from typing import TYPE_CHECKING

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


__lowerCamelCase :     Optional[int]          =  {
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase :     Optional[int]          =  ['''LayoutLMTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase :     Optional[Any]          =  [
		    '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMForMaskedLM''',
		    '''LayoutLMForSequenceClassification''',
		    '''LayoutLMForTokenClassification''',
		    '''LayoutLMForQuestionAnswering''',
		    '''LayoutLMModel''',
		    '''LayoutLMPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase :     Any          =  [
		    '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFLayoutLMForMaskedLM''',
		    '''TFLayoutLMForSequenceClassification''',
		    '''TFLayoutLMForTokenClassification''',
		    '''TFLayoutLMForQuestionAnswering''',
		    '''TFLayoutLMMainLayer''',
		    '''TFLayoutLMModel''',
		    '''TFLayoutLMPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
		from .tokenization_layoutlm import LayoutLMTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlm import (
				    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMForMaskedLM,
				    LayoutLMForQuestionAnswering,
				    LayoutLMForSequenceClassification,
				    LayoutLMForTokenClassification,
				    LayoutLMModel,
				    LayoutLMPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlm import (
				    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMForMaskedLM,
				    TFLayoutLMForQuestionAnswering,
				    TFLayoutLMForSequenceClassification,
				    TFLayoutLMForTokenClassification,
				    TFLayoutLMMainLayer,
				    TFLayoutLMModel,
				    TFLayoutLMPreTrainedModel,
				)

else:
		import sys

		__lowerCamelCase :     Tuple          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

from ..utils import DummyObject, requires_backends






class 		a__						( metaclass=A__ ):
					A     	=							['tensorflow_text']

					def __init__(   self				:	List[str],*_A				:	Optional[Any],**_A				:	int   ):







							"""simple docstring"""






							requires_backends(self,["tensorflow_text"]   )
