
"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase__   					=		{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__   					=		["""XLMRobertaTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__   					=		["""XLMRobertaTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__   					=		[
							    """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XLMRobertaForCausalLM""",
							    """XLMRobertaForMaskedLM""",
							    """XLMRobertaForMultipleChoice""",
							    """XLMRobertaForQuestionAnswering""",
							    """XLMRobertaForSequenceClassification""",
							    """XLMRobertaForTokenClassification""",
							    """XLMRobertaModel""",
							    """XLMRobertaPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__   					=		[
							    """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXLMRobertaForCausalLM""",
							    """TFXLMRobertaForMaskedLM""",
							    """TFXLMRobertaForMultipleChoice""",
							    """TFXLMRobertaForQuestionAnswering""",
							    """TFXLMRobertaForSequenceClassification""",
							    """TFXLMRobertaForTokenClassification""",
							    """TFXLMRobertaModel""",
							    """TFXLMRobertaPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__   					=		[
							    """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxXLMRobertaForMaskedLM""",
							    """FlaxXLMRobertaForCausalLM""",
							    """FlaxXLMRobertaForMultipleChoice""",
							    """FlaxXLMRobertaForQuestionAnswering""",
							    """FlaxXLMRobertaForSequenceClassification""",
							    """FlaxXLMRobertaForTokenClassification""",
							    """FlaxXLMRobertaModel""",
							    """FlaxXLMRobertaPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_xlm_roberta import (
							    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XLMRobertaConfig,
							    XLMRobertaOnnxConfig,
							)

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta import XLMRobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_roberta import (
														    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMRobertaForCausalLM,
														    XLMRobertaForMaskedLM,
														    XLMRobertaForMultipleChoice,
														    XLMRobertaForQuestionAnswering,
														    XLMRobertaForSequenceClassification,
														    XLMRobertaForTokenClassification,
														    XLMRobertaModel,
														    XLMRobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xlm_roberta import (
														    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLMRobertaForCausalLM,
														    TFXLMRobertaForMaskedLM,
														    TFXLMRobertaForMultipleChoice,
														    TFXLMRobertaForQuestionAnswering,
														    TFXLMRobertaForSequenceClassification,
														    TFXLMRobertaForTokenClassification,
														    TFXLMRobertaModel,
														    TFXLMRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xlm_roberta import (
														    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxXLMRobertaForCausalLM,
														    FlaxXLMRobertaForMaskedLM,
														    FlaxXLMRobertaForMultipleChoice,
														    FlaxXLMRobertaForQuestionAnswering,
														    FlaxXLMRobertaForSequenceClassification,
														    FlaxXLMRobertaForTokenClassification,
														    FlaxXLMRobertaModel,
														    FlaxXLMRobertaPreTrainedModel,
														)

else:
							import sys

							lowerCamelCase__   					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
'''simple docstring'''

def      UpperCamelCase_						(					snake_case_       :       int						,		snake_case_       :       int      )					->     str:






 '''simple docstring'''

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

 __lowerCAmelCase											=						str(bin(snake_case_      )      )[2:]  # remove the leading "0b"
 __lowerCAmelCase											=						str(bin(snake_case_      )      )[2:]  # remove the leading "0b"

 __lowerCAmelCase											=						max(len(snake_case_      )						,		len(snake_case_      )      )

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()






