


'''simple docstring'''



class 		_snake_case					:




					def __init__(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					int        =     size
								UpperCAmelCase__  :					List[str]        =     [0] * size
								UpperCAmelCase__  :					Union[str, Any]        =     [0] * size




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								return index | (index + 1)




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								return (index & (index + 1)) - 1




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     value
								while index < self.size:
											UpperCAmelCase__  :					Any        =     self.get_prev(_lowerCamelCase) + 1
											if current_left_border == index:
														UpperCAmelCase__  :					Optional[int]        =     value
											else:
														UpperCAmelCase__  :					Dict        =     max(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					int        =     self.get_next(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								right -= 1  # Because of right is exclusive
								UpperCAmelCase__  :					str        =     0
								while left <= right:
											UpperCAmelCase__  :					Any        =     self.get_prev(_lowerCamelCase)
											if left <= current_left:
														UpperCAmelCase__  :					Tuple        =     max(_lowerCamelCase							,							self.tree[right])
														UpperCAmelCase__  :					int        =     current_left
											else:
														UpperCAmelCase__  :					Tuple        =     max(_lowerCamelCase							,							self.arr[right])
														right -= 1
								return result


if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A	={
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['BertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BertForMaskedLM',
		    'BertForMultipleChoice',
		    'BertForNextSentencePrediction',
		    'BertForPreTraining',
		    'BertForQuestionAnswering',
		    'BertForSequenceClassification',
		    'BertForTokenClassification',
		    'BertLayer',
		    'BertLMHeadModel',
		    'BertModel',
		    'BertPreTrainedModel',
		    'load_tf_weights_in_bert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBertEmbeddings',
		    'TFBertForMaskedLM',
		    'TFBertForMultipleChoice',
		    'TFBertForNextSentencePrediction',
		    'TFBertForPreTraining',
		    'TFBertForQuestionAnswering',
		    'TFBertForSequenceClassification',
		    'TFBertForTokenClassification',
		    'TFBertLMHeadModel',
		    'TFBertMainLayer',
		    'TFBertModel',
		    'TFBertPreTrainedModel',
		]
try:
		if not is_tensorflow_text_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['TFBertTokenizer']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'FlaxBertForCausalLM',
		    'FlaxBertForMaskedLM',
		    'FlaxBertForMultipleChoice',
		    'FlaxBertForNextSentencePrediction',
		    'FlaxBertForPreTraining',
		    'FlaxBertForQuestionAnswering',
		    'FlaxBertForSequenceClassification',
		    'FlaxBertForTokenClassification',
		    'FlaxBertModel',
		    'FlaxBertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
		from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_fast import BertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bert import (
				    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BertForMaskedLM,
				    BertForMultipleChoice,
				    BertForNextSentencePrediction,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    BertForTokenClassification,
				    BertLayer,
				    BertLMHeadModel,
				    BertModel,
				    BertPreTrainedModel,
				    load_tf_weights_in_bert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_bert import (
				    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBertEmbeddings,
				    TFBertForMaskedLM,
				    TFBertForMultipleChoice,
				    TFBertForNextSentencePrediction,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertForTokenClassification,
				    TFBertLMHeadModel,
				    TFBertMainLayer,
				    TFBertModel,
				    TFBertPreTrainedModel,
				)

		try:
				if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_tf import TFBertTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_bert import (
				    FlaxBertForCausalLM,
				    FlaxBertForMaskedLM,
				    FlaxBertForMultipleChoice,
				    FlaxBertForNextSentencePrediction,
				    FlaxBertForPreTraining,
				    FlaxBertForQuestionAnswering,
				    FlaxBertForSequenceClassification,
				    FlaxBertForTokenClassification,
				    FlaxBertModel,
				    FlaxBertPreTrainedModel,
				)

else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)