
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 				lowercase		:




							def __init__(				self   ,A__   ,A__=1_3   ,A__=3_0   ,A__=2   ,A__=3   ,A__=True   ,A__=True   ,A__=3_2   ,A__=2   ,A__=4   ,A__=3_7   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=1_0   ,A__=0.02   ,A__=3   ,A__=0.6   ,A__=None   ,):
												lowercase							=						parent
												lowercase							=						batch_size
												lowercase							=						image_size
												lowercase							=						patch_size
												lowercase							=						num_channels
												lowercase							=						is_training
												lowercase							=						use_labels
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						intermediate_size
												lowercase							=						hidden_act
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						type_sequence_label_size
												lowercase							=						initializer_range
												lowercase							=						mask_ratio
												lowercase							=						scope

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




							def  A__      (				self):
												lowercase							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

												lowercase							=						None
												if self.use_labels:
																	lowercase							=						ids_tensor([self.batch_size]   ,self.type_sequence_label_size)

												lowercase							=						self.get_config()

												return config, pixel_values, labels




							def  A__      (				self):
												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=A__   ,initializer_range=self.initializer_range   ,mask_ratio=self.mask_ratio   ,)




							def  A__      (				self   ,A__   ,A__   ,A__):
												lowercase							=						TFViTMAEModel(config=A__)
												lowercase							=						model(A__   ,training=A__)
												self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size))




							def  A__      (				self   ,A__   ,A__   ,A__):
												lowercase							=						TFViTMAEForPreTraining(A__)
												lowercase							=						model(A__   ,training=A__)
												# expected sequence length = num_patches
												lowercase							=						(self.image_size // self.patch_size) ** 2
												lowercase							=						self.patch_size**2 * self.num_channels
												self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels))

												# test greyscale images
												lowercase							=						1
												lowercase							=						TFViTMAEForPreTraining(A__)

												lowercase							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
												lowercase							=						model(A__   ,training=A__)
												lowercase							=						self.patch_size**2
												self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels))





							def  A__      (				self):
												lowercase							=						self.prepare_config_and_inputs()
												((lowercase)	,						(lowercase)	,						(lowercase))							=						config_and_inputs
												lowercase							=						{'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_tf
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Any       					=(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							lowercase_       :       str       					={'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

							lowercase_       :       Tuple       					=False
							lowercase_       :       Optional[int]       					=False
							lowercase_       :       Dict       					=False
							lowercase_       :       Tuple       					=False




							def  A__      (				self):
												lowercase							=						TFViTMAEModelTester(self)
												lowercase							=						ConfigTester(self   ,config_class=A__   ,has_text_modality=A__   ,hidden_size=3_7)




							def  A__      (				self):
												self.config_tester.run_common_tests()




							@unittest.skip(reason='''ViTMAE does not use inputs_embeds''')
							def  A__      (				self):
												pass




							def  A__      (				self):
												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	lowercase							=						model_class(A__)
																	self.assertIsInstance(model.get_input_embeddings()   ,(tf.keras.layers.Layer))
																	lowercase							=						model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(A__   ,tf.keras.layers.Layer))




							def  A__      (				self):
												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase							=						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]   ,A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*A__)




							def  A__      (				self):
												# make the mask reproducible
												np.random.seed(2)

												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																	lowercase							=						model_class(A__)
																	lowercase							=						self._prepare_for_class(A__   ,A__)

																	lowercase							=						model(A__   ,noise=A__)

																	lowercase							=						copy.deepcopy(self._prepare_for_class(A__   ,A__))
																	lowercase							=						model(**A__   ,noise=A__)
																	lowercase							=						outputs_dict[0].numpy()
																	lowercase							=						outputs_keywords[0].numpy()

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




							def  A__      (				self):
												# make the mask reproducible
												np.random.seed(2)

												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

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

												def prepare_numpy_arrays(A__):
																	lowercase							=						{}
																	for k, v in inputs_dict.items():
																						if tf.is_tensor(A__):
																											lowercase							=						v.numpy()
																						else:
																											lowercase							=						np.array(A__)

																	return inputs_np_dict

												for model_class in self.all_model_classes:
																	lowercase							=						model_class(A__)

																	lowercase							=						self._prepare_for_class(A__   ,A__)
																	lowercase							=						prepare_numpy_arrays(A__)

																	lowercase							=						model(A__   ,noise=A__)
																	lowercase							=						model(**A__   ,noise=A__)
																	self.assert_outputs_same(A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__):
												# make masks reproducible
												np.random.seed(2)

												lowercase							=						int((tf_model.config.image_size // tf_model.config.patch_size) ** 2)
												lowercase							=						np.random.uniform(size=(self.model_tester.batch_size, num_patches))
												lowercase							=						tf.constant(A__)

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

												super().check_pt_tf_models(A__   ,A__   ,A__)




							def  A__      (				self):
												# make mask reproducible
												np.random.seed(2)

												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

												lowercase							=						{
												    module_member
												    for model_class in self.all_model_classes
												    for module in (import_module(model_class.__module__),)
												    for module_member_name in dir(A__)
												    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(A__   ,A__),)
												    if isinstance(A__   ,A__)
												    and tf.keras.layers.Layer in module_member.__bases__
												    and getattr(A__   ,'''_keras_serializable'''   ,A__)
												}

												lowercase							=						int((config.image_size // config.patch_size) ** 2)
												lowercase							=						np.random.uniform(size=(self.model_tester.batch_size, num_patches))
												lowercase							=						tf.convert_to_tensor(A__)
												inputs_dict.update({'''noise''': noise})

												for main_layer_class in tf_main_layer_classes:
																	lowercase							=						main_layer_class(A__)

