'''simple docstring'''

def 					UpperCamelCase_						( snake_case_	:	Any						,		snake_case_	:	Union[str, Any]				) ->					Tuple:






 '''simple docstring'''

 if len(str(snake_case_				)				) == 1 or len(str(snake_case_				)				) == 1:
  return a * b

 __lowerCAmelCase    							=						max(len(str(snake_case_				)				)						,		len(str(snake_case_				)				)				)
 __lowerCAmelCase    							=						ma // 2

 __lowerCAmelCase							,	__lowerCAmelCase    							=						divmod(snake_case_						,		10**ma				)
 __lowerCAmelCase							,	__lowerCAmelCase    							=						divmod(snake_case_						,		10**ma				)

 __lowerCAmelCase    							=						karatsuba(snake_case_						,		snake_case_				)
 __lowerCAmelCase    							=						karatsuba((aa + aa)						,		(ba + ba)				)
 __lowerCAmelCase    							=						karatsuba(snake_case_						,		snake_case_				)

 return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)







def 					UpperCamelCase_						( ) ->					Optional[int]:






 '''simple docstring'''

 print(karatsuba(1_54_63						,		2_34_89				)				)


if __name__ == "__main__":
    main()







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A      :				Optional[int] 				=    {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A      :				Any 				=    [
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 _A      :				Tuple 				=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






