lowercase)
            __UpperCamelCase     :Any			  =		self._prepare_for_class(__lowercase , __lowercase)

            __UpperCamelCase     :Tuple			  =		model(__lowercase , noise=__lowercase)
            __UpperCamelCase     :List[Any]			  =		model.get_config()
            # make sure that returned config is jsonifiable, which is required by keras
            json.dumps(__lowercase)
            __UpperCamelCase     :Optional[Any]			  =		model_class.from_config(model.get_config())
            # make sure it also accepts a normal config
            __UpperCamelCase     :Any			  =		model_class.from_config(model.config)
            __UpperCamelCase     :List[Any]			  =		new_model(__lowercase)  # Build model
            new_model.set_weights(model.get_weights())
            __UpperCamelCase     :str			  =		new_model(__lowercase , noise=__lowercase)

            self.assert_outputs_same(__lowercase , __lowercase)







    @unittest.skip(
        reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.''')
    def 			UpperCamelCase__   (	self)       ->	Dict:
        pass







    @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''')
    def 			UpperCamelCase__   (	self)       ->	Any:
        pass







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :List[Any]			  =		TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''')
        self.assertIsNotNone(__lowercase)



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image


@require_tf
@require_vision
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    @cached_property
    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''') if is_vision_available() else None







    @slow
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        # make random mask reproducible across the PT and TF model
        np.random.seed(2)

        __UpperCamelCase     :Optional[Any]			  =		TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''')

        __UpperCamelCase     :Optional[int]			  =		self.default_image_processor
        __UpperCamelCase     :Optional[int]			  =		prepare_img()
        __UpperCamelCase     :Optional[int]			  =		image_processor(images=__lowercase , return_tensors='''tf''')

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        __UpperCamelCase     :Union[str, Any]			  =		ViTMAEConfig()
        __UpperCamelCase     :Union[str, Any]			  =		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
        __UpperCamelCase     :Tuple			  =		np.random.uniform(size=(1, num_patches))

        # forward pass
        __UpperCamelCase     :int			  =		model(**__lowercase , noise=__lowercase)

        # verify the logits
        __UpperCamelCase     :Optional[int]			  =		tf.convert_to_tensor([1, 196, 768])
        self.assertEqual(outputs.logits.shape , __lowercase)

        __UpperCamelCase     :List[Any]			  =		tf.convert_to_tensor(
            [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

        tf.debugging.assert_near(outputs.logits[0, :3, :3] , __lowercase , atol=1E-4)









"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_rembert import RemBertTokenizer
else:
  __snake_case   			=    None

__snake_case   			=    logging.get_logger(__name__)
__snake_case   			=    {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case   			=    {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

__snake_case   			=    {
    """google/rembert""": 256,
}

__snake_case   			=    """▁"""







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[str]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Union[str, Any]							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       str							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       str							  =  RemBertTokenizer







   def __init__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=False						,	UpperCamelCase__="[CLS]"						,	UpperCamelCase__="[SEP]"						,	UpperCamelCase__="<unk>"						,	UpperCamelCase__="[SEP]"						,	UpperCamelCase__="<pad>"						,	UpperCamelCase__="[CLS]"						,	UpperCamelCase__="[MASK]"						,	**UpperCamelCase__						,	)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Dict      					=    AddedToken(UpperCamelCase__						,	lstrip=UpperCamelCase__						,	rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__						,	UpperCamelCase__						) else mask_token

       super().__init__(
           UpperCamelCase__						,	tokenizer_file=UpperCamelCase__						,	do_lower_case=UpperCamelCase__						,	remove_space=UpperCamelCase__						,	keep_accents=UpperCamelCase__						,	bos_token=UpperCamelCase__						,	eos_token=UpperCamelCase__						,	unk_token=UpperCamelCase__						,	sep_token=UpperCamelCase__						,	pad_token=UpperCamelCase__						,	cls_token=UpperCamelCase__						,	mask_token=UpperCamelCase__						,	**UpperCamelCase__						,	)

       snake_case    :      int      					=    do_lower_case
       snake_case    :      Union[str, Any]      					=    remove_space
       snake_case    :      Optional[int]      					=    keep_accents
       snake_case    :      Any      					=    vocab_file
       snake_case    :      Any      					=    False if not self.vocab_file else True







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [self.cls_token_id]
       if token_ids_a is None:
           return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = False						)	->		List[int]:

       '''simple docstring'''




       if already_has_special_tokens:
           if token_ids_a is not None:
               raise ValueError(
                   "You should not supply a second sequence if the provided sequence of "
                   "ids is already formatted with special tokens for the model."						)
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
           return [1] + ([0] * len(UpperCamelCase__						)) + [1] + ([0] * len(UpperCamelCase__						)) + [1]
       return [1] + ([0] * len(UpperCamelCase__						)) + [1]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		Tuple[str]:

       '''simple docstring'''




       if not os.path.isdir(UpperCamelCase__						):
           logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__						)						)
           return
       snake_case    :      str      					=    os.path.join(
           UpperCamelCase__						,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCamelCase__						):
           copyfile(self.vocab_file						,	UpperCamelCase__						)

       return (out_vocab_file,)





