
from typing import TYPE_CHECKING

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


lowercase        =  {
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''RemBertTokenizer''']

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RemBertForCausalLM''',
		    '''RemBertForMaskedLM''',
		    '''RemBertForMultipleChoice''',
		    '''RemBertForQuestionAnswering''',
		    '''RemBertForSequenceClassification''',
		    '''RemBertForTokenClassification''',
		    '''RemBertLayer''',
		    '''RemBertModel''',
		    '''RemBertPreTrainedModel''',
		    '''load_tf_weights_in_rembert''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRemBertForCausalLM''',
		    '''TFRemBertForMaskedLM''',
		    '''TFRemBertForMultipleChoice''',
		    '''TFRemBertForQuestionAnswering''',
		    '''TFRemBertForSequenceClassification''',
		    '''TFRemBertForTokenClassification''',
		    '''TFRemBertLayer''',
		    '''TFRemBertModel''',
		    '''TFRemBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_rembert import RemBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_rembert_fast import RemBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_rembert import (
				    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RemBertForCausalLM,
				    RemBertForMaskedLM,
				    RemBertForMultipleChoice,
				    RemBertForQuestionAnswering,
				    RemBertForSequenceClassification,
				    RemBertForTokenClassification,
				    RemBertLayer,
				    RemBertModel,
				    RemBertPreTrainedModel,
				    load_tf_weights_in_rembert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_rembert import (
				    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRemBertForCausalLM,
				    TFRemBertForMaskedLM,
				    TFRemBertForMultipleChoice,
				    TFRemBertForQuestionAnswering,
				    TFRemBertForSequenceClassification,
				    TFRemBertForTokenClassification,
				    TFRemBertLayer,
				    TFRemBertModel,
				    TFRemBertPreTrainedModel,
				)


else:
		import sys

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







def      __lowerCAmelCase						(							UpperCAmelCase__				:      int = 4_0_0_0_0_0_0       )  ->    int:
							lowerCamelCase_							  =					[]
							lowerCamelCase_  ,   lowerCamelCase_							  =					0, 1
							while b <= n:
														if b % 2 == 0:
																					even_fibs.append(UpperCAmelCase__       )
														lowerCamelCase_  ,   lowerCamelCase_							  =					b, a + b
							return sum(UpperCAmelCase__       )


if __name__ == "__main__":
		print(F"""{solution() = }""")





