import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Any      =				DebertaTokenizer
    a__     :						List[str]      =				True
    a__     :						Optional[Any]      =				DebertaTokenizerFast







    def       UpperCamelCase__       (     self)	->     Tuple:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        __UpperCamelCase					:Optional[int]        =		[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''[UNK]''',
        ]
        __UpperCamelCase					:List[str]        =		dict(zip(__lowercase			,			range(len(__lowercase))))
        __UpperCamelCase					:Union[str, Any]        =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        __UpperCamelCase					:Dict        =		{'''unk_token''': '''[UNK]'''}

        __UpperCamelCase					:Any        =		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file'''])
        __UpperCamelCase					:Union[str, Any]        =		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file'''])
        with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8''') as fp:
            fp.write(json.dumps(__lowercase) + '''\n''')
        with open(self.merges_file			,			'''w'''			,			encoding='''utf-8''') as fp:
            fp.write('''\n'''.join(__lowercase))







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     List[Any]:
        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Any:
        __UpperCamelCase					:List[Any]        =		'''lower newer'''
        __UpperCamelCase					:Union[str, Any]        =		'''lower newer'''
        return input_text, output_text







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:int        =		self.get_tokenizer()
        __UpperCamelCase					:Any        =		'''lower newer'''
        __UpperCamelCase					:Optional[int]        =		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        __UpperCamelCase					:Optional[int]        =		tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:str        =		tokens + [tokenizer.unk_token]
        __UpperCamelCase					:Dict        =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase)			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:int        =		self.get_tokenizer()
        __UpperCamelCase					:Union[str, Any]        =		tokenizer('''Hello'''			,			'''World''')
        __UpperCamelCase					:Optional[Any]        =		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['''token_type_ids''']			,			__lowercase)







    @slow
    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:int        =		self.tokenizer_class.from_pretrained('''microsoft/deberta-base''')

        __UpperCamelCase					:Dict        =		tokenizer.encode('''sequence builders'''			,			add_special_tokens=__lowercase)
        __UpperCamelCase					:List[Any]        =		tokenizer.encode('''multi-sequence build'''			,			add_special_tokens=__lowercase)

        __UpperCamelCase					:Tuple        =		tokenizer.encode(
            '''sequence builders'''			,			add_special_tokens=__lowercase			,			add_prefix_space=__lowercase)
        __UpperCamelCase					:Optional[Any]        =		tokenizer.encode(
            '''sequence builders'''			,			'''multi-sequence build'''			,			add_special_tokens=__lowercase			,			add_prefix_space=__lowercase)

        __UpperCamelCase					:Optional[int]        =		tokenizer.build_inputs_with_special_tokens(__lowercase)
        __UpperCamelCase					:Dict        =		tokenizer.build_inputs_with_special_tokens(__lowercase			,			__lowercase)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode







    @slow
    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[str]        =		[self.tokenizer_class]
        if self.test_rust_tokenizer:
            tokenizer_classes.append(self.rust_tokenizer_class)

        for tokenizer_class in tokenizer_classes:
            __UpperCamelCase					:str        =		tokenizer_class.from_pretrained('''microsoft/deberta-base''')

            __UpperCamelCase					:Optional[int]        =		[
                '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                '''ALBERT incorporates two parameter reduction techniques''',
                '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                ''' vocabulary embedding.''',
            ]

            __UpperCamelCase					:int        =		tokenizer(__lowercase			,			padding=__lowercase)
            __UpperCamelCase					:List[Any]        =		[tokenizer.decode(__lowercase			,			skip_special_tokens=__lowercase) for seq in encoding['''input_ids''']]

            # fmt: off
            __UpperCamelCase					:Tuple        =		{
                '''input_ids''': [
                    [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
                ],
                '''token_type_ids''': [
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                ],
                '''attention_mask''': [
                    [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],
                    [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],
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                ]
            }
            # fmt: on

            __UpperCamelCase					:Optional[Any]        =		[
                '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                '''ALBERT incorporates two parameter reduction techniques''',
                '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                ''' vocabulary embedding.''',
            ]

            self.assertDictEqual(encoding.data			,			__lowercase)

            for expected, decoded in zip(__lowercase			,			__lowercase):
                self.assertEqual(__lowercase			,			__lowercase)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__lowercase										= {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3TokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LayoutLMv3ForQuestionAnswering''',
        '''LayoutLMv3ForSequenceClassification''',
        '''LayoutLMv3ForTokenClassification''',
        '''LayoutLMv3Model''',
        '''LayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFLayoutLMv3ForQuestionAnswering''',
        '''TFLayoutLMv3ForSequenceClassification''',
        '''TFLayoutLMv3ForTokenClassification''',
        '''TFLayoutLMv3Model''',
        '''TFLayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3FeatureExtractor''']
    __lowercase										= ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
    from .configuration_layoutlmva import (
        LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LayoutLMvaConfig,
        LayoutLMvaOnnxConfig,
    )
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlmva import (
            TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
            TFLayoutLMvaModel,
            TFLayoutLMvaPreTrainedModel,
        )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
        from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
    import sys

    __lowercase										= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





