"""simple docstring"""







from typing import TYPE_CHECKING

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


_a					:					str=			{
    "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:
	_a					:					List[str]=			["LayoutLMv3TokenizerFast"]

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

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a					:					Union[str, Any]=			[
	    "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFLayoutLMv3ForQuestionAnswering",
	    "TFLayoutLMv3ForSequenceClassification",
	    "TFLayoutLMv3ForTokenClassification",
	    "TFLayoutLMv3Model",
	    "TFLayoutLMv3PreTrainedModel",
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a					:					List[str]=			["LayoutLMv3FeatureExtractor"]
	_a					:					Any=			["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

	_a					:					Tuple=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""simple docstring"""







def   __UpperCAmelCase    (   UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    int  )							->      int:

	'''simple docstring'''





	while a != 0:
		__snake_case		,    __snake_case      :							Union[str, Any]						=			b % a, a
	return b

def   __UpperCAmelCase    (   UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    int  )							->      int:

	'''simple docstring'''





	if gcd(UpperCAmelCase_      ,							UpperCAmelCase_  ) != 1:
		__snake_case      :							Union[str, Any]						=			F"mod inverse of {a!r} and {m!r} does not exist"
		raise ValueError(UpperCAmelCase_  )
	__snake_case		,    __snake_case		,    __snake_case      :							List[str]						=			1, 0, a
	__snake_case		,    __snake_case		,    __snake_case      :							Dict						=			0, 1, m
	while va != 0:
		__snake_case      :							List[str]						=			ua // va
		__snake_case		,    __snake_case		,    __snake_case		,    __snake_case		,    __snake_case		,    __snake_case      :							List[str]						=			(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
	return ua % m





