
'''simple docstring'''


from typing import TYPE_CHECKING

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


__lowercase							:				List[str]					=       {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				int					=       [
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''SamImageProcessor''']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

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




'''simple docstring'''


def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					if not isinstance(_lowercase     ,    _lowercase			):
										raise TypeError('''recursive_lucas_number accepts only integer arguments.'''			)
					if n_th_number == 0:
										return 2
					if n_th_number == 1:
										return 1

					return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
					    n_th_number - 2			)




def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					if not isinstance(_lowercase     ,    _lowercase			):
										raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''			)
					lowerCamelCase_, lowerCamelCase_   : List[Any]       =						2, 1
					for _ in range(_lowercase			):
										lowerCamelCase_, lowerCamelCase_   : Dict       =						b, a + b
					return a


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					__lowercase							:				Any					=       int(input('''Enter the number of terms in lucas series:\n''').strip())
					print('''Using recursive function to calculate lucas series:''')
					print(''' '''.join(str(recursive_lucas_number(i)) for i in range(n)))
					print('''\nUsing dynamic function to calculate lucas series:''')
					print(''' '''.join(str(dynamic_lucas_number(i)) for i in range(n)))


