



'''simple docstring'''
def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):



						if a < 0 or b < 0:
												raise ValueError('the value of both inputs must be positive'							)

						_UpperCamelCase :		List[Any]			    =       str(bin(A__							)							)[2:]  # remove the leading "0b"
						_UpperCamelCase :		int			    =       str(bin(A__							)							)[2:]  # remove the leading "0b"

						_UpperCamelCase :		Optional[int]			    =       max(len(A__							)  ,  len(A__							)							)

						return "0b" + "".join(
						    str(int(char_a == '1' and char_b == '1'							)							)
						    for char_a, char_b in zip(a_binary.zfill(A__							)  ,  b_binary.zfill(A__							)							)							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_			:     Any  	=  {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Dict  	=  ['MvpTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MvpForCausalLM',
					    'MvpForConditionalGeneration',
					    'MvpForQuestionAnswering',
					    'MvpForSequenceClassification',
					    'MvpModel',
					    'MvpPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

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


