






'''simple docstring'''

from typing import TYPE_CHECKING

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


A:      List[Any]  				=				{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Any  				=				["RoFormerTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Any  				=				[
						    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RoFormerForCausalLM",
						    "RoFormerForMaskedLM",
						    "RoFormerForMultipleChoice",
						    "RoFormerForQuestionAnswering",
						    "RoFormerForSequenceClassification",
						    "RoFormerForTokenClassification",
						    "RoFormerLayer",
						    "RoFormerModel",
						    "RoFormerPreTrainedModel",
						    "load_tf_weights_in_roformer",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      List[Any]  				=				[
						    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRoFormerForCausalLM",
						    "TFRoFormerForMaskedLM",
						    "TFRoFormerForMultipleChoice",
						    "TFRoFormerForQuestionAnswering",
						    "TFRoFormerForSequenceClassification",
						    "TFRoFormerForTokenClassification",
						    "TFRoFormerLayer",
						    "TFRoFormerModel",
						    "TFRoFormerPreTrainedModel",
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      str  				=				[
						    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FlaxRoFormerForMaskedLM",
						    "FlaxRoFormerForMultipleChoice",
						    "FlaxRoFormerForQuestionAnswering",
						    "FlaxRoFormerForSequenceClassification",
						    "FlaxRoFormerForTokenClassification",
						    "FlaxRoFormerModel",
						    "FlaxRoFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						A:      int  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	int						,			a		:	int							)			->			int:





						"""simple docstring"""
						while second != 0:
												lowercase_      :   Any        =			first & second
												first ^= second
												lowercase_      :   List[str]        =			c << 1
						return first


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A:      Union[str, Any]  				=				int(input("Enter the first number: ").strip())
						A:      Union[str, Any]  				=				int(input("Enter the second number: ").strip())
						print(f"""{add(first, second) = }""")
