

from typing import TYPE_CHECKING

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


A__						=							{
    "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__						=							["LayoutLMv3TokenizerFast"]

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

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

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							["LayoutLMv3FeatureExtractor"]
						A__						=							["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__						=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







from math import factorial, radians




def 					_lowercase     (				a_				:					float					,a_				:					int = 1_8					,a_				:					int = 1_0				)				->	float:




					'''simple docstring'''







					__magic_name__							=    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

					# Converting from degrees to radians
					__magic_name__							=    radians(a_				)

					__magic_name__							=    angle_in_radians
					__magic_name__							=    3
					__magic_name__							=    -1

					for _ in range(a_				):
										result += (b * (angle_in_radians**a)) / factorial(a_				)

										__magic_name__							=    -b  # One positive term and the next will be negative and so on...
										a += 2  # Increased by 2 for every term.

					return round(a_					,a_				)


if __name__ == "__main__":
						__import__("doctest").testmod()




