



"""simple docstring"""





from typing import TYPE_CHECKING

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


__lowerCAmelCase							:       List[str]          =       {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       List[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:
					__lowerCAmelCase							:       List[str]          =       [
					    '''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:
					__lowerCAmelCase							:       Optional[Any]          =       [
					    '''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

					__lowerCAmelCase							:       List[Any]          =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 if height >= 1:
  move_tower(height - 1      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)
  move_disk(lowerCamelCase_      ,			lowerCamelCase_			)
  move_tower(height - 1      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 print("moving disk from"      ,			lowerCamelCase_      ,			"to"      ,			lowerCamelCase_			)


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	int(input("Height of hanoi: "			).strip()			)
 move_tower(lowerCamelCase_      ,			"A"      ,			"B"      ,			"C"			)


if __name__ == "__main__":
     main()





