import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      lowerCamelCase_					(       _UpperCAmelCase						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Dict      =				FunnelTokenizer
    a__     :						Union[str, Any]      =				FunnelTokenizerFast
    a__     :						Union[str, Any]      =				True
    a__     :						Tuple      =				True







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        super().setUp()

        __UpperCamelCase					:Optional[Any]        =		[
            '''<unk>''',
            '''<cls>''',
            '''<sep>''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        __UpperCamelCase					:int        =		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file'''])
        with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8''') as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     str:
        return FunnelTokenizer.from_pretrained(self.tmpdirname			,			**_UpperCAmelCase)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Tuple:
        return FunnelTokenizerFast.from_pretrained(self.tmpdirname			,			**_UpperCAmelCase)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Tuple:
        __UpperCamelCase					:Tuple        =		'''UNwant\u00E9d,running'''
        __UpperCamelCase					:Optional[int]        =		'''unwanted, running'''
        return input_text, output_text







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:List[str]        =		self.tokenizer_class(self.vocab_file)

        __UpperCamelCase					:Tuple        =		tokenizer.tokenize('''UNwant\u00E9d,running''')
        self.assertListEqual(_UpperCAmelCase			,			['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''])
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase)			,			[7, 4, 5, 10, 8, 9])







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Union[str, Any]        =		self.get_tokenizers(do_lower_case=_UpperCAmelCase)
        for tokenizer in tokenizers:
            __UpperCamelCase					:int        =		tokenizer('''UNwant\u00E9d,running''')
            __UpperCamelCase					:Union[str, Any]        =		len(inputs['''input_ids''']) - 1
            self.assertListEqual(inputs['''token_type_ids''']			,			[2] + [0] * sentence_len)

            __UpperCamelCase					:Union[str, Any]        =		tokenizer('''UNwant\u00E9d,running'''			,			'''UNwant\u00E9d,running''')
            self.assertListEqual(inputs['''token_type_ids''']			,			[2] + [0] * sentence_len + [1] * sentence_len)






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__)