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

																	lowercase							=						tf.keras.Model(A__   ,outputs=main_layer(A__))
																	lowercase							=						model(A__)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						lowercase							=						os.path.join(A__   ,'''keras_model.h5''')
																						model.save(A__)
																						lowercase							=						tf.keras.models.load_model(
																						    A__   ,custom_objects={main_layer_class.__name__: main_layer_class})
																						assert isinstance(A__   ,tf.keras.Model)
																						lowercase							=						model(A__)
																						self.assert_outputs_same(A__   ,A__)




							@slow
							def  A__      (				self):
												# make mask reproducible
												np.random.seed(2)

												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																	lowercase							=						model_class(A__)
																	lowercase							=						self._prepare_for_class(A__   ,A__)
																	lowercase							=						model(A__   ,noise=A__)

																	if model_class.__name__ == "TFViTMAEModel":
																						lowercase							=						outputs.last_hidden_state.numpy()
																						lowercase							=						0
																	else:
																						lowercase							=						outputs.logits.numpy()
																						lowercase							=						0

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						model.save_pretrained(A__   ,saved_model=A__)
																						lowercase							=						model_class.from_pretrained(A__)
																						lowercase							=						model(A__   ,noise=A__)

																						if model_class.__name__ == "TFViTMAEModel":
																											lowercase							=						after_outputs['''last_hidden_state'''].numpy()
																											lowercase							=						0
																						else:
																											lowercase							=						after_outputs['''logits'''].numpy()
																											lowercase							=						0

																						lowercase							=						np.amax(np.abs(out_a - out_a))
																						self.assertLessEqual(A__   ,1E-5)




							def  A__      (				self):
												# make mask reproducible
												np.random.seed(2)

												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																	lowercase							=						model_class(A__)
																	lowercase							=						self._prepare_for_class(A__   ,A__)

																	lowercase							=						model(A__   ,noise=A__)
																	lowercase							=						model.get_config()
																	# make sure that returned config is jsonifiable, which is required by keras
																	json.dumps(A__)
																	lowercase							=						model_class.from_config(model.get_config())
																	# make sure it also accepts a normal config
																	lowercase							=						model_class.from_config(model.config)
																	lowercase							=						new_model(A__)  # Build model
																	new_model.set_weights(model.get_weights())
																	lowercase							=						new_model(A__   ,noise=A__)

																	self.assert_outputs_same(A__   ,A__)




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




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





							@slow
							def  A__      (				self):
												lowercase							=						TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''')
												self.assertIsNotNone(A__)



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image







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




							@cached_property
							def  A__      (				self):
												return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''') if is_vision_available() else None





							@slow
							def  A__      (				self):
												# make random mask reproducible across the PT and TF model
												np.random.seed(2)

												lowercase							=						TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''')

												lowercase							=						self.default_image_processor
												lowercase							=						prepare_img()
												lowercase							=						image_processor(images=A__   ,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)
												lowercase							=						ViTMAEConfig()
												lowercase							=						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
												lowercase							=						np.random.uniform(size=(1, num_patches))

												# forward pass
												lowercase							=						model(**A__   ,noise=A__)

												# verify the logits
												lowercase							=						tf.convert_to_tensor([1, 1_9_6, 7_6_8])
												self.assertEqual(outputs.logits.shape   ,A__)

												lowercase							=						tf.convert_to_tensor(
												    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]])

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






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


lowercase__		:List[Any]   =    logging.get_logger(__name__)

lowercase__		:Optional[Any]   =    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase__		:Union[str, Any]   =    {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__		:str   =    {
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

lowercase__		:Dict   =    {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       str       					=VOCAB_FILES_NAMES
							lowercase_       :       Dict       					=PRETRAINED_VOCAB_FILES_MAP
							lowercase_       :       Optional[int]       					=PRETRAINED_INIT_CONFIGURATION
							lowercase_       :       Tuple       					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_       :       Any       					=BertTokenizer




							def __init__(				self   ,A__=None   ,A__=None   ,A__=True   ,A__="[UNK]"   ,A__="[SEP]"   ,A__="[PAD]"   ,A__="[CLS]"   ,A__="[MASK]"   ,A__=True   ,A__=None   ,**A__   ,):
												super().__init__(
												    A__   ,tokenizer_file=A__   ,do_lower_case=A__   ,unk_token=A__   ,sep_token=A__   ,pad_token=A__   ,cls_token=A__   ,mask_token=A__   ,tokenize_chinese_chars=A__   ,strip_accents=A__   ,**A__   ,)

												lowercase							=						json.loads(self.backend_tokenizer.normalizer.__getstate__())
												if (
												    normalizer_state.get('''lowercase'''   ,A__) != do_lower_case
												    or normalizer_state.get('''strip_accents'''   ,A__) != strip_accents
												    or normalizer_state.get('''handle_chinese_chars'''   ,A__) != tokenize_chinese_chars
												):
																	lowercase							=						getattr(A__   ,normalizer_state.pop('''type'''))
																	lowercase							=						do_lower_case
																	lowercase							=						strip_accents
																	lowercase							=						tokenize_chinese_chars
																	lowercase							=						normalizer_class(**A__)

												lowercase							=						do_lower_case




							def  A__      (				self   ,A__   ,A__=None):
												lowercase							=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

												return output




							def  A__      (				self   ,A__   ,A__ = None):
												lowercase							=						[self.sep_token_id]
												lowercase							=						[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  A__      (				self   ,A__   ,A__ = None):
												lowercase							=						self._tokenizer.model.save(A__   ,name=A__)
												return tuple(A__)




