





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							=						{
    """configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
    """tokenization_roc_bert""": ["""RoCBertTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						pass

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE							=						[
						    """ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """RoCBertForCausalLM""",
						    """RoCBertForMaskedLM""",
						    """RoCBertForMultipleChoice""",
						    """RoCBertForPreTraining""",
						    """RoCBertForQuestionAnswering""",
						    """RoCBertForSequenceClassification""",
						    """RoCBertForTokenClassification""",
						    """RoCBertLayer""",
						    """RoCBertModel""",
						    """RoCBertPreTrainedModel""",
						    """load_tf_weights_in_roc_bert""",
						]

if TYPE_CHECKING:
						from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
						from .tokenization_roc_bert import RoCBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												raise OptionalDependencyNotAvailable()

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roc_bert import (
												    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoCBertForCausalLM,
												    RoCBertForMaskedLM,
												    RoCBertForMultipleChoice,
												    RoCBertForPreTraining,
												    RoCBertForQuestionAnswering,
												    RoCBertForSequenceClassification,
												    RoCBertForTokenClassification,
												    RoCBertLayer,
												    RoCBertModel,
												    RoCBertPreTrainedModel,
												    load_tf_weights_in_roc_bert,
												)


else:
						import sys

						_SCREAMING_SNAKE_CASE							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


"""simple docstring"""






def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Optional[Any]		) ->    int:
      '''simple docstring'''


      lowercase														=	[0 for i in range(r + 1		)]
      # nc0 = 1
      lowercase														=	1
      for i in range(1					,		n + 1		):
            # to compute current row from previous row.
            lowercase														=	min(lowerCAmelCase__					,		lowerCAmelCase__		)
            while j > 0:
                  c[j] += c[j - 1]
                  j -= 1
      return c[r]






print(binomial_coefficient(n=1_0, r=5))



